scholarly journals Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrienne Tin ◽  
Pascal Schlosser ◽  
Pamela R. Matias-Garcia ◽  
Chris H. L. Thio ◽  
Roby Joehanes ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Cardiometabolic Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elevated Serum ◽  
Complex Trait ◽  
Serum Urate ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractElevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E–7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.

scholarly journals Sex Differences in Urate Handling

2020 ◽  
Vol 21 (12) ◽  
pp. 4269 ◽  
Author(s):  
Victoria L. Halperin Kuhns ◽  
Owen M. Woodward
Keyword(s):  
Sex Differences ◽  
Disease Risk ◽  
Association Studies ◽  
Elevated Serum ◽  
Serum Urate ◽  
Genome Wide Association Studies ◽  
Renal Handling ◽  
Physiological Regulation ◽  
Genome Wide

Hyperuricemia, or elevated serum urate, causes urate kidney stones and gout and also increases the incidence of many other conditions including renal disease, cardiovascular disease, and metabolic syndrome. As we gain mechanistic insight into how urate contributes to human disease, a clear sex difference has emerged in the physiological regulation of urate homeostasis. This review summarizes our current understanding of urate as a disease risk factor and how being of the female sex appears protective. Further, we review the mechanisms of renal handling of urate and the significant contributions from powerful genome-wide association studies of serum urate. We also explore the role of sex in the regulation of specific renal urate transporters and the power of new animal models of hyperuricemia to inform on the role of sex and hyperuricemia in disease pathogenesis. Finally, we advocate the use of sex differences in urate handling as a potent tool in gaining a further understanding of physiological regulation of urate homeostasis and for presenting new avenues for treating the constellation of urate related pathologies.

scholarly journals Host genetics and microbiome associations from the lens of genome wide association studies

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal
Keyword(s):  
Gut Microbiome ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Lessons Learned ◽  
Host Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.

scholarly journals Both variants of A1CF and BAZ1B genes are associated with gout susceptibility: a replication study and meta-analysis in a Japanese population

Human Cell ◽  
2021 ◽  
Vol 34 (2) ◽  
pp. 293-299
Author(s):  
Makoto Kawaguchi ◽  
Akiyoshi Nakayama ◽  
Yuka Aoyagi ◽  
Takahiro Nakamura ◽  
Seiko Shimizu ◽  
...  
Keyword(s):  
Japanese Population ◽  
Association Studies ◽  
Meta Analysis ◽  
Elevated Serum ◽  
Common Type ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Replication Studies ◽  
Genome Wide

AbstractGout is a common type of acute arthritis that results from elevated serum uric acid (SUA) levels. Recent genome-wide association studies (GWASs) have revealed several novel single nucleotide polymorphism (SNPs) associated with SUA levels. Of these, rs10821905 of A1CF and rs1178977 of BAZ1B showed the greatest and the second greatest significant effect size for increasing SUA level in the Japanese population, but their association with gout is not clear. We examined their association with gout using 1411 clinically-defined Japanese gout patients and 1285 controls, and meta-analyzed our previous gout GWAS data to investigate any association with gout. Replication studies revealed both SNPs to be significantly associated with gout (P = 0.0366, odds ratio [OR] with 95% confidence interval [CI]: 1.30 [1.02–1.68] for rs10821905 of A1CF, P = 6.49 × 10–3, OR with 95% CI: 1.29 [1.07–1.55] for rs1178977 of BAZ1B). Meta-analysis also revealed a significant association with gout in both SNPs (Pmeta = 3.16 × 10–4, OR with 95% CI: 1.39 [1.17–1.66] for rs10821905 of A1CF, Pmeta = 7.28 × 10–5, OR with 95% CI 1.32 [1.15–1.51] for rs1178977 of BAZ1B). This study shows the first known association between SNPs of A1CF, BAZ1B and clinically-defined gout cases in Japanese. Our results also suggest a shared physiological/pathophysiological background between several populations, including Japanese, for both SUA increase and gout susceptibility. Our findings will not only assist the elucidation of the pathophysiology of gout and hyperuricemia, but also suggest new molecular targets.

scholarly journals Quantifying genetic heterogeneity between continental populations for human height and body mass index

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Guo ◽  
Andrew Bakshi ◽  
Ying Wang ◽  
Longda Jiang ◽  
Loic Yengo ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Complex Trait ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Population Difference ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ($$r_{g}$$ r g ) or genome-wide significant SNPs ($$r_{{g\left( {GWS} \right)}}$$ r g GWS ) for height and body mass index (BMI) in samples of European (EUR; $$n = 49,839$$ n = 49 , 839 ) and African (AFR; $$n = 17,426$$ n = 17 , 426 ) ancestry. The $$\hat{r}_{g}$$ r ^ g between EUR and AFR was 0.75 ($${\text{s}}.{\text{e}}. = 0.035$$ s . e . = 0.035 ) for height and 0.68 ($${\text{s}}.{\text{e}}. = 0.062$$ s . e . = 0.062 ) for BMI, and the corresponding $$\hat{r}_{{g\left( {GWS} \right)}}$$ r ^ g GWS was 0.82 ($${\text{s}}.{\text{e}}. = 0.030$$ s . e . = 0.030 ) for height and 0.87 ($${\text{s}}.{\text{e}}. = 0.064$$ s . e . = 0.064 ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\hat{r}_{g}$$ r ^ g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

scholarly journals Quantifying genetic heterogeneity between continental populations for human height and body mass index

2019 ◽  
Author(s):  
Jing Guo ◽  
Andrew Bakshi ◽  
Ying Wang ◽  
Longda Jiang ◽  
Loic Yengo ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Complex Trait ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Population Difference ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide significant SNPs (rg(GWS)) for height and body mass index (BMI) in samples of European (EUR; n = 49,839) and African (AFR; n = 17,426) ancestry. The between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

scholarly journals Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

2021 ◽  
Author(s):  
Mark J. O’Connor ◽  
Philip Schroeder ◽  
Alicia Huerta-Chagoya ◽  
Paula Cortés-Sánchez ◽  
Silvía Bonàs-Guarch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Low Frequency ◽  
Additive Models ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, <i>P</i>=1´10<sup>-16</sup>) and a stronger effect in men than in women (interaction <i>P</i>=7´10<sup>-7</sup>). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby <i>PELO</i> gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

scholarly journals Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

2018 ◽  
Author(s):  
Urmo Võsa ◽  
Annique Claringbould ◽  
Harm-Jan Westra ◽  
Marc Jan Bonder ◽  
Patrick Deelen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Genome Wide ◽  
Polygenic Scores

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.To identify these effects, we performedcis-andtrans-expressionquantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.We observed thatcis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to usecis-eQTLs to pinpoint causal genes within susceptibility loci.In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

scholarly journals Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

2019 ◽  
Vol 20 (1) ◽  
pp. 461-493 ◽  
Author(s):  
Guy Sella ◽  
Nicholas H. Barton
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Practical Importance ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Heritable Variation ◽  
Genome Wide ◽  
Polygenic Adaptation

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

scholarly journals GWEHS: A Genome-Wide Effect Sizes and Heritability Screener

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 558
Author(s):  
López-Cortegano ◽  
Caballero
Keyword(s):  
Effect Size ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Online Application ◽  
Genome Wide ◽  
A Genome ◽  
Additive Contribution

During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations.

scholarly journals Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

2021 ◽  
Author(s):  
Mark J. O’Connor ◽  
Philip Schroeder ◽  
Alicia Huerta-Chagoya ◽  
Paula Cortés-Sánchez ◽  
Silvía Bonàs-Guarch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Low Frequency ◽  
Additive Models ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, <i>P</i>=1´10<sup>-16</sup>) and a stronger effect in men than in women (interaction <i>P</i>=7´10<sup>-7</sup>). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby <i>PELO</i> gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

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