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Genome-wide association studies in plants: the missing heritability is in the field
Benjamin Brachi, Geoffrey P Morris, Justin O Borevitz
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-10-232
Abstract: The genetic sources of phenotypic variation have been a major focus of both plant and animal studies aimed at identifying the causes of disease, improving agriculture and understanding adaptive processes. In plants, quantitative trait loci (QTL) were originally mapped in biparental crosses, but they were restricted in allelic diversity and in having limited genomic resolution [1]. The genome-wide association approach (GWAS) overcomes several limitations of traditional gene mapping by (i) providing higher resolution, often to the gene level, and (ii) using samples from previously well-studied populations in which commonly occurring genetic variations can be associated with phenotypic variation. The advent of high-density single-nucleotide polymorphism (SNP) typing allowed whole-genome scans to identify often small haplotype blocks that are significantly correlated with quantitative trait variation. These approaches have enabled both large studies of human disease, which have identified important loci [2], and recent plant studies that have been successful in identifying loci that explain large portions of phenotypic variation.Significant associations between genetic variations and phenotypic diversity have been found in some human studies, but they explain only a few percent of the phenotypic diversity, leading many geneticists to ask 'Where is the missing heritability?' [3,4]. This question has several possible answers. First, rare variants [3-5], major alleles that are unique to local families, can be detected only when sampling is adequate at the local level. Second, allelic heterogeneity, the phenomenon in which multiple functional alleles of the same gene exist and are associated with different phenotypes, is common, especially in wide population samples [6-8]. Third, single-marker approaches suffer from genetic heterogeneity when multiple major loci are involved and in linkage disequilibrium (LD) with each other [9]. Fourth, variation resulting from epistatic i
Genetic Dissection of the Drosophila melanogaster Female Head Transcriptome Reveals Widespread Allelic Heterogeneity  [PDF]
Elizabeth G. King ,Brian J. Sanderson,Casey L. McNeil,Anthony D. Long,Stuart J. Macdonald
PLOS Genetics , 2014, DOI: doi/10.1371/journal.pgen.1004322
Abstract: Modern genetic mapping is plagued by the “missing heritability” problem, which refers to the discordance between the estimated heritabilities of quantitative traits and the variance accounted for by mapped causative variants. One major potential explanation for the missing heritability is allelic heterogeneity, in which there are multiple causative variants at each causative gene with only a fraction having been identified. The majority of genome-wide association studies (GWAS) implicitly assume that a single SNP can explain all the variance for a causative locus. However, if allelic heterogeneity is prevalent, a substantial amount of genetic variance will remain unexplained. In this paper, we take a haplotype-based mapping approach and quantify the number of alleles segregating at each locus using a large set of 7922 eQTL contributing to regulatory variation in the Drosophila melanogaster female head. Not only does this study provide a comprehensive eQTL map for a major community genetic resource, the Drosophila Synthetic Population Resource, but it also provides a direct test of the allelic heterogeneity hypothesis. We find that 95% of cis-eQTLs and 78% of trans-eQTLs are due to multiple alleles, demonstrating that allelic heterogeneity is widespread in Drosophila eQTL. Allelic heterogeneity likely contributes significantly to the missing heritability problem common in GWAS studies.
Quantifying Missing Heritability at Known GWAS Loci  [PDF]
Alexander Gusev ,Gaurav Bhatia,Noah Zaitlen,Bjarni J. Vilhjalmsson,Dorothée Diogo,Eli A. Stahl,Peter K. Gregersen,Jane Worthington,Lars Klareskog,Soumya Raychaudhuri,Robert M. Plenge,Bogdan Pasaniuc,Alkes L. Price
PLOS Genetics , 2013, DOI: 10.1371/journal.pgen.1003993
Abstract: Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.
Heritability and Tissue Specificity of Expression Quantitative Trait Loci  [PDF]
Enrico Petretto ,Jonathan Mangion,Nicholas J Dickens,Stuart A Cook,Mande K Kumaran,Han Lu,Judith Fischer,Henrike Maatz,Vladimir Kren,Michal Pravenec,Norbert Hubner,Timothy J Aitman
PLOS Genetics , 2006, DOI: 10.1371/journal.pgen.0020172
Abstract: Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.
Mining the LIPG Allelic Spectrum Reveals the Contribution of Rare and Common Regulatory Variants to HDL Cholesterol  [PDF]
Sumeet A. Khetarpal equal contributor,Andrew C. Edmondson equal contributor,Avanthi Raghavan,Hemanth Neeli,Weijun Jin,Karen O. Badellino,Serkalem Demissie,Alisa K. Manning,Stephanie L. DerOhannessian,Megan L. Wolfe,L. Adrienne Cupples,Mingyao Li,Sekar Kathiresan,Daniel J. Rader
PLOS Genetics , 2011, DOI: 10.1371/journal.pgen.1002393
Abstract: Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5′ UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5′ UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.
The Multi-allelic Genetic Architecture of a Variance-Heterogeneity Locus for Molybdenum Concentration in Leaves Acts as a Source of Unexplained Additive Genetic Variance  [PDF]
Simon K. G. Forsberg?,Matthew E. Andreatta?,Xin-Yuan Huang?,John Danku?,David E. Salt?,?rjan Carlborg
PLOS Genetics , 2015, DOI: 10.1371/journal.pgen.1005648
Abstract: Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations.
Model-Based Clustering using multi-allelic loci data with loci selection  [PDF]
Wilson Toussile,Elisabeth Gassiat
Mathematics , 2008,
Abstract: We propose a Model-Based Clustering (MBC) method combined with loci selection using multi-allelic loci genetic data. The loci selection problem is regarded as a model selection problem and models in competition are compared with the Bayesian Information Criterion (BIC). The resulting procedure selects the subset of clustering loci, the number of clusters, estimates the proportion of each cluster and the allelic frequencies within each cluster. We prove that the selected model converges in probability to the true model under a single realistic assumption as the size of the sample tends to infinity. The proposed method named MixMoGenD (Mixture Model using Genetic Data) was implemented using c++ programming language. Numerical experiments on simulated data sets was conducted to highlight the interest of the proposed loci selection procedure.
Phenotypic Complexity, Measurement Bias, and Poor Phenotypic Resolution Contribute to the Missing Heritability Problem in Genetic Association Studies  [PDF]
Sophie van der Sluis,Matthijs Verhage,Danielle Posthuma,Conor V. Dolan
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0013929
Abstract: The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms.
Caucasian and Asian Specific Rheumatoid Arthritis Risk Loci Reveal Limited Replication and Apparent Allelic Heterogeneity in North Indians  [PDF]
Pushplata Prasad, Ashok Kumar, Rajiva Gupta, Ramesh C. Juyal, Thelma B. K.
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0031584
Abstract: Genome-wide association studies and meta-analysis indicate that several genes/loci are consistently associated with rheumatoid arthritis (RA) in European and Asian populations. To evaluate the transferability status of these findings to an ethnically diverse north Indian population, we performed a replication analysis. We investigated the association of 47 single-nucleotide polymorphisms (SNPs) at 43 of these genes/loci with RA in a north Indian cohort comprising 983 RA cases and 1007 age and gender matched controls. Genotyping was done using Infinium human 660w-quad. Association analysis by chi-square test implemented in plink was carried out in two steps. Firstly, association of the index or surrogate SNP (r2>0.8, calculated from reference GIH Hap-Map population) was tested. In the second step, evidence for allelic/locus heterogeneity at aforementioned genes/loci was assessed for by testing additional flanking SNPs in linkage equilibrium with index/surrogate marker. Of the 44 European specific index SNPs, neither index nor surrogate SNPs were present for nine SNPs in the genotyping array. Of the remaining 35, associations were replicated at seven genes namely PTPN22 (rs1217407, p = 3×10?3); IL2–21 (rs13119723, p = 0.008); HLA-DRB1 (rs660895, p = 2.56×10?5; rs6457617, p = 1.6×10?09; rs13192471, p = 6.7×10?16); TNFA1P3 (rs9321637, p = 0.03); CCL21 (rs13293020, p = 0.01); IL2RA (rs2104286, p = 1.9×10?4) and ZEB1 (rs2793108, p = 0.006). Of the three Asian specific loci tested, rs2977227 in PADI4 showed modest association (p<0.02). Further, of the 140 SNPs (in LE with index/surrogate variant) tested, association was observed at 11 additional genes: PTPRC, AFF3, CD28, CTLA4, PXK, ANKRD55, TAGAP, CCR6, BLK, CD40 and IL2RB. This study indicates limited replication of European and Asian index SNPs and apparent allelic heterogeneity in RA etiology among north Indians warranting independent GWAS in this population. However, replicated associations of HLA-DRB1, PTPN22 (which confer ~50% of the heritable risk to RA) and IL2RA suggest that cross-ethnicity fine mapping of such loci is apposite for identification of causal variants.
Finding the sources of missing heritability in a yeast cross  [PDF]
Joshua S. Bloom,Ian M. Ehrenreich,Wesley Loo,Thúy-Lan V? Lite,Leonid Kruglyak
Quantitative Biology , 2012, DOI: 10.1038/nature11867
Abstract: For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this "missing heritability" have been proposed. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to 50%. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits.
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