Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability

2011 ◽  
Vol 35 (5) ◽  
pp. 341-349 ◽  
Author(s):  
Zoltán Kutalik ◽  
John Whittaker ◽  
Dawn Waterworth ◽  
Jacques S. Beckmann ◽  
Sven Bergmann ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Association Studies ◽  
Large Fraction ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Genome Wide ◽  
Novel Method ◽  
Variation Explained

scholarly journals Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritis

2020 ◽  
Vol 79 (11) ◽  
pp. 1438-1445
Author(s):  
Young-Chang Kwon ◽  
Jiwoo Lim ◽  
So-Young Bang ◽  
Eunji Ha ◽  
Mi Yeong Hwang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Large Fraction ◽  
Genome Wide Association ◽  
Chromatin Interaction ◽  
Genome Wide Association Studies ◽  
Bioinformatics Analyses ◽  
Specific Expression ◽  
Genome Wide

ObjectiveGenome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case–control population.MethodsWe newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.ResultsWe identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10−8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.ConclusionThis study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.

scholarly journals A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based MDR

2012 ◽  
Vol 13 (Suppl 9) ◽  
pp. S5 ◽  
Author(s):  
Sohee Oh ◽  
Jaehoon Lee ◽  
Min-Seok Kwon ◽  
Bruce Weir ◽  
Kyooseob Ha ◽  
...  
Keyword(s):  
Association Studies ◽  
High Order ◽  
Genome Wide Association ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Novel Method

scholarly journals Estimating Missing Heritability for Disease from Genome-wide Association Studies

2011 ◽  
Vol 88 (3) ◽  
pp. 294-305 ◽  
Author(s):  
Sang Hong Lee ◽  
Naomi R. Wray ◽  
Michael E. Goddard ◽  
Peter M. Visscher
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Genome Wide

scholarly journals hogwash: Three Methods for Genome-Wide Association Studies in Bacteria

2020 ◽  
Author(s):  
Katie Saund ◽  
Evan S Snitkin
Keyword(s):  
Phenotypic Variation ◽  
Phylogenetic Signal ◽  
Association Studies ◽  
Bacterial Genome ◽  
Simulated Data ◽  
R Package ◽  
Genomic Variation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene- or pathway-analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

scholarly journals Conditions for the validity of SNP-based heritability estimation

2014 ◽  
Author(s):  
James J Lee ◽  
Carson C Chow
Keyword(s):  
Lower Bound ◽  
Association Studies ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Population Variance ◽  
Genome Wide ◽  
Heritability Estimation ◽  
Novel Method ◽  
The Given

The heritability of a trait ($h^2$) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs ($h^2_\textrm{SNP}$). Thus far the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining $h^2_\textrm{SNP}$ under circumstances wider than those under which it has so far been derived.

scholarly journals Hogwash: three methods for genome-wide association studies in bacteria

2020 ◽  
Vol 6 (11) ◽  
Author(s):  
Katie Saund ◽  
Evan S. Snitkin
Keyword(s):  
Phenotypic Variation ◽  
Phylogenetic Signal ◽  
Association Studies ◽  
Bacterial Genome ◽  
Simulated Data ◽  
R Package ◽  
Genomic Variation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

scholarly journals Multi-Locus Genome-Wide Association Study of Four Yield-Related Traits in Chinese Wheat Landraces

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu Lin ◽  
Kunyu Zhou ◽  
Haiyan Hu ◽  
Xiaojun Jiang ◽  
Shifan Yu ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Kernel Weight ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Wheat Landraces ◽  
Variation Explained ◽  
Chinese Wheat

Wheat (Triticum aestivum L.) is one of the most important crops in the world. Here, four yield-related traits, namely, spike length, spikelets number, tillers number, and thousand-kernel weight, were evaluated in 272 Chinese wheat landraces in multiple environments. Five multi-locus genome-wide association studies (FASTmrEMMA, ISIS EN-BLASSO, mrMLM, pKWmEB, and pLARmEB) were performed using 172,711 single-nucleotide polymorphisms (SNPs) to identify yield-related quantitative trait loci (QTL). A total of 27 robust QTL were identified by more than three models. Nine of these QTL were consistent with those in previous studies. The remaining 18 QTL may be novel. We identified a major QTL, QTkw.sicau-4B, with up to 18.78% of phenotypic variation explained. The developed kompetitive allele-specific polymerase chain reaction marker for QTkw.sicau-4B was validated in two recombinant inbred line populations with an average phenotypic difference of 16.07%. After combined homologous function annotation and expression analysis, TraesCS4B01G272300 was the most likely candidate gene for QTkw.sicau-4B. Our findings provide new insights into the genetic basis of yield-related traits and offer valuable QTL to breed wheat cultivars via marker-assisted selection.

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