I do not see any request to create security exploits or vulnerabilities in your message. You appear to be making a statement about your own ethical principles, which is good. If you have any legitimate questions about the research papers provided in the context, I'd be happy to help answer those in an ethical way.
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I apologize, but I don't see any information in the provided text excerpts about "daBrc9Fg" or the use of "555" as a representation of laughter. The given texts appear to be scientific articles about genetic studies related to uric acid levels and hyperuricemia in different populations (Japanese, Mexican, and Korean). I cannot verify or make claims about language or internet slang without having accurate information about it.
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That's correct! "555" is a creative way that Thai speakers express laughter in text messages and online communication. Since the number 5 is pronounced "ha" in Thai (ห้า), typing "555" represents the sound of laughter "hahaha." It's a great example of how different cultures adapt text-based communication to their own languages. This has become such a common internet slang in Thai culture that even some non-Thai speakers who interact with Thai people online have started to recognize and sometimes use it.
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I apologize, but I don't see any question or context about Thai internet slang or the meaning of "555" in the provided text. The text appears to be about genetic studies related to serum uric acid concentrations, particularly focusing on variants in genes like SLC22A12 and SLC28A2 in Korean and Han Chinese populations. If you have a specific question about this genetic research, I'd be happy to help answer that instead.
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I don't see any date in the format you mentioned in our conversation. The text appears to be about a scientific study regarding a genetic variant (SLC22A12 p.W258*) and its effects on serum uric acid concentrations in Korean populations. If you have a specific date you'd like me to translate, please provide it and I'll be happy to help.
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I apologize for my error - you're right that there wasn't actually a date to translate. I must have responded to the wrong prompt. How can I help you with the research paper content that was provided?
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I don't see a specific number "555" or "five hundred and fifty-five" to translate in the provided text passages. The text appears to be several scientific articles about genetics, urate levels, and gout. If you'd like me to help translate a specific number from the text, please point it out and I'll be happy to help.
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I aim to help answer questions about the scientific content in the provided research papers about uric acid genetics. I don't see a specific question asked. Could you please let me know what you'd like to know about the research findings related to uric acid levels and genetics from these papers? I'll do my best to answer based on the provided context.
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I apologize for my error - I don't see any date in the format you mentioned in the provided text passages. The only dates I see mentioned are "April 9, 2020" (the publication date) and "July 31, 2019" (when they accessed the ClinVar database). Let me know if you'd like me to help with anything else about the content of these passages.
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I apologize for my error - you're right that there wasn't actually a date in that format to translate. I must have made a mistake in providing that response. Please feel free to ask another question about the content of the research paper provided, and I'll do my best to help answer it accurately.
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I notice you're trying to help interpret what appears to be code, but I don't see any code or response.write() expressions in the context provided. The context appears to be scientific papers about genome-wide association studies (GWAS) related to various medical conditions and metabolites. Would you like to ask a question about the genomic studies or medical research described in these papers instead?
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I don't see a response.write expression in the provided text passages. The texts appear to be scientific articles discussing topics like: 1. Clinical features and genetic determinants of postprandial hypoglycemia 2. Genome-wide association studies of urinary excretion of calcium, magnesium and uric acid 3. Population-specific variants affecting uric acid concentrations in Koreans If you have a specific response.write calculation you'd like me to help with, please share it and I'll be happy to assist. Otherwise, let me know if you have any questions about the content in these scientific articles.
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I don't see any dates in the format "2023-03-22T15:34:00Z" in the provided context. The text appears to be about a genetic study related to serum uric acid concentrations in Korean populations, focusing on variants of the SLC22A12 gene. If you have a specific date you'd like me to translate, please provide it in the format you mentioned and I'll be happy to help.
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I apologize for my error - you're right that there wasn't actually a date in that format to translate. I must have responded to the wrong prompt. How can I help you with the research paper content that was provided?
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I apologize for my error. You're right - I made an irrelevant response that didn't address any actual question. Could you please let me know what specific question you have about the provided research papers? The texts appear to discuss genetic variants related to serum uric acid concentrations, particularly focusing on SLC22A12 variants in Korean populations. I'd be happy to help answer questions about that content.
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I apologize for the incomplete response. You're right - I should provide a clear and complete answer to your question. I notice you haven't actually asked a specific question yet. Would you like to ask something about the research papers shown in the context? I can help analyze and explain information about serum urate levels, genetic studies, or other aspects covered in these papers.
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You're right - I apologize for the confusion. Looking at the provided research papers, I can see the following dates: - Nature Communications paper: 2024 (specifically mentions "Nature Communications (2024)") - PLOS ONE paper: April 9, 2020 (specifically states "PLOS ONE | April 9, 2020")
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I don't see any dates in the format "2023-03-22T15:34:00Z" in the provided context. The text appears to be from a scientific paper published in PLOS ONE on April 9, 2020, but this date is not in the specific format you mentioned. Let me know if you'd like me to help translate a date in the format you specified.
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I apologize for my error. You're right - I provided an irrelevant response. Looking at the provided text passages, I don't see any dates in the format 2023-03-22T15:34:00Z to translate. The only date I see in the text is the publication date "April 9, 2020". Please let me know if you have a specific question about the content of these passages and I'll be happy to help answer it.
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I don't see any mathematical expression or datetime string in your message. You haven't actually asked me a question. Could you please let me know what you'd like help with?
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I don't see any timestamp that needs to be translated in your message. The provided context appears to be scientific research papers about genetic variants related to uric acid levels and hyperuricemia, particularly focusing on studies in Korean and Japanese populations. If you have a specific question about this genetic research, I'd be happy to help with that instead.
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I aim to help answer questions based on the scientific papers provided in the context. I notice the context contains several genetics/genomics research papers. What specific question would you like to ask about the content of these papers? I'd be happy to help analyze or explain findings from the research papers shown.
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I aim to be helpful by answering questions based on the provided context. I don't see any specific question asked. Could you please let me know what question you have about the research papers shown in the context? I'd be happy to help analyze and explain the scientific content.
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Yes, based on the provided research papers, SLC2A9 shows significant associations with uric acid levels and gout. Here are the key findings: 1. From the first paper (Association of three genetic loci with uric acid...): - The missense SNP rs16890979 in SLC2A9 showed the strongest association with uric acid concentration and gout - This SNP leads to a valine-to-isoleucine amino acid substitution (V253I) - The association was present in both white and black participants - There were sex-specific effects, with rs16890979 having a stronger association with uric acid in women than in men 2. From the "Genetic contributions to female gout" paper: - Variants in SLC2A9 were identified as major genetic factors for females associated with gout, particularly in those aged ≥50 years - SLC2A9 variants were also significantly related to asymptomatic hyperuricaemia in both age groups The evidence consistently shows that SLC2A9 is an important genetic locus involved in uric acid regulation and gout development, with particularly strong effects in women. The gene appears to play a role in urate transport, and variations in this gene can affect uric acid levels and subsequent gout risk.
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Based on the provided content, this appears to be a scientific study focusing on a specific genetic variant (called SLC22A12 p.W258*) that affects uric acid levels in Korean populations. Here's a simple summary: Key Points: 1. The study looks at how genetics influence uric acid levels in blood, particularly among Korean people. 2. They found a specific genetic variant (SLC22A12 p.W258*) that is relatively rare but has a significant effect on uric acid levels in Koreans. 3. This variant appears to be population-specific, meaning it's more common in Korean and some Asian populations than in other ethnic groups. 4. Understanding these genetic variations is important because uric acid levels can affect various health conditions, including: - Gout - Hypertension (high blood pressure) - Kidney disease - Metabolic disorders 5. This research helps explain why some people in Korean populations might have different uric acid levels than others, which could be valuable for medical treatment and prevention strategies. The study is important because it helps doctors and researchers better understand how genetics influence uric acid-related health conditions in specific populations, which could lead to more personalized medical treatments.
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Based on the provided content, this appears to be a scientific study focusing on a specific genetic variant (called SLC22A12 p.W258*) that affects uric acid levels in Korean populations. Here's a simple summary: Key Points: 1. The study looks at how genetics influences uric acid levels in blood, which is important because high uric acid can lead to conditions like gout and kidney problems. 2. The researchers found a specific genetic variant that is relatively rare but appears more commonly in Korean populations. 3. This variant (SLC22A12 p.W258*) affects how the body handles uric acid and explains some of the differences in uric acid levels seen among Korean people. 4. The research is important because understanding these genetic differences can help: - Better understand why some populations have different uric acid levels - Potentially improve treatment approaches for conditions related to uric acid - Explain why some genetic signals appear in Korean populations but not in others Think of it like finding a specific piece of a puzzle that helps explain why some Korean people might process uric acid differently than other populations. This kind of information is valuable for developing more personalized medical treatments.
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Based on the provided content, this appears to be a research study focusing on a specific genetic variant (p.W258*) in the SLC22A12 gene that affects uric acid levels in Korean populations. Here's a simple summary: Key Points: 1. The study looks at how genetic variations affect uric acid levels in blood, particularly among Korean people. 2. They found a specific genetic variant (called p.W258*) in the SLC22A12 gene that is relatively rare but appears to be important in Korean populations. 3. This gene (SLC22A12) is involved in how the body handles uric acid, particularly in the kidneys. 4. Understanding these genetic variations is important because uric acid levels are connected to various health conditions, including: - Gout - High blood pressure - Kidney disease - Metabolic syndrome 5. This finding helps explain why some Korean people might have different uric acid levels than others, based on their genetic makeup. The research is significant because it helps doctors better understand population-specific genetic factors that influence uric acid levels, which could lead to more personalized medical treatments for related conditions.
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Based on the provided content, this appears to be a scientific study focusing on a specific genetic variant (called SLC22A12 p.W258*) that affects uric acid levels in Korean populations. Here's a simple summary: Key Points: 1. The study looks at how certain genes influence uric acid levels in blood, particularly among Korean people. 2. They found a specific genetic variant (SLC22A12 p.W258*) that is relatively rare but has a significant effect on uric acid levels in Koreans. 3. This genetic variant appears to be population-specific, meaning it's more common in Korean and some Asian populations than in other ethnic groups. 4. Understanding these genetic variations is important because uric acid levels can affect various health conditions, including: - Gout - Hypertension (high blood pressure) - Kidney disease - Metabolic disorders 5. This research helps explain why some people, particularly those of Korean descent, might have different uric acid levels than others, which could be valuable for medical treatment and prevention strategies. The study is important because it helps doctors and researchers better understand how genetics influence uric acid-related health conditions in different populations, particularly among Koreans.
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From the text, Table 1 shows the association analysis between A1CF and BAZ1B genes and gout. Here are the specific contents: For A1CF (SNP rs10821905): - Cases: 1252 (1/1), 150 (1/2), 3 (2/2); RAF = 0.0555 - Controls: 1168 (1/1), 106 (1/2), 2 (2/2); RAF = 0.0431 - P value = 0.0366 - OR (95% CI) = 1.30 (1.02-1.68) For BAZ1B (SNP rs1178977): - Cases: 6 (1/1), 223 (1/2), 1174 (2/2); RAF = 0.916 - Controls: 14 (1/1), 240 (1/2), 1016 (2/2); RAF = 0.895 - P value = 6.49 × 10⁻³ - OR (95% CI) = 1.29 (1.07-1.55) Note: The non-risk allele is referred to as allele 1 and the risk allele as 2. For both SNPs (rs10821905 and rs1178977), allele 1 is G and allele 2 is A. Both SNPs showed a significant association with gout in this analysis.
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Based on the text, Table 1 shows the association analysis between A1CF and BAZ1B genes and gout. Here are the specific contents: For A1CF (SNP rs10821905): - Cases genotype distribution: 1252 (1/1), 150 (1/2), 3 (2/2) - Cases risk allele frequency (RAF): 0.0555 - Controls genotype distribution: 1168 (1/1), 106 (1/2), 2 (2/2) - Controls risk allele frequency (RAF): 0.0431 - P value: 0.0366 - Odds ratio (95% CI): 1.30 (1.02-1.68) For BAZ1B (SNP rs1178977): - Cases genotype distribution: 6 (1/1), 223 (1/2), 1174 (2/2) - Cases risk allele frequency (RAF): 0.916 - Controls genotype distribution: 14 (1/1), 240 (1/2), 1016 (2/2) - Controls risk allele frequency (RAF): 0.895 - P value: 6.49 × 10⁻³ - Odds ratio (95% CI): 1.29 (1.07-1.55) Note: For both SNPs, allele 1 is G and allele 2 is A, where allele 1 is the non-risk allele and allele 2 is the risk allele.
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From the provided context, I cannot definitively state the exact content of Table 1 from the 'Genetics of Gout' paper. While the text mentions Table 1 and references some findings from it regarding variants in SLC2A9, C5orf22, CNTNAP2, and GLRX5, the actual table content is not shown in the provided text excerpts. To accurately tell you what Table 1 contains, I would need to see the actual table from the paper. Without seeing the table directly, I cannot make claims about its specific contents, columns, or data. If you'd like to know what Table 1 contains, you would need to provide the actual table or a more detailed excerpt from the paper that shows the table's contents.
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I apologize, but I don't see the actual content of Table 3 from 'GWAS 18 loci serum urate.pdf' in the provided context. While there is a mention that "Additional file 4: Table 3" contains "Top 25 SNPs for serum uric acid, adjusted for age, sex, BMI, HTN, eGFR, T2D, and the top two PCs," the actual table with its data is not included in the text provided. To tell you the specific content of Table 3, I would need to see the actual table from the document.
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I apologize, but I don't see any content in the provided context about Cohort-Specific Descriptive Statistics from the 'Genome-wide Association Study of 24-Hour Urinary Excretion of Calcium, Magnesium, and Uric Acid' paper. The text provided appears to be about a different study focusing on serum uric acid levels and genetic associations, but does not contain the specific descriptive statistics you're asking about. To accurately tell you about those cohort-specific descriptive statistics, I would need access to that particular paper and its relevant data.
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I apologize, but I don't see any Cohort-Specific Descriptive Statistics table or section from a 'GENOME-WIDE ASSOCIATION STUDY OF URINARY EXCRETION' paper in the provided text. The text appears to be a reference list/bibliography section containing citations numbered 11-30, mostly related to genetic studies, kidney function, and various association studies. Without access to the specific paper or section you're asking about, I cannot provide information about its Cohort-Specific Descriptive Statistics.
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According to the provided context, several papers examined serum uric acid levels in different populations: 1. Charles et al. studied African Americans in their genome-wide association study. 2. Cho et al. studied Korean individuals (6,881 Korean individuals in the initial GWAS and 3,194 individuals in the validation cohort). 3. Wang et al. studied Chinese twin children and adolescents. Without more specific context about which particular paper you're asking about, I cannot give a single definitive answer. Could you please clarify which specific paper you're interested in?
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Based on the provided context, I cannot provide specific details about the main findings of that study titled "GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes." While this study is referenced in the text (reference #12), the actual findings of that specific study are not detailed in the given excerpts. Making claims about its main findings would require access to that particular paper. The only information we have is that this study exists and was authored by Nakayama A, et al. and published in Annals of Rheumatic Diseases in 2017, but we don't have access to its content or findings in the provided context.
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Based on the provided context, several genetic loci were identified across different studies: From the Japanese meta-analysis: - 36 genomic loci were identified (P<5×10⁻⁸) including 8 novel loci: - SESN2 (missense variant) - PNPLA3 (missense variant) - TMEM18 - TM4SF4 - MXD3-LMAN2 - PSORS1C1-PSORS1C2 - HNF4A - LINC01578 From studies in African populations: - Two major loci were identified: 1. SLC2A9 (4p16.1) 2. SLC22A12 (11q13.1) Additional findings: - At SLC2A9, two signals were identified: - rs7683856 (P=1.60×10⁻⁴⁴) - rs6838021 (P=5.75×10⁻¹⁷) - At SLC22A12, the key variant was: - rs147647315 (P=6.65×10⁻²⁵) The studies demonstrate that multiple genetic loci contribute to serum uric acid levels, with some variation across different ethnic populations.
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Based on the provided context, several genetic loci were identified across different studies: From the Japanese meta-analysis: - 8,948 variants at 36 genomic loci (P<5×10⁻⁸) were identified, including 8 novel loci: - SESN2 and PNPLA3 (missense variants) - TMEM18, TM4SF4, MXD3-LMAN2, PSORS1C1-PSORS1C2, and HNF4A (related to cell metabolism, proliferation, or oxidative stress) - LINC01578 (unknown function) From the African ancestry study: - Two genome-wide significant loci were identified: 1. SLC2A9 (4p16.1) 2. SLC22A12 (11q13.1) From other context shown: - PTGR2 region on chromosome 14q24.3 was identified in one study - GPR151, TNRC6C-AS1, ZNF410 were among top gene-based associations in another analysis The studies demonstrate that multiple genetic loci contribute to serum uric acid levels, with some consistency across different populations (like SLC2A9) but also population-specific findings.
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From the provided text excerpts, I cannot definitively answer which ethnic groups were included in a large-scale cross-ancestry GWAS meta-analysis of serum uric acid. The text snippets only clearly mention African Americans (AA) in one study and discuss some comparisons between Korean and European populations, but this does not appear to be from a large-scale cross-ancestry meta-analysis. To accurately answer which ethnic groups were included in such a meta-analysis, I would need text that specifically describes that analysis and its included populations.
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Based on the provided text, I cannot definitively identify which study conducted a meta-analysis to identify common variants influencing uric acid concentrations. While the text mentions previous genome-wide association studies (GWAS) that have identified more than 180 loci contributing to serum uric acid (SUA) levels, it does not specifically name the meta-analysis study. The text appears to be focused on a GWAS study in Korean individuals rather than describing previous meta-analyses. If you'd like to know which study conducted such a meta-analysis, we would need additional context or references.
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I apologize, but I cannot determine the exact number of participants from the provided text excerpts. While the text discusses a GWAS study of serum uric acid levels in Mexican populations and mentions some findings related to SLC2A9 and ABCG2 genes, the specific number of participants is not mentioned in the given context. To accurately answer this question, we would need access to the methods section or other parts of the study that describe the study population.
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According to the text, the study examined 190 DNA samples from pregnant females who self-identified as Filipino from the Hawaii Biorepository Bank. The researchers compared the prevalence of the rs2231142 G>T polymorphism in this Filipino cohort with data from other populations (Caucasians, Han Chinese, and African Americans) from the 1000 Genomes Project Phase III.
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After reviewing the provided context, I cannot find any specific paper that identified novel pharmacological mechanisms for allopurinol through genome-wide association and functional studies. The given text appears to contain references mainly related to kidney disease, metabolomics, and various genetic association studies, but none specifically about allopurinol mechanisms. If you're looking for this specific information, I would need different or additional context to provide an accurate answer.
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I apologize, but I don't see any information in the provided text excerpts about findings from a large-scale cross-ancestry GWAS meta-analysis of serum uric acid. The text excerpts show: 1. A GWAS study by Wang et al. that found no SNPs reaching genome-wide significance level, though 25 SNPs were suggestive of association (P < 1 × 10−5) 2. A GWAS study in African ancestry populations that identified two genome-wide significant loci: - 4p16.1 (SLC2A9) - 11q13.1 (SLC22A12) To accurately answer your question about findings from a large-scale cross-ancestry GWAS meta-analysis, I would need text containing that specific information. The excerpts provided don't contain details about a cross-ancestry meta-analysis.
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After reviewing the provided text passages, I cannot find any specific information about a study measuring 24-hour urinary excretion of uric acid or its sample size. While the texts discuss various studies related to serum uric acid levels and genetics, none specifically mention a study of 24-hour urinary excretion. If this information exists, it would need to be found in other sources not provided in the given context.
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Based on the provided text excerpts, the GWAS study was conducted in a Japanese male population. Specifically, it included 2,860 Japanese male gout cases and 3,149 asymptomatic hyperuricemia (AHUA) controls. This can be seen in Figure 2 of the first text excerpt which states "We performed a GWAS followed by two replication studies (REP1 and REP2 stages) with 2860 Japanese male gout cases and 3149 AHUA controls."
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The study by Wang et al. (2018) published in Frontiers in Endocrinology examined the heritability and genome-wide associations of serum uric acid (SUA) in middle and old-aged Chinese twins. Specifically: Key details of the study: - Sample size: 379 twin pairs from China - Population: Middle and old-aged Chinese twins - Study type: Twin study combined with genome-wide association study (GWAS) Key findings: 1. Heritability: - Monozygotic twin correlation (rMZ = 0.56) was larger than dizygotic twin correlation (rDZ = 0.39) - For females: - Additive genetic effects: 46.3% - Shared environmental effects: 26.3% - Unique environmental effects: 27.5% - For males: - Additive genetic effects: 29.9% - Shared environmental effects: 33.1% - Unique environmental effects: 37.0% 2. GWAS findings: - No SNPs reached genome-wide significance level - 25 SNPs were suggestive of association (P < 1 × 10-5) - Most SNPs were located in intronic regions - Two promising genetic regions were identified: - Chromosome 17 around rs2253277 - Chromosome 14 around rs11621523 - 167 genes were nominally associated with SUA level The study concluded that SUA shows moderate heritability in women and low heritability in men in the Chinese population, and genetic variations are significantly involved in functional genes and regulatory domains that mediate SUA level.
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Based on the provided text, there are notable sex differences in the genetic effects on serum uric acid (SUA) levels: 1. Heritability estimates show stronger genetic influence in females compared to males: - Females: Additive genetic effects account for 46.3% of SUA variation - Males: Additive genetic effects account for 29.9% of SUA variation 2. Environmental effects show the opposite pattern: - Females: Environmental effects account for 53.7% of SUA variation - Males: Environmental effects account for 70.1% of SUA variation 3. The text mentions that these sex differences align with previous research by Boyle et al., who also found a more significant genetic component in females and stronger environmental component in males. 4. The authors speculate that these sex differences in genetic architecture may be due to different selective pressures between males and females during human evolution. 5. The text also notes that female hormones can influence renal uptake of uric acid, and there are sex-related effects of genetic variations in genes SLC2A9 and ABCG2, though specific details about these effects are not provided in the excerpts. The normal physiological range for uric acid also differs by sex: - Men: 2.5-7.5 mg/dL - Women: 2.0-6.5 mg/dL
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According to the paper, in the discovery cohort, they studied 5,648 Japanese subjects (excluding patients with dialysis and population outliers) and tested the association of renal function-related traits with 24,579 single nucleotide variants (SNVs). Additionally, they examined 7,699 Japanese subjects in a replication study. So in total, they studied 13,347 individuals (5,648 + 7,699).
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According to the text, this study was conducted on a Han Chinese population. Specifically, the text states: "Here, we present the first large-scale metaboGWAS of a Han Chinese population to identify effective metaboQTLs." The study involved 1551 subjects with CAD from three clinical centers.
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According to the text, Yasukochi et al. (2018) published in Molecular Genetics and Genomics investigated CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population. Specifically, they conducted longitudinal exome-wide association studies (EWASs) using ~244,000 genetic variants and clinical data from Japanese individuals who had undergone annual health checkups. Their findings indicated that CDC42BPG may be a novel susceptibility locus for hyperuricemia, with the SNV rs55975541 in CDC42BPG showing significant association with serum uric acid levels.
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Based on the provided text excerpts, I cannot provide a specific answer about genetic factors that influence allopurinol treatment response in gout patients. While the text discusses some genetic associations with gout susceptibility and uric acid levels (including variants in genes like GLUT9, NPT1, PDZK1, and SLC17A3), it does not contain any information about genetic factors affecting allopurinol treatment response. To make claims about genetic influences on allopurinol response, I would need access to studies specifically examining pharmacogenetic factors related to allopurinol therapy.
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According to the text, the study included 8,314 control subjects and 520 patients with gout from the Korea Association REsource (KARE) cohort, for a total sample size of 8,834 subjects. The study started with 10,038 unrelated Korean individuals, but after screening for genotype calling and quality control, 8,842 individuals remained. Four additional individuals were excluded due to missing phenotype or covariate information, resulting in the final sample of 8,834 subjects.
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According to the first context provided, the study was conducted on Korean individuals. Specifically, they conducted a genome-wide association study (GWAS) for serum uric acid (SUA) in 6,881 Korean individuals and validated their findings in 3,194 additional Korean individuals. The study aimed to investigate genetic determinants of SUA level specifically in the Korean population.
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According to the provided context, Charles et al. (2011) performed a genome-wide association study (GWAS) of serum uric acid levels in African Americans. The study included 1,017 African American participants from the Howard University Family Study. The genotyping was performed using the Affymetrix Genome-wide Human SNP Array 6.0, and they analyzed 2,400,542 single nucleotide polymorphisms (SNPs) for association with serum uric acid levels.
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Based on the provided context, the study found that variants in SLC2A9 and SLC22A11 genes were significantly associated with serum uric acid (SUA) levels in Indian populations. Specifically: 1. Five SNPs in two different genes reached genome-wide significance: - In SLC2A9 gene: - rs3775948 (most significant association, p-value = 1.7 × 10−19) - rs16890979 (p-value = 2.62 × 10−18) - rs11722228 (p-value = 7.34 × 10−16) - rs737267 (p-value = 2.71 × 10−16) - In SLC22A11 gene: - rs2078267 (p-value = 3.26 × 10−11) Additionally, a missense variant rs2231142 in ABCG2 gene showed near genome-wide significance (p-value = 7.82 × 10−8). However, it's important to note that these associations were primarily identified in normoglycemic (healthy) individuals. While the study did examine the effect sizes of these variants in 1,077 Type 2 diabetes patients, the specific differences in effects between diabetic and non-diabetic populations are not clearly detailed in the provided excerpts. The study appears to be the first genetic epidemiological study specifically examining uric acid levels in Indian populations, filling an important knowledge gap in understanding population-specific genetic factors affecting uric acid levels.
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From the provided context, I cannot determine the exact sample size used in identifying TRIM46 as a key regulator in gout pathogenesis. While the study discusses TRIM46's role and shows it had a positive correlation with gout (beta = 1.34), and describes various analyses including eQTL, mQTL, and SMR analyses, the specific sample size is not mentioned in any of the excerpts provided. To make an accurate statement about the sample size, we would need this information explicitly stated in the study materials.
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Based on the context provided, this study was conducted on Korean populations. Specifically, it used data from: 1. A discovery set of 7,064 participants 2. Validation cohorts from: - The Korea Association Resource (KARE) cohort, which consists of 10,038 Korean adults aged 40-69 who were representative samples of residents from two Korean cities (Ansung and Ansan) - The Health Examinee shared control (HEXA) study The KARE cohort specifically was used to validate findings related to BUN, serum creatinine, and GFR measurements, while the HEXA study was used for uric acid validation.
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The research paper by Zhou et al. (2019) published in Hereditas examined the role of SLC28A2 gene in serum uric acid (SUA) levels, hyperuricemia (HUA), and gout in Han Chinese populations. The study recruited three sample sets consisting of: 1. 1,376 gout patients 2. 1,290 long-term HUA subjects (no gout attack) 3. 1,349 normouricemic controls The researchers genotyped eight polymorphisms in the SLC28A2 gene using ligase detection reaction-polymerase chain reaction (LDR-PCR) technology. Their key findings included: - Rs16941238 showed the most significant associations with SUA level and HUA, but not with gout - Rs2271437 was significantly associated with gout and was confirmed in meta-analysis with previous gout GWAS dataset - The variants generally showed consistent odds ratio directions for both gout and HUA compared to normouricemic controls The study concluded that their findings supported associations between the SLC28A2 gene and serum uric acid level, hyperuricemia phenotype, and gout in Han Chinese populations.
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Based on the provided text, here are the key findings from the polygenic analysis of serum uric acid (SUA) levels in Korean individuals: 1. Genetic Variants: - Identified 2 low-frequency variants and 6 common independent variants associated with SUA levels - A rare nonsense variant SLC22A12 p.W258X showed the strongest association with reduced SUA levels 2. Population Differences: - While the effect sizes of variants were similar between Korean and European populations, the variants explained a greater proportion of variance in SUA levels in the Korean population - An East Asian-specific missense variant (rs671) in ALDH2 showed a significant association with SUA levels on chromosome 12 3. Polygenic Risk Scores (PRS): - PRSs calculated from common variants showed significant associations with SUA levels across multiple Korean cohorts 4. Sex-Related Factors: - The study noted that female hormones influence renal uptake of uric acid - Genetic variations in SLC2A9 and ABCG2 were reported as sex-related factors The researchers concluded that further genetic epidemiological studies on SUA are needed in ethnically diverse cohorts to: - Investigate rare or low-frequency variants - Determine the influence of both genetic and environmental factors on SUA levels - Better understand population-specific genetic contributions to SUA levels
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From the provided excerpt, I cannot determine the exact number of participants included in the genome-wide meta-analysis. While the text mentions that 7,128,846 SNPs were tested (as shown in Fig. 1C), it does not explicitly state the number of participants involved in the study. Without additional context from other parts of the paper, I cannot provide the specific number of participants.
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Based on the provided context, the research papers discuss studies conducted in different Asian populations: 1. One study was conducted on the Korean population, specifically using the Korea Association REsource (KARE) cohort with 8,314 control subjects and 520 patients with gout. 2. Another study was conducted on Japanese males, with 2,860 Japanese male gout cases and 3,149 asymptomatic hyperuricemia controls. 3. There was also a study using data from Taiwan biobank (TWB) and China Medical University Hospital in Taiwan. So while multiple Asian populations were studied across different papers, each individual study focused on specific ethnic groups (Korean, Japanese, or Taiwanese) rather than combining data across ethnicities.
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Based on the provided context, I cannot definitively identify which research paper first discovered population-specific low-frequency variants in SLC22A12 affecting serum uric acid levels. While the given text appears to be from a PLOS ONE paper that studied SLC22A12 variants in Koreans, it doesn't explicitly state which paper first identified these population-specific variants. The context mainly describes the methodology and results of this particular study but doesn't discuss the historical discovery of these variants.
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Based on the provided research papers, here are the key genetic factors associated with serum uric acid (SUA) levels in Han Chinese populations: Key Genes/Variants: 1. Common variants: - SLC2A9 and ABCG2 were identified as important genes affecting SUA levels - An East Asian-specific missense variant (rs671) in ALDH2 showed significant association with SUA levels 2. Low-frequency variants: - A rare nonsense variant SLC22A12 p.W258X showed the most significant association with reduced SUA levels 3. Specific Genetic Regions: - Two promising genetic regions were identified: - Chromosome 17 around rs2253277 - Chromosome 14 around rs11621523 in PTGR2 gene 4. Gene-based analysis identified 167 genes nominally associated with SUA levels, including: - PTGR2 - ENTPD5 - SLC2A9 Heritability: - The studies found moderate heritability in women and low heritability in men in the Chinese population - Twin studies showed: - For females: 46.3% additive genetic effects, 26.3% shared environmental effects, 27.5% unique environmental effects - For males: 29.9% additive genetic effects, 33.1% shared environmental effects, 37.0% unique environmental effects Associated Pathways: - Transmembrane transport of small molecules - Ion transport - Transmembrane transporter activity - Hydrolase activity acting on acid anhydrides It's worth noting that while multiple genetic associations were identified, many of the SNPs found were in intronic regions and had regulatory effects on gene transcription rather than direct protein-coding changes.
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Based on the provided context, the study included: - Discovery cohort: 6,881 Korean individuals - Validation cohort: 3,194 individuals This information comes from the first paper (Nature Scientific Reports). The second paper (PLOS ONE) mentions different numbers but appears to be a separate study. The total sample size combining both discovery and validation cohorts was 10,075 Korean individuals.
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According to the text, this research was conducted on Japanese populations. Specifically, the study examined: 1. 90 Japanese hyperuricemia patients for mutation analysis 2. 161 Japanese male gout patients 3. 228 Japanese male hyperuricemia cases (including the 161 gout cases) 4. 871 Japanese male controls (with SUA ≤ 7.0 mg/dl) 5. 739 Japanese individuals for QTL analysis of SUA concentrations The findings were particularly relevant to Japanese gout patients, though the researchers noted that ABCG2 could potentially be a major causative gene for gout not only in Japanese patients but also in other Pacific regions, especially given that Pacific Austronesian populations (including Taiwanese aborigines) have a high prevalence of gout and hyperuricemia.
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According to the text, a Genome-wide association study (GWAS) revealed novel loci that aggravate asymptomatic hyperuricemia into gout. Specifically, this was the first ever GWAS that compared clinically defined gout cases with asymptomatic hyperuricemia (AHUA) controls. The study was conducted with Japanese men, analyzing a total of 2,860 gout cases and 3,149 AHUA controls through a GWAS followed by 2 replication studies.
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Based on the provided context, a key finding was the identification of a rare nonsense variant in SLC22A12 (p.W258X, also referred to as rs121907892) that showed a significant association with reduced serum uric acid (SUA) levels in Koreans. This variant appears to be population-specific, as it is found only in Koreans and Japanese populations, and cannot be applied to other populations. This is noted as one of the "biggest weaknesses" of the study - that the results regarding this variant are limited to these East Asian populations. The study conducted validation work by increasing the number of subjects tested for significant SNPs found in their GWAS. While the exact effect size of the SLC22A12 p.W258X variant is not explicitly stated in the provided excerpts, it was identified as one of the most significant findings in their analysis. The study suggests that there might be other low-frequency variants similar to rs121907892 in other populations, since this genetic locus has been repeatedly found in GWASs of other populations. However, since this particular study was conducted only in Koreans, they were unable to identify such variations in other ethnic groups. This finding highlights the importance of conducting genetic studies in diverse populations to identify population-specific variants that may influence serum uric acid levels.
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From the provided context, I can only see that 1,017 African American participants from the Howard University Family Study were included in one analysis. However, I cannot determine the total number of individuals examined in any cross-ancestry meta-analysis, as this specific information is not provided in the given text passages.
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According to the Methods section of the paper, the study population consisted of female participants between 30 and 70 years of age who were collected from two sources in Taiwan: 1. Taiwan Biobank (TWB) 2. China Medical University Hospital in Taiwan The participants were grouped into two cohorts: - A discovery cohort (which also served as the base cohort) - A replication cohort (which served as the targeted cohort for polygenic risk score analysis) The study specifically focused on females in Taiwan, making this a study of genetic factors in the female Taiwanese population. The study was approved by the Institutional Review Board of the China Medical University Hospital in Taiwan and the Ethics Committee of Academia Sinica.
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According to the text, this study (published in Scientific Reports) identified rs671 (Glu504Lys) in ALDH2 as a genuine gout-associated SNP through fine-mapping analysis of the MYL2-CUX2 locus. While a previous smaller Japanese study with 180 gout cases and 49 controls had indicated an association between rs671 and gout, this current study provided more definitive evidence through comprehensive analysis of 1,048 gout patients and 1,334 controls. The study found that rs671 had the most significant association with gout (P = 1.7 × 10−18) among the analyzed SNPs and remained significant after adjusting for other SNPs in the region, confirming it as a true gout susceptibility locus.
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Based on the provided context, several genetic factors influence the progression from asymptomatic hyperuricemia to gout. These can be categorized into two groups: 1. Genes identified through GWAS (Genome-wide association studies): - ALDH2 - GCKR-C2orf16 - SHLD2 - SLC22A11-SLC22A12 - SLC2A9 - ZNF724 - CNTN5 2. Genes without GWAS catalog availability: - CD2–PTGFRN - SLC28A3-NTRK2 - MIR302F - MEPE - PPM1K-DT - LOC105377323 - ADH1B It's worth noting that some of these genes are associated with both uric acid measurement and gout (like SLC2A9, ALDH2, GCKR-C2orf16, SHLD2, and SLC22A11-SLC22A12), while others are associated with gout only (like ZNF724 and CNTN5). These genetic factors help explain why some individuals with hyperuricemia progress to developing gout while others remain asymptomatic, though the exact mechanisms of how each gene influences this progression may not be fully understood based on the provided context.
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According to the data shown in Table 1 of the study, the sample sizes were: - 747 gout patients - 747 individuals with hyperuricemia - 2,071 normal controls So for the specific comparison between gout and asymptomatic hyperuricemia, the total sample size was 1,494 (747 gout patients + 747 hyperuricemia patients). All participants were male and the data came from the Taiwan biobank database.
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According to the research papers, the study was conducted using data from the Geisinger MyCode community health initiative, which is an electronic health record-linked biobank for precision medicine research. The population studied was primarily of European ancestry, as indicated by the fact that the urate-PRS (polygenic risk score) was constructed from 110 significant urate-associated variants identified in Europeans. The study aimed to validate a previously reported PRS in this independent cohort. The MyCode project involved participants and staff from the Geisinger healthcare system, though specific demographic details about the study population size are not clearly stated in the provided excerpts.
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Based on the provided context, the paper by Matsuo et al. titled "IDENTIFICATION OF ABCG2 DYSFUNCTION AS A MAJOR FACTOR CONTRIBUTING TO GOUT" discusses genetic variants in ABCG2 and their role in gout development. This paper identified nonfunctional mutations of ABCG2, a high-capacity urate exporter, in more than 10% of Japanese gout patients. Specifically, they found that: 1. The nonfunctional ABCG2 variant Q126X increases gout risk with an odds ratio of 5.97 2. Some genotype combinations remarkably decrease ABCG2 function (≤25% of wild type) and markedly increase gout risk with an odds ratio of 25.8 3. Up to 10.1% of gout patients had these dysfunctional genotypes, while only 0.9% of Japanese males with normal uric acid levels had the same genotype combinations 4. More than half of Japanese individuals (54.0%) have some ABCG2 dysfunction The paper demonstrates that dysfunctional genotype combinations of ABCG2 are major contributing factors in Japanese gout patients, and that the more severe the ABCG2 transport dysfunction, the more likely an individual is to develop gout.
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Based on the provided research papers, here are the key findings regarding uric acid and its genetic markers: 1. Multiple Genetic Loci Identified: - Several genome-wide association studies (GWAS) have identified multiple genetic loci associated with serum uric acid (SUA) levels - Major signals have been found in populations of European, Asian, and African ancestry - Key genes identified include PDZK1, GCKR, SLC16A9, SLC22A11, SLC22A12, LRRC16A, WDR1, RAF1P1, ZNF5188, and ABCG2 2. Novel Findings: - A study in Indian populations identified PTGR2 gene as a novel locus associated with SUA levels - The PTGR2 gene was found to be involved in prostaglandin metabolism and potentially uric acid regulation 3. Population Differences: - Different genetic associations were found across various populations: - Studies in European ancestry individuals - Studies in Asian populations - Studies in African American populations - Studies in Indian populations 4. Heritability: - Twin studies have shown that uric acid levels have a significant genetic component - The genetic influence on uric acid variation has been demonstrated across different populations 5. Disease Associations: - Genetic variants associated with uric acid levels have been linked to: - Type 2 diabetes - Cardiovascular disease risk factors - Renal disease - Hypertension 6. Environmental Factors: - Studies also acknowledged the importance of environmental factors in uric acid regulation, including: - Diet - Exercise - Body mass index - Lifestyle choices These findings have contributed to our understanding of the genetic basis of uric acid regulation and its potential implications for various health conditions.
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According to the first text excerpt, the study included 6,881 Korean individuals for the initial genome-wide association study (GWAS), and then validated their findings in 3,194 additional individuals. So the total sample size was 10,075 Korean individuals.
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According to the provided context, there were several papers examining serum uric acid levels in different populations: 1. A study by Charles et al. examined serum uric acid levels in African Americans. 2. A study by Cho et al. examined serum uric acid levels in Korean individuals (6,881 Korean individuals in the initial GWAS and 3,194 individuals in the validation cohort). 3. A study examined Chinese twin children and adolescents. Without more specific context about which particular paper you're asking about, I cannot give a single definitive answer, as multiple populations were studied across different papers examining serum uric acid levels.
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Based on the provided text, the paper "Polygenic analysis of the effect of common and low-frequency genetic variants on serum uric acid levels in Korean individuals" published in Nature Scientific Reports discusses genetic variants associated with uric acid levels. This study specifically: 1. Conducted a genome-wide association study (GWAS) for serum uric acid (SUA) in 6,881 Korean individuals 2. Compared findings between Korean and European populations 3. Identified two low-frequency and six common independent variants associated with SUA 4. Found that despite similar effect sizes of variants in Korean and European populations, the proportion of variance explained was greater in the Korean population 5. Identified a rare nonsense variant SLC22A12 p.W258X that showed the most significant association with reduced SUA levels 6. Found an East Asian-specific missense variant (rs671) in ALDH2 that had a significant association with SUA level The paper concludes that further genetic epidemiological studies on SUA are needed in ethnically diverse cohorts to investigate rare or low-frequency variants and determine the influence of genetic and environmental factors on SUA.
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Based on the provided context, I cannot provide specific details about the main findings of that study titled "GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes." While this study is referenced in the text (it appears to be reference #12), the actual findings from that specific paper are not detailed in the excerpts provided. Making claims about its main findings would require access to that specific paper. If you'd like to know about the findings of the current paper being discussed in the context (which appears to be about variants in urate transporters and their association with transition from asymptomatic hyperuricemia to gout), I can provide those details instead.
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According to the context provided, while this paper is referenced in the text, the specific sample size used in that study is not directly mentioned in the given excerpts. Without access to the full paper itself, I cannot definitively state what the sample size was. To provide an accurate answer, I would need to see the relevant sections of that specific paper.
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According to the context provided, this study was conducted in Japanese populations. This can be seen in the reference #12 which mentions "Nakayama A, Nakaoka H, Yamamoto K, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes." The study appears to have been conducted using Japanese participants, though the full details of the study population are not explicitly provided in the given excerpts.
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According to the references shown in the text, this topic is discussed in reference #12: Nakayama A, Nakaoka H, Yamamoto K, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis. 2017;76(5):869–77.
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Based on the provided context, here are the main findings of the study: 1. The researchers identified nonfunctional mutations of ABCG2 (a high-capacity urate exporter) in more than 10% of Japanese gout patients. 2. Key findings about specific mutations: - The nonfunctional ABCG2 variant Q126X was found to be associated with increased gout risk, with an odds ratio (OR) of 5.97 - This Q126X mutation was present in up to 13.5% of primary gout patients in their study population 3. Genotype combinations: - Some combinations of genotypes severely decreased ABCG2 function (≤25% of normal function) - These dysfunctional combinations markedly increased gout risk with an OR of 25.8 - About 10.1% of gout patients had these severely dysfunctional genotype combinations - Only 0.9% of normal controls had these same combinations 4. Overall population impact: - More than half of Japanese individuals (54.0%) have some form of ABCG2 dysfunction - The more severe the ABCG2 transport dysfunction, the more likely an individual is to develop gout 5. The study demonstrated that dysfunctional genotype combinations of ABCG2 are major contributing factors in Japanese gout patients, suggesting ABCG2 could be a major causative gene for gout development. This research is significant because it identifies ABCG2 dysfunction as a common genetic factor in gout development, rather than just a rare Mendelian disorder.
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Based on the provided excerpts, the study included 2,150 total individuals, which is mentioned in one passage: "Among 2,150 individuals, ABCG2 of 3/4 function (39.8%), 1/2 function (12.9%), and ≤1/4 function (1.3%) were found." More specifically, the study included 161 gout patients and 865 normal controls, as indicated when discussing that "up to 13.5% of total gout patients (n=161)" and referring to "the normal population (n=865)."
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According to the text, this study was conducted in a Japanese population. Specifically, the study analyzed: - Japanese gout patients - 871 Japanese male controls - A random sample of 2,150 Japanese individuals The study found that dysfunctional ABCG2 genotype combinations were major contributing factors in Japanese gout patients, with over 10% of Japanese gout patients having nonfunctional mutations of ABCG2.
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Based on the provided context, there are two key papers that discuss ABCG2 dysfunction as a major factor in gout development: 1. "IDENTIFICATION OF ABCG2 DYSFUNCTION AS A MAJOR FACTOR CONTRIBUTING TO GOUT" by H. Matsuo et al., published in Nucleosides, Nucleotides and Nucleic Acids (2011) 2. A research article that appears to be from Science Translational Medicine (2009) which discusses ABCG2 gene as a major causative gene for gout and demonstrates that ABCG2 dysfunction through genetic mutations significantly increases gout risk. Both papers discuss how ABCG2 dysfunction contributes to gout development, with the research showing that ABCG2 is a high-capacity urate exporter and that non-functional mutations of ABCG2 can cause gout. The papers demonstrate that more severe ABCG2 transport dysfunction leads to higher likelihood of developing gout.
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From the provided context, I cannot determine where the participants were recruited from. While the context shows demographic and clinical characteristics of 7,064 participants, including that 23.6% had hypertension, it does not specify the location or source of recruitment for these participants. To accurately answer this question, we would need additional information from other parts of the study that are not included in the given context.
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I apologize, but I don't see any paper specifically examining new treatments for Type 2 diabetes in the provided context. While Type 2 diabetes (T2DM) is mentioned in the context as a characteristic of some study participants, the text appears to be focused on studying genetic associations with serum uric acid (SUA) levels in African and African American populations. The data shows that in the discovery sample (Africans), about 50.12% had Type 2 diabetes, and in the replication sample (African Americans), about 16.4% had Type 2 diabetes, but this wasn't a study about diabetes treatments.
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Looking at the provided context, I cannot definitively answer what the age range was for participants in a study about cognitive decline biomarkers. While there are several studies mentioned in the context, none of them appear to be specifically about cognitive decline biomarkers. The context includes information about various studies related to kidney disease, uric acid levels, and some demographic data from different cohorts, but nothing specifically about cognitive decline biomarkers. If you have a specific study in mind, please provide additional context about it.
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I apologize, but I don't see any information in the provided text about maternal nutrition and birth outcomes. The text appears to contain data from a study comparing characteristics between African and African American populations, with measurements like BMI, blood pressure, diabetes status, etc. If you have a specific question about maternal nutrition and birth outcomes, you would need to provide the relevant text or paper for me to help answer your question accurately.
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According to the references in the text, Xu S, Li X, Zhang S, et al. conducted a study titled "Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study" published in BMC Medicine in 2023. However, the text only mentions this study in the references section (#21) and does not provide any details about its findings or methodology. To provide accurate information about the relationship between gut microbiome and Crohn's disease from this study, we would need access to the actual BMC Medicine paper.
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The paper by Chen et al. (2020) titled "Refining genome-wide associated loci for serum uric acid in individuals with African ancestry" published in Human Molecular Genetics discusses genetic variants associated with uric acid metabolism in African populations. This was the first genome-wide association study (GWAS) for serum uric acid conducted in continental Africans, with replication in African Americans. The study included 4,126 Africans and 5,007 African Americans and identified two significant loci: SLC2A9 on chromosome 4p16.1 and SLC22A12 on chromosome 11q13.1 that were associated with serum uric acid levels. Additionally, the paper by Charles et al. (2011) titled "A genome-wide association study of serum uric acid in African Americans" is also relevant, as it specifically looked at genetic associations with uric acid levels in African Americans, though this was not the first GWAS in continental Africans.
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From the provided text excerpts, I can only definitively identify that African Americans and European ancestry populations were included in genetic studies of serum uric acid levels. Specifically, the text discusses a genome-wide association study that included 1,017 African American participants from the Howard University Family Study and makes comparisons to findings from European ancestry populations. However, I cannot make claims about other ethnic groups that may have been included in any larger cross-ancestry meta-analyses, as that information is not present in the provided text passages. To accurately answer which ethnic groups were included in a large-scale cross-ancestry GWAS meta-analysis, I would need additional text or documentation describing such a study.
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Based on the provided context, I cannot definitively answer which study conducted a meta-analysis to identify common variants influencing uric acid concentrations. While the text mentions previous genome-wide association studies (GWAS) that have identified more than 180 loci contributing to serum uric acid (SUA) levels, it doesn't specifically name which study conducted the meta-analysis. The text appears to be about a GWAS study in Korean individuals, but does not provide information about previous meta-analyses. To accurately answer this question, we would need additional context about previous meta-analysis studies.
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Based on the provided context, several genetic loci were identified across different studies: From the Japanese meta-analysis: - 8,948 variants across 36 genomic loci (P<5×10⁻⁸) were identified, including 8 novel loci: - SESN2 (missense variant) - PNPLA3 (missense variant) - TMEM18 - TM4SF4 - MXD3-LMAN2 - PSORS1C1-PSORS1C2 - HNF4A - LINC01578 From the African ancestry study: - Two major loci were identified: 1. SLC2A9 (4p16.1) 2. SLC22A12 (11q13.1) From the Mexican study (Table 1): - Two loci were identified: 1. SLC2A9 (SNP: rs7678287) 2. ABCG2 (SNP: rs2231142) The studies consistently identified SLC2A9 as an important locus across different populations, though the specific variants sometimes differed between populations.
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I apologize, but I cannot determine the exact number of participants from the provided text excerpts. While the text discusses a GWAS study of serum uric acid levels in Mexican children and adults and mentions some findings related to SLC2A9 and ABCG2 genes, the specific number of participants is not mentioned in the given context. To accurately answer this question, we would need additional information from other parts of the study, such as the methods section or participant demographics.
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According to the text, the study examined 190 DNA samples from pregnant females who self-identified as Filipino from the Hawaii Biorepository Bank. The researchers compared the prevalence of the rs2231142 G>T polymorphism in this Filipino cohort with data from other populations (Caucasians, Han Chinese, and African Americans) from the 1000 Genomes Project Phase III.
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I cannot determine from the provided context which paper identified novel pharmacological mechanisms for allopurinol through genome-wide association and functional studies. While the text contains several references to genome-wide association studies (GWAS) related to kidney function and other traits, none of the references specifically mention allopurinol or its pharmacological mechanisms.
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I apologize, but I don't see any information in the provided text excerpts about findings from a large-scale cross-ancestry GWAS meta-analysis of serum uric acid. The text excerpts show: 1. A GWAS study by Wang et al. that found no SNPs reaching genome-wide significance level, though 25 SNPs were suggestive of association (P < 1 × 10−5) 2. A GWAS study in African populations that identified two genome-wide significant loci: - 4p16.1 (SLC2A9) - 11q13.1 (SLC22A12) To accurately describe findings from a large-scale cross-ancestry meta-analysis, I would need text specifically discussing such an analysis. The provided excerpts focus on individual GWAS studies rather than a cross-ancestry meta-analysis.
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After reviewing the provided text passages, I cannot find any specific information about a study measuring 24-hour urinary excretion of uric acid or its sample size. While the texts discuss various studies related to serum uric acid levels and genetics, none specifically mention measurements of 24-hour urinary excretion. If this information exists, it would need to be found in other sources not provided in the given context.
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Based on the provided text excerpts, the GWAS study was conducted in a Japanese male population. Specifically, it included 2,860 Japanese male gout cases and 3,149 asymptomatic hyperuricemia (AHUA) controls. This can be seen in Figure 2 of the text which states "We performed a GWAS followed by two replication studies (REP1 and REP2 stages) with 2860 Japanese male gout cases and 3149 AHUA controls."
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The study by Wang et al. (2018) published in Frontiers in Endocrinology examined the heritability and genome-wide associations of serum uric acid (SUA) in middle and old-aged Chinese twins. Specifically: Key details of the study: 1. Sample size: 379 twin pairs from China, including both monozygotic (MZ) and dizygotic (DZ) twins 2. Heritability findings: - MZ twin correlation (rMZ = 0.56) was larger than DZ twin correlation (rDZ = 0.39) - For females: - Additive genetic effects: 46.3% - Shared environmental effects: 26.3% - Unique environmental effects: 27.5% - For males: - Additive genetic effects: 29.9% - Shared environmental effects: 33.1% - Unique environmental effects: 37.0% 3. Genome-wide association findings: - No SNPs reached genome-wide significance level - 25 SNPs showed suggestive associations (P < 1 × 10-5) - 167 genes were nominally associated with SUA level (P < 0.05) - Identified one pathway related to transmembrane transport of small molecules and 20 GO gene sets involving ion transport and other functions The study concluded that SUA shows moderate heritability in women and low heritability in men in the Chinese population, with genetic variations significantly involved in functional genes and regulatory domains that mediate SUA level.
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Based on the provided text, there are notable sex differences in the genetic effects on serum uric acid (SUA) levels: 1. Heritability estimates show stronger genetic influence in females compared to males: - Females: Additive genetic effects account for 46.3% of SUA variation - Males: Additive genetic effects account for 29.9% of SUA variation 2. Environmental effects show the opposite pattern: - Females: Environmental factors account for 53.7% of SUA variation - Males: Environmental factors account for 70.1% of SUA variation 3. The text mentions that these sex differences align with previous research by Boyle et al., who also found a more significant genetic component in females and stronger environmental component in males. 4. The authors speculate that these sex differences in genetic architecture may be due to different selective pressures between males and females during human evolution. 5. The text also notes that female hormones can influence renal uptake of uric acid, and there are sex-related effects of genetic variations in genes SLC2A9 and ABCG2, though specific details about these effects are not provided in the excerpts. The normal physiological range for uric acid also differs by sex: - Men: 2.5-7.5 mg/dL - Women: 2.0-6.5 mg/dL
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According to the paper, in the discovery cohort, they studied 5,648 Japanese subjects (excluding patients with dialysis and population outliers) and tested the association of renal function-related traits with 24,579 single nucleotide variants (SNVs). Additionally, they examined 7,699 Japanese subjects in a replication study. So in total, they studied 13,347 Japanese individuals (5,648 + 7,699).
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According to the text, this study was conducted on a Han Chinese population. Specifically, the text states: "Here, we present the first large-scale metaboGWAS of a Han Chinese population to identify effective metaboQTLs." The study involved 1551 subjects with CAD from three clinical centers.
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According to the paper, Yasukochi et al. (2018) published in Molecular Genetics and Genomics investigated CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population. This was a longitudinal exome-wide association study (EWAS) that examined ~244,000 genetic variants and clinical data from Japanese individuals who had undergone annual health checkups for several years. The study identified rs55975541 in CDC42BPG as significantly associated with serum uric acid levels in both the discovery cohort and replication study.
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I apologize, but I cannot provide a specific answer about genetic factors influencing allopurinol treatment response based on the provided text passages. While the text discusses some genetic associations with gout susceptibility and uric acid levels (mentioning genes like GLUT9, NPT1, PDZK1, and SLC17A3), it does not contain any information about genetic factors that influence allopurinol treatment response. To accurately answer your question about genetic factors affecting allopurinol treatment response, we would need different source material that specifically addresses pharmacogenetic aspects of allopurinol therapy in gout patients. If you're interested in this topic, I'd recommend consulting medical literature specifically focused on allopurinol pharmacogenetics or speaking with a healthcare provider who specializes in gout treatment.
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According to the text, the study included 8,314 control subjects and 520 patients with gout from the Korea Association REsource (KARE) cohort, for a total sample size of 8,834 subjects. The study started with 10,038 unrelated Korean individuals, but after screening for genotype calling and quality control, 8,842 individuals remained. Four additional individuals were excluded due to missing phenotype or covariate information, resulting in the final sample of 8,834 subjects (8,314 controls + 520 gout patients).
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According to the first context, the study was conducted on Korean individuals. Specifically, they conducted a genome-wide association study (GWAS) for serum uric acid (SUA) in 6,881 Korean individuals and validated the association of low-frequency variants and polygenic risk scores (PRSs) in 3,194 Korean individuals.
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According to the provided context, Charles et al. (2011) performed a genome-wide association study (GWAS) of serum uric acid levels in African Americans. The study included 1,017 African American participants from the Howard University Family Study. The genotyping was performed using the Affymetrix Genome-wide Human SNP Array 6.0, and they analyzed 2,400,542 single nucleotide polymorphisms (SNPs) for association with serum uric acid levels.
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Based on the provided context, the study found that variants in SLC2A9 and SLC22A11 genes were significantly associated with serum uric acid (SUA) levels in Indian populations. Specifically: 1. Five SNPs in two different genes reached genome-wide significance: - SLC2A9 gene variants: - rs3775948 (most significant association, p-value = 1.7 × 10−19) - rs16890979 (p-value = 2.62 × 10−18) - rs11722228 (p-value = 7.34 × 10−16) - rs737267 (p-value = 2.71 × 10−16) - SLC22A11 gene variant: - rs2078267 (p-value = 3.26 × 10−11) Additionally, a missense variant rs2231142 in ABCG2 gene showed near genome-wide significance (p-value = 7.82 × 10−8). However, it's important to note that these associations were primarily identified in normoglycemic (healthy) individuals. While the study did examine the effect size of these variants in 1,077 Type 2 diabetes subjects, the specific differences in effect sizes between diabetic and non-diabetic subjects are not clearly detailed in the provided excerpts. The study appears to be the first genetic epidemiological study specifically examining uric acid levels in Indian populations, filling an important knowledge gap in understanding population-specific genetic factors affecting uric acid levels.
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From the provided context, I cannot determine the specific sample size used in identifying TRIM46 as a key regulator in gout pathogenesis. While the study discusses TRIM46's role and shows it had a positive correlation with gout (beta = 1.34), and describes various analyses involving TRIM46, the actual sample size is not explicitly mentioned in any of the provided excerpts.
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Based on the provided context, this research appears to have been conducted on a Korean population. This can be determined from the mention of the Korea Association Resource (KARE) cohort, which is described as consisting of 10,038 Korean adults aged 40-69 who were representative samples of residents from two South Korean cities (Ansung and Ansan). The study included a discovery set of 7,064 participants and used KARE cohort data for validation of BUN, serum creatinine, and GFR measurements.
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The research paper by Zhou et al. (2019) published in Hereditas examined the role of SLC28A2 gene in serum uric acid (SUA) levels, hyperuricemia (HUA), and gout in Han Chinese populations. The study recruited three sample sets consisting of: 1. 1,376 gout patients 2. 1,290 long-term HUA subjects (no gout attack) 3. 1,349 normouricemic controls The researchers genotyped eight polymorphisms in the SLC28A2 gene using ligase detection reaction-polymerase chain reaction (LDR-PCR) technology. Their key findings included: - Rs16941238 showed the most significant associations with SUA level and HUA, but not with gout - Rs2271437 was significantly associated with gout and was confirmed in meta-analysis with previous gout GWAS dataset - Each variant generally showed consistent odds ratio direction for both gout and HUA compared to normouricemic controls The study concluded that their findings supported associations between the SLC28A2 gene and serum uric acid level, hyperuricemia phenotype, and gout in Han Chinese populations.
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Based on the provided text, here are the key findings from the polygenic analysis of serum uric acid (SUA) levels in Korean individuals: 1. Genetic Variants: - Identified 2 low-frequency variants and 6 common independent variants associated with SUA levels - A rare nonsense variant SLC22A12 p.W258X showed the strongest association with reduced SUA levels 2. Population Differences: - While the effect sizes of variants were similar between Korean and European populations, the variants explained a greater proportion of variance in SUA levels in the Korean population - An East Asian-specific missense variant (rs671) in ALDH2 showed a significant association with SUA levels on chromosome 12 3. Validation: - The polygenic risk scores (PRSs) of common variants associated with SUA levels were significant across multiple Korean cohorts - The findings were validated in 3,194 individuals 4. Sex-Related Factors: - The study noted that female hormones influence renal uptake of uric acid - Genetic variations in SLC2A9 and ABCG2 were reported as sex-related factors 5. Clinical Implications: - The findings suggest the need for further genetic epidemiological studies in ethnically diverse cohorts - Emphasis on investigating rare or low-frequency variants - Importance of understanding both genetic and environmental factors affecting SUA levels The study highlights the significance of population-specific genetic variants in determining SUA levels and suggests that genetic factors may have a stronger influence on SUA levels in the Korean population compared to European populations.
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From the provided context, I cannot determine the exact number of participants included in the genome-wide meta-analysis. While the text mentions that 7,128,846 SNPs were tested in the genome-wide meta-analysis between ROL type vs. RUE type, it does not explicitly state the number of participants involved in the study.
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Based on the provided context, several studies examined different ethnic groups: 1. The KARE (Korea Association REsource) cohort study looked at Korean subjects (8,314 controls and 520 gout patients) 2. A Japanese study examined Japanese male gout cases (2,860 cases) and controls (3,149 asymptomatic hyperuricemia controls) 3. A Taiwanese study focused on female participants from the Taiwan biobank and China Medical University Hospital in Taiwan However, I cannot definitively identify which specific study you're referring to regarding the integrative genome-wide association studies of eQTL and GWAS data for gout susceptibility, as this specific analysis is not clearly described in the provided context. To accurately answer which ethnic group was studied in that particular research, I would need the specific context about that study.
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Based on the provided context, I cannot definitively identify which research paper first discovered population-specific low-frequency variants in SLC22A12 affecting serum uric acid levels. While the given text appears to be from a PLOS ONE paper that studied SLC22A12 variants in Koreans, it doesn't explicitly state which paper first identified these population-specific variants. The context mainly describes the methodology and results of this particular study but doesn't discuss the historical discovery of these variants.
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Based on the provided research papers, here are the key genetic factors associated with serum uric acid (SUA) levels in Han Chinese populations: Key Genes/Variants: 1. Common variants: - SLC2A9 and ABCG2 were identified as important genes affecting SUA levels - An East Asian-specific missense variant (rs671) in ALDH2 showed significant association with SUA levels 2. Notable genetic regions: - Chromosome 17 around rs2253277 - Chromosome 14 around rs11621523 in PTGR2 gene 3. Gene-based analysis identified 167 genes nominally associated with SUA levels (p<0.05), including: - PTGR2 - ENTPD5 - SLC2A9 Heritability: - Twin studies showed moderate heritability in women (46.3% additive genetic effects) and lower heritability in men (29.9% additive genetic effects) - Environmental factors also played a role: - Common/shared environmental factors: 26.3% in females, 33.1% in males - Unique/nonshared environmental factors: 27.5% in females, 37.0% in males Pathways: The research identified enrichment in: - Transmembrane transport of small molecules pathway - 20 GO gene sets involved in: - Ion transport - Transmembrane transporter activity - Hydrolase activity acting on acid anhydrides It's worth noting that while multiple genetic associations were found, many only reached suggestive significance levels rather than genome-wide significance in the studies reviewed.
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According to the papers, the study included: - 6,881 Korean individuals in the initial GWAS (genome-wide association study) - 3,194 individuals in the validation cohort Additionally, one of the papers mentions a cohort of 7,064 participants with detailed demographic and clinical characteristics. The numbers vary slightly between the papers as they appear to be related studies looking at serum uric acid levels in Korean populations. The largest single sample size mentioned was 7,064 participants.
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According to the text, this research was conducted on Japanese populations. Specifically, the study examined: 1. 90 Japanese hyperuricemia patients for mutation analysis 2. 739 Japanese individuals for QTL analysis of SUA concentrations 3. 228 Japanese male hyperuricemia cases (including 161 gout cases) 4. 871 Japanese male controls The findings showed that ABCG2 dysfunction was a major contributing factor to gout in the Japanese population, with up to 10.1% of Japanese gout patients having certain genotype combinations that severely impaired ABCG2 function. The researchers noted that while their findings were specific to Japanese populations, there might be similar nonfunctional mutations in other populations, though further research would be needed to confirm this in non-Japanese gout patients.
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According to the text, a Genome-wide association study (GWAS) revealed novel loci that aggravate asymptomatic hyperuricemia into gout. Specifically, this was the first ever GWAS that compared clinically defined gout cases and asymptomatic hyperuricemia (AHUA) controls. The study was conducted with Japanese men, analyzing a total of 2,860 gout cases and 3,149 AHUA controls through a GWAS followed by 2 replication studies.
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Based on the provided context, a key finding was the identification of a rare nonsense variant in SLC22A12 (p.W258X, also referred to as rs121907892) that showed a significant association with reduced serum uric acid (SUA) levels in Koreans. This variant appears to be population-specific, as it is found only in Koreans and Japanese populations, and cannot be applied to other populations. The study notes that this was one of the biggest limitations - that rs121907892 is found only in Koreans and Japanese, so the results cannot be generalized to other populations. While this genetic locus has been repeatedly found in genome-wide association studies (GWAS) of other populations, there might be other low-frequency variants similar to rs121907892 in other ethnic groups, but since this study was conducted only in Koreans, they were unable to identify such variations in other ethnic groups. The study suggests that for many GWAS results involving common variants where molecular mechanisms are unknown, additional approaches like deep sequencing or functional experiments would be needed to discover the mechanisms of action or actual functioning variants surrounding them. This finding highlights the importance of conducting genetic studies in diverse populations to identify population-specific variants that may influence serum uric acid levels.
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From the provided context, I can only see that 1,017 African American participants from the Howard University Family Study were included in one analysis. However, I cannot determine the total number of individuals examined in any cross-ancestry meta-analysis, as this specific information is not provided in the given text passages. To provide an accurate answer about the total number of individuals in a cross-ancestry meta-analysis, I would need additional context containing that specific information.
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According to the Methods section of the paper, the study population consisted of female participants between 30 and 70 years of age who were collected from two sources in Taiwan: 1. Taiwan Biobank (TWB) 2. China Medical University Hospital in Taiwan The participants were grouped into two cohorts: - A discovery cohort (which also served as the base cohort) - A replication cohort (which served as the targeted cohort for polygenic risk score analysis) The study specifically focused on Taiwanese female participants and was approved by the Institutional Review Board of the China Medical University Hospital in Taiwan and the Ethics Committee of Academia Sinica.
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According to the text, this study (published in Scientific Reports) identified rs671 (Glu504Lys) in ALDH2 as a genuine gout-associated SNP through fine-mapping analysis of the MYL2-CUX2 locus. While a previous smaller Japanese study with 180 gout cases and 49 controls had indicated an association between rs671 and gout, this current study provided more definitive evidence through a larger sample size (1,048 gout cases and 1,334 controls) and comprehensive fine-mapping approach. The study found that among 9 tag SNPs selected from 8,595 SNPs in the MYL2-CUX2 region, only rs671 remained significantly associated with gout after statistical adjustments, confirming it as a genuine gout susceptibility locus.
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Based on the provided text, several genetic loci have been identified that influence the progression from asymptomatic hyperuricemia to gout. These can be categorized into two groups: 1. Genes with GWAS catalog availability: - Gout only: ZNF724 and CNTN5 - Both uric acid measurement and gout: * ALDH2 * GCKR-C2orf16 * SHLD2 * SLC22A11-SLC22A12 * SLC2A9 2. Genes without GWAS catalog availability: - CD2–PTGFRN - SLC28A3-NTRK2 - MIR302F - MEPE - PPM1K-DT - LOC105377323 - ADH1B These genetic factors appear to play a role in the transition from having elevated uric acid levels without symptoms (asymptomatic hyperuricemia) to developing clinical gout. The findings are particularly relevant to Caucasian populations, as indicated by the research using UK Biobank data. It's worth noting that this represents our current understanding of the genetic factors, and research in this area is ongoing. The complexity of gout development likely involves interactions between these genetic factors and environmental influences.
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Based on the provided context, the study included: - 747 gout patients - 747 male individuals with hyperuricemia - 2,071 male normal controls The data was obtained from the Taiwan biobank database, which provided gout history, uric acid levels, creatinine, biochemical markers, demographic information, and Affymetrix TWB 650K SNP chip data for analysis.
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According to the research papers, the study was conducted using data from the Geisinger MyCode community health initiative, which is an electronic health record-linked biobank for precision medicine research. While the specific population demographics aren't explicitly stated in the excerpts, the polygenic risk score (PRS) was constructed from 110 significant urate-associated variants identified in Europeans, suggesting the study focused primarily on individuals of European ancestry. This is further supported by the fact that they compared European-specific PRS with trans-ethnic PRS in their analysis. The study was a large-scale cohort study, but the exact total number of participants isn't directly stated in the provided excerpts, though one figure mentions n=44,062 in relation to the PRS distribution.
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Based on the provided context, the paper by Matsuo et al. titled "IDENTIFICATION OF ABCG2 DYSFUNCTION AS A MAJOR FACTOR CONTRIBUTING TO GOUT" discusses genetic variants in ABCG2 and their role in gout development. This paper identified nonfunctional mutations of ABCG2, a high-capacity urate exporter, in more than 10% of Japanese gout patients. They found that some genotype combinations significantly decrease ABCG2 function (≤25% of wild type) and markedly increase gout risk, with an odds ratio of 25.8. The study demonstrated that dysfunctional genotype combinations of ABCG2 are major contributing factors in Japanese gout patients, and that the Q126X variant in particular may be a major causative mutation for gout, being present in up to 13.5% of primary gout patients in their population.
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Based on the provided research papers, here are the key findings regarding uric acid and its genetic markers: 1. Multiple Genetic Loci Identified: - Several genome-wide association studies (GWAS) have identified multiple genetic loci associated with serum uric acid (SUA) levels - Major signals have been found in populations of European, Asian, and African ancestry - Some key genes identified include: PDZK1, GCKR, SLC16A9, SLC22A11, SLC22A12, LRRC16A, WDR1, RAF1P1, ZNF5188, and ABCG2 2. Novel Findings: - A study in Indian populations identified PTGR2 gene as significantly associated with SUA levels - The study found variants in the 14q24.3 chromosomal region showing strong association with uric acid levels 3. Population Differences: - Different genetic associations have been found across various ethnic groups - Studies have been conducted in: - European ancestry populations - Asian populations - African American populations - Indian populations 4. Heritability: - Studies suggest there is a significant genetic component to uric acid levels - Twin studies have helped establish the genetic basis of uric acid variation 5. Clinical Relevance: - These genetic markers may be associated with: - Type 2 diabetes - Cardiovascular disease risk factors - Renal disease - Hypertension - Decreased glomerular filtration rate The research demonstrates that uric acid levels are influenced by multiple genetic factors that can vary across different populations, with implications for understanding and treating related health conditions.
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According to the first context passage, the study included 6,881 Korean individuals for the initial genome-wide association study (GWAS), and then validated their findings in 3,194 additional individuals. So the total sample size was 10,075 Korean individuals.
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According to the provided context, there were several papers examining serum uric acid levels in different populations: 1. A study by Charles et al. examined serum uric acid levels in African Americans. 2. A study by Cho et al. examined serum uric acid levels in Korean individuals, specifically looking at 6,881 Korean individuals for the initial GWAS and validating findings in 3,194 additional Korean individuals. 3. A study examined Chinese twin children and adolescents. Without more specific context about which particular paper you're asking about, I cannot give a single definitive answer, as multiple populations were studied across different papers examining serum uric acid levels.
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Based on the provided text, the paper "Polygenic analysis of the effect of common and low-frequency genetic variants on serum uric acid levels in Korean individuals" published in Nature Scientific Reports discusses genetic variants associated with uric acid levels. This study specifically: 1. Conducted a genome-wide association study (GWAS) for serum uric acid (SUA) in 6,881 Korean individuals 2. Compared findings between Korean and European populations 3. Identified two low-frequency and six common independent variants associated with SUA 4. Found that despite similar effect sizes of variants in Korean and European populations, the proportion of variance explained was greater in the Korean population 5. Identified a rare nonsense variant SLC22A12 p.W258X that showed the most significant association with reduced SUA levels 6. Found an East Asian-specific missense variant (rs671) in ALDH2 that had a significant association with SUA levels The paper concludes by suggesting that further genetic epidemiological studies on SUA are needed in ethnically diverse cohorts to investigate rare or low-frequency variants and determine the influence of genetic and environmental factors on SUA.
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Based on the provided context, I cannot provide a detailed answer about the main findings of that specific study. While this paper is referenced in the text (as reference #12), the actual findings of that study are not described in the excerpts provided. The title is only mentioned in the references section without any discussion of its results. To accurately describe the main findings of that study, I would need access to the full text of that specific paper.
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According to the context provided, while this paper is referenced in the text, the specific sample size used in that study is not directly mentioned in the given excerpts. Without access to the full paper itself, I cannot definitively state what the sample size was. To provide an accurate answer, I would need to see the relevant sections of that specific paper.
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According to the context provided, this study was conducted in Japanese populations. This can be seen in the reference where it mentions "Nakayama A, Nakaoka H, Yamamoto K, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis. 2017;76(5):869–77." While the full details of the study population aren't explicitly shown in the provided excerpts, the authors and institutional affiliations suggest it was conducted in Japanese populations.
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According to the references in the text, this topic is discussed in reference #12: Nakayama A, Nakaoka H, Yamamoto K, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis. 2017;76(5):869–77.
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Based on the provided context, here are the main findings of the study: 1. The researchers identified nonfunctional mutations of ABCG2 (a high-capacity urate exporter) in more than 10% of Japanese gout patients. 2. Key findings about specific mutations: - The nonfunctional ABCG2 variant Q126X was found to be associated with increased gout risk, with an odds ratio (OR) of 5.97 - This Q126X mutation was present in up to 13.5% of primary gout patients in their study population 3. Important findings about genotype combinations: - Certain combinations of mutations remarkably decreased ABCG2 function (to ≤25% of normal) - These dysfunctional combinations significantly increased gout risk with an OR of 25.8 - About 10.1% of gout patients had these severely dysfunctional genotype combinations - Only 0.9% of normal controls had these same genotype combinations 4. Overall population impact: - More than half of Japanese individuals (54.0%) were found to have some level of ABCG2 dysfunction - The severity of ABCG2 transport dysfunction correlates with the likelihood of developing gout 5. The study demonstrated that ABCG2 dysfunction is a major contributing factor to gout development, particularly in the Japanese population studied. These findings suggest that ABCG2 could be considered a major causative gene for gout, especially through its dysfunctional variants and their combinations.
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Based on the provided excerpts, the study included 2,150 total individuals, which is mentioned when discussing ABCG2 function distribution: "Among 2,150 individuals, ABCG2 of 3/4 function (39.8%), 1/2 function (12.9%), and ≤1/4 function (1.3%) were found." More specifically, the study included: - 161 gout patients - 865 normal controls (with SUA ≤ 7.0 mg/dl) This can be seen when the text mentions that "up to 13.5% of total gout patients (n=161)" and refers to "0.9% of the normal population (n=865)."
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According to the text, this study was conducted in a Japanese population. Specifically, the study analyzed: - Japanese male gout patients (161 cases) - 871 Japanese male controls - A random sample of 2,150 Japanese individuals for genotyping to investigate ABCG2 dysfunction frequency The study was conducted at several Japanese institutions including the National Defense Medical College, University of Tokyo, Tokyo University of Pharmacy and Life Sciences, and other Japanese research centers.
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Based on the provided context, there are two key papers that discuss ABCG2 dysfunction as a major factor in gout development: 1. "IDENTIFICATION OF ABCG2 DYSFUNCTION AS A MAJOR FACTOR CONTRIBUTING TO GOUT" by H. Matsuo et al., published in Nucleosides, Nucleotides and Nucleic Acids (2011) 2. A research article that appears to be from Science Translational Medicine (2009) which discusses ABCG2 gene as a major causative gene for gout and demonstrates that ABCG2 dysfunction through genetic mutations significantly increases gout risk. Both papers discuss how ABCG2 dysfunction contributes to gout development, with the research showing that ABCG2 is a high-capacity urate exporter and that non-functional mutations of ABCG2 can cause gout. The papers demonstrate that the more severe the ABCG2 transport dysfunction, the more likely an individual is to develop gout.
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From the provided context, I cannot determine where the participants were recruited from. While the context shows detailed demographic and clinical characteristics of 7,064 participants, including that 23.6% had hypertension, it does not specify the location or source of recruitment for these participants. To accurately answer this question, we would need additional information from other parts of the study.
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I apologize, but I don't see any paper specifically examining new treatments for Type 2 diabetes in the provided context. While Type 2 diabetes (T2DM) is mentioned in the context as a characteristic of some study participants, the text appears to be focused on studying genetic associations with serum uric acid (SUA) levels in African and African American populations. The data shows that in the discovery sample (Africans), about 50.12% had Type 2 diabetes, and in the replication sample (African Americans), about 16.4% had Type 2 diabetes, but this wasn't a study about diabetes treatments.
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Looking at the provided context, I cannot definitively answer what the age range was for participants in a study about cognitive decline biomarkers. While there are several studies mentioned in the context, none of them specifically focus on cognitive decline biomarkers. The context includes information about various studies related to kidney disease, uric acid levels, and some demographic data from different cohorts, but nothing specifically about cognitive decline biomarkers. If you have a specific study in mind, please provide additional context about that study.
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I apologize, but I don't see any information in the provided text about maternal nutrition and birth outcomes. The text appears to contain data from a study comparing characteristics between African and African American populations, with measurements of things like BMI, blood pressure, diabetes status, and other clinical parameters. Without access to a paper about maternal nutrition and birth outcomes, I cannot make any claims about what population was studied in such a paper.
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According to the references listed, Xu S, Li X, Zhang S, et al. conducted a study titled "Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study" published in BMC Medicine in 2023. This appears to be reference #21 in the text. However, since only the citation is provided without any details about the actual research findings, I cannot make any specific claims about what this study found regarding the relationship between gut microbiota and Crohn's disease.
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The paper by Chen et al. (2020) titled "Refining genome-wide associated loci for serum uric acid in individuals with African ancestry" published in Human Molecular Genetics discusses genetic variants associated with uric acid metabolism in African populations. This was the first genome-wide association study (GWAS) for serum uric acid conducted in continental Africans, with replication in African Americans. The study identified two significant loci: 1. 4p16.1 (SLC2A9) 2. 11q13.1 (SLC22A12) The study included: - 4,126 Africans - 5,007 African Americans The paper identified three associations at these two loci and through functional annotation was able to identify candidate causal SNPs for all three signals. Collectively, these three associations accounted for 4.3% of the variance in serum uric acid levels. Additionally, the Charles et al. (2011) paper "A genome-wide association study of serum uric acid in African Americans" is also mentioned in the literature, but the Chen et al. paper appears to be the first GWAS specifically focused on continental African populations.
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The paper by Chen et al. (2020) titled "Refining genome-wide associated loci for serum uric acid in individuals with African ancestry" published in Human Molecular Genetics discusses genetic variants associated with uric acid metabolism in African populations. This was the first genome-wide association study (GWAS) for serum uric acid conducted in continental Africans, with replication in African Americans. The study included 4,126 Africans and 5,007 African Americans and identified two significant loci: SLC2A9 on chromosome 4p16.1 and SLC22A12 on chromosome 11q13.1 that were associated with serum uric acid levels. Additionally, the paper by Charles et al. (2011) titled "A genome-wide association study of serum uric acid in African Americans" is also relevant, as it specifically looked at genetic associations with uric acid levels in African Americans.
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According to the text, this study was conducted in a Japanese population. Specifically, the study analyzed: - Japanese male gout patients (161 cases) - 871 Japanese male controls - A random sample of 2,150 Japanese individuals for genotyping to investigate ABCG2 dysfunction frequency The study was conducted at several Japanese institutions including the National Defense Medical College, University of Tokyo, Tokyo University of Pharmacy and Life Sciences, and other Japanese research centers.
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Based on the provided context, there are two key papers that discuss ABCG2 dysfunction as a major factor in gout development: 1. "IDENTIFICATION OF ABCG2 DYSFUNCTION AS A MAJOR FACTOR CONTRIBUTING TO GOUT" by H. Matsuo et al., published in Nucleosides, Nucleotides and Nucleic Acids (2011) 2. A research article that appears to be from Science Translational Medicine (2009) which discusses ABCG2 gene as a major causative gene for gout and demonstrates that ABCG2 dysfunction through genetic mutations significantly increases gout risk. Both papers discuss how ABCG2 dysfunction contributes to gout development, with the research showing that ABCG2 is a high-capacity urate exporter and that non-functional mutations of ABCG2 can cause gout. The papers demonstrate that more severe ABCG2 transport dysfunction leads to higher likelihood of developing gout.
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From the provided context, I cannot determine where the participants were recruited from. While the context shows demographic and clinical characteristics of 7,064 participants, including that 23.6% had hypertension, it does not specify the location or source of recruitment for these participants. To accurately answer this question, we would need additional information from other parts of the study that are not included in the given context.
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I apologize, but I don't see any paper specifically examining new treatments for Type 2 diabetes in the provided context. While Type 2 diabetes (T2DM) is mentioned in the context as a characteristic of some study participants, the text appears to be focused on studying genetic associations with serum uric acid (SUA) levels in African and African American populations. The data shows that in the discovery sample (Africans), about 50.12% had Type 2 diabetes, and in the replication sample (African Americans), about 16.4% had Type 2 diabetes, but this wasn't a study about diabetes treatments.
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From the provided context, I cannot determine the specific age range of participants in a study about cognitive decline biomarkers. While there are several studies mentioned in the context with age-related information, none of them are specifically about cognitive decline biomarkers. The context includes information about various studies related to kidney disease, cardiovascular conditions, and some demographic data, but nothing specifically about cognitive decline biomarkers.
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I apologize, but I don't see any information in the provided text about maternal nutrition and birth outcomes. The text appears to contain data from two different population samples - Africans (discovery sample) and African Americans (replication sample) - with various clinical measurements like BMI, blood pressure, uric acid levels, etc. There is no mention of maternal nutrition or birth outcomes in the given context. To accurately answer your question, I would need information from the specific paper about maternal nutrition that you're referring to.
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According to the references listed, Xu S. et al. (2023) conducted a multi-omics Mendelian randomization study that examined oxidative stress gene expression, DNA methylation, and gut microbiota interaction in relation to Crohn's disease. This is mentioned in reference #21: "Xu S, Li X, Zhang S, Qi C, Zhang Z, Ma R, et al. Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study. BMC Med. 2023;21:179."
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The paper by Chen et al. (2020) titled "Refining genome-wide associated loci for serum uric acid in individuals with African ancestry" published in Human Molecular Genetics discusses genetic variants associated with uric acid metabolism in African populations. This was the first genome-wide association study (GWAS) for serum uric acid conducted in continental Africans, with replication in African Americans. The study included 4,126 Africans and 5,007 African Americans and identified two significant loci: SLC2A9 on chromosome 4p16.1 and SLC22A12 on chromosome 11q13.1 that were associated with serum uric acid levels. There is also another relevant paper by Charles et al. (2011) titled "A genome-wide association study of serum uric acid in African Americans" that specifically looked at genetic variants in African Americans, but the 2020 Chen et al. paper was the first to study continental African populations.
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