scholarly journals Multi-ethnic genome-wide association study of decomposed cardioelectric phenotypes illustrates strategies to identify and characterize evidence of shared genetic effects for complex traits

2019 ◽  
Author(s):  
Antoine R. Baldassari ◽  
Colleen M. Sitlani ◽  
Heather M. Highland ◽  
Dan E. Arking ◽  
Steve Buyske ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Genome Wide ◽  
Trait Loci

ABSTRACTBackgroundPublished genome-wide association studies (GWAS) are mainly European-centric, examine a narrow view of phenotypic variation, and infrequently interrogate genetic effects shared across traits. We therefore examined the extent to which a multi-ethnic, combined trait GWAS of phenotypes that map to well-defined biology can enable detection and characterization of complex trait loci.MethodsWith 1000 Genomes Phase 3 imputed data in 34,668 participants (15% African American; 3% Chinese American; 51% European American; 30% Hispanic/Latino), we performed covariate-adjusted univariate GWAS of six contiguous electrocardiogram (ECG) traits that decomposed an average heartbeat and two commonly reported composite ECG traits that summed contiguous traits. Combined phenotype testing was performed using the adaptive sum of powered scores test (aSPU).ResultsWe identified six novel and 87 known ECG trait loci (aSPU p-value < 5E-9). Lead SNP rs3211938 at novel locus CD36 was common in African Americans (minor allele frequency=10%) and near-monomorphic in European Americans, with effect sizes for the composite trait, QT interval, among the largest reported. Only one novel locus was detected for the composite traits, due to opposite directions of effects across contiguous traits that summed to near-zero. Combined phenotype testing did not detect novel loci unapparent by univariate testing. However, this approach aided locus characterization, particularly when loci harbored multiple independent signals that differed by trait.ConclusionsDespite including one-third as few participants as the largest published GWAS of ECG traits, our study identifies multiple novel ECG genetic loci, emphasizing the importance of ancestral diversity and phenotype measurement in this era of ever-growing GWAS.AUTHOR SUMMARYWe leveraged a multiethnic cohort with precise measures of cardioelectric function to identify novel genetic loci affecting this complex, multifaceted phenotype. The success of our approach stresses the importance of phenotypic precision and participant diversity for future locus discovery and characterization efforts, and cautions against compromises made in genome-wide association studies to pursue ever-growing sample sizes.

scholarly journals Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Josine L. Min ◽  
Rebecca C. Richmond ◽  
Ake T. Lu ◽  
Maria K. Sobczyk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Biological Aging ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Biomarkers Of Aging ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

scholarly journals Genome-Wide Association Study Identifies Loci Associated with Sensitive Skin

Cosmetics ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 49
Author(s):  
Miranda A. Farage ◽  
Yunxuan Jiang ◽  
Jay P. Tiesman ◽  
Pierre Fontanillas ◽  
Rosemarie Osborne
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
European Ancestry ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Online Questionnaire ◽  
Sensitive Skin ◽  
Genome Wide ◽  
Skin Conditions

Individuals suffering from sensitive skin often have other skin conditions and/or diseases, such as fair skin, freckles, rosacea, or atopic dermatitis. Genome-wide association studies (GWAS) have been performed for some of these conditions, but not for sensitive skin. In this study, a total of 23,426 unrelated participants of European ancestry from the 23andMe database were evaluated for self-declared sensitive skin, other skin conditions, and diseases using an online questionnaire format. Responders were separated into two groups: those who declared they had sensitive skin (n = 8971) and those who declared their skin was not sensitive (controls, n = 14,455). A GWAS of sensitive skin individuals identified three genome-wide significance loci (p-value < 5 × 10−8) and seven suggestive loci (p-value < 1 × 10−6). Of the three most significant loci, all have been associated with pigmentation and two have been associated with acne.

scholarly journals Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model

2017 ◽  
Author(s):  
Haohan Wang ◽  
Xiang Liu ◽  
Yunpeng Xiao ◽  
Ming Xu ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Association Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Genetic Loci ◽  
Genome Wide

AbstractGenome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

scholarly journals Genome-Wide Association Studies of CKD and Related Traits

2020 ◽  
Vol 15 (11) ◽  
pp. 1643-1656
Author(s):  
Adrienne Tin ◽  
Anna Köttgen
Keyword(s):  
Kidney Function ◽  
Complex Traits ◽  
Kidney Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Genome Wide ◽  
Causal Genes

The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function–related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function–related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function–related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.

scholarly journals Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Declan Bennett ◽  
Donal O’Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

AbstractOngoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

scholarly journals Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

2020 ◽  
Author(s):  
Siwen Deng ◽  
Daniel Caddell ◽  
Jinliang Yang ◽  
Lindsay Dahlen ◽  
Lorenzo Washington ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Population Level ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Host Genotype ◽  
Rhizosphere Microbiome ◽  
Genome Wide ◽  
Sorghum Genotypes

AbstractHost genetics has recently been shown to be a driver of plant microbiome composition. However, identifying the underlying genetic loci controlling microbial selection remains challenging. Genome wide association studies (GWAS) represent a potentially powerful, unbiased method to identify microbes sensitive to host genotype, and to connect them with the genetic loci that influence their colonization. Here, we conducted a population-level microbiome analysis of the rhizospheres of 200 sorghum genotypes. Using 16S rRNA amplicon sequencing, we identify rhizosphere-associated bacteria exhibiting heritable associations with plant genotype, and identify significant overlap between these lineages and heritable taxa recently identified in maize. Furthermore, we demonstrate that GWAS can identify host loci that correlate with the abundance of specific subsets of the rhizosphere microbiome. Finally, we demonstrate that these results can be used to predict rhizosphere microbiome structure for an independent panel of sorghum genotypes based solely on knowledge of host genotypic information.

scholarly journals Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

2021 ◽  
Author(s):  
Siwen Deng ◽  
Daniel F. Caddell ◽  
Gen Xu ◽  
Lindsay Dahlen ◽  
Lorenzo Washington ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Population Level ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Host Genotype ◽  
Rhizosphere Microbiome ◽  
Genome Wide ◽  
Sorghum Genotypes

AbstractHost genetics has recently been shown to be a driver of plant microbiome composition. However, identifying the underlying genetic loci controlling microbial selection remains challenging. Genome-wide association studies (GWAS) represent a potentially powerful, unbiased method to identify microbes sensitive to the host genotype and to connect them with the genetic loci that influence their colonization. Here, we conducted a population-level microbiome analysis of the rhizospheres of 200 sorghum genotypes. Using 16S rRNA amplicon sequencing, we identify rhizosphere-associated bacteria exhibiting heritable associations with plant genotype, and identify significant overlap between these lineages and heritable taxa recently identified in maize. Furthermore, we demonstrate that GWAS can identify host loci that correlate with the abundance of specific subsets of the rhizosphere microbiome. Finally, we demonstrate that these results can be used to predict rhizosphere microbiome structure for an independent panel of sorghum genotypes based solely on knowledge of host genotypic information.

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 Performing post-genome-wide association study analysis: overview, challenges and recommendations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1002
Author(s):  
Yagoub Adam ◽  
Chaimae Samtal ◽  
Jean-tristan Brandenburg ◽  
Oluwadamilare Falola ◽  
Ezekiel Adebiyi
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Single Phenotype ◽  
Insight Into

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

scholarly journals Controlling for Background Genetic Effects Using Polygenic Scores Improves the Power of Genome-wide Association Studies

2021 ◽  
Author(s):  
Declan Bennett ◽  
Dónal O'Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

Abstract Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

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