Quantifying Missing Heritability at Known GWAS Loci

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.     

A Genome-Wide Association Study of Neuroticism in a Population-Based Sample

Neuroticism is a moderately heritable personality trait considered to be a risk factor for developing major depression, anxiety disorders and dementia. We performed a genome-wide association study in 2,235 participants drawn from a population-based study of neuroticism, making this the largest association study for neuroticism to date. Neuroticism was measured by the Eysenck Personality Questionnaire. After Quality Control, we analysed 430,000 autosomal SNPs together with an additional 1.2 million SNPs imputed with high quality from the Hap Map CEU samples. We found a very small effect of population stratification, corrected using one principal component, and some cryptic kinship that required no correction. NKAIN2 showed suggestive evidence of association with neuroticism as a main effect (p<10⁻⁶) and GPC6 showed suggestive evidence for interaction with age (p≈10⁻⁷). We found support for one previously-reported association (PDE4D), but failed to replicate other recent reports. These results suggest common SNP variation does not strongly influence neuroticism. Our study was powered to detect almost all SNPs explaining at least 2% of heritability, and so our results effectively exclude the existence of loci having a major effect on neuroticism.     

Identification, replication, and fine-mapping of Loci associated with adult height in individuals of african ancestry

Adult height is a classic polygenic trait of high heritability (h ² ∼0.8). More than 180 single nucleotide polymorphisms (SNPs), identified mostly in populations of European descent, are associated with height. These variants convey modest effects and explain ∼10% of the variance in height. Discovery efforts in other populations, while limited, have revealed loci for height not previously implicated in individuals of European ancestry. Here, we performed a meta-analysis of genome-wide association (GWA) results for adult height in 20,427 individuals of African ancestry with replication in up to 16,436 African Americans. We found two novel height loci (Xp22-rs12393627, P = 3.4×10⁻¹² and 2p14-rs4315565, P = 1.2×10⁻⁸). As a group, height associations discovered in European-ancestry samples replicate in individuals of African ancestry (P = 1.7×10⁻⁴ for overall replication). Fine-mapping of the European height loci in African-ancestry individuals showed an enrichment of SNPs that are associated with expression of nearby genes when compared to the index European height SNPs (P<0.01). Our results highlight the utility of genetic studies in non-European populations to understand the etiology of complex human diseases and traits.     

Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-Wide and Candidate Gene-Based Association Mapping Approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r²; when r²<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.     

Conditions for the validity of SNP-based heritability estimation

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 ( \(h_{\text{SNP}}^{2}\) ). 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 \(h_{\text{SNP}}^{2}\) under circumstances wider than those under which it has so far been derived.     

Ubiquitous Polygenicity of Human Complex Traits: Genome-Wide Analysis of 49 Traits in Koreans

Recent studies in population of European ancestry have shown that 30%∼50% of heritability for human complex traits such as height and body mass index, and common diseases such as schizophrenia and rheumatoid arthritis, can be captured by common SNPs and that genetic variation attributed to chromosomes are in proportion to their length. Using genome-wide estimation and partitioning approaches, we analysed 49 human quantitative traits, many of which are relevant to human diseases, in 7,170 unrelated Korean individuals genotyped on 326,262 SNPs. For 43 of the 49 traits, we estimated a nominally significant (P<0.05) proportion of variance explained by all SNPs on the Affymetrix 5.0 genotyping array (). On average across 47 of the 49 traits for which the estimate of is non-zero, common SNPs explain approximately one-third (range of 7.8% to 76.8%) of narrow sense heritability.The estimate of is highly correlated with the proportion of SNPs with association P<0.031 (r ² = 0.92). Longer genomic segments tend to explain more phenotypic variation, with a correlation of 0.78 between the estimate of variance explained by individual chromosomes and their physical length, and 1% of the genome explains approximately 1% of the genetic variance. Despite the fact that there are a few SNPs with large effects for some traits, these results suggest that polygenicity is ubiquitous for most human complex traits and that a substantial proportion of the “missing heritability” is captured by common SNPs.     

The Molecular Genetic Architecture of Self-Employment

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable–entrepreneurship–that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σ_g ²/σ_P ² = 25%, h ² = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10⁻⁵ were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.     

Evaluating GWAS-Identified SNPs for Age at Natural Menopause among Chinese Women

Background

Age at natural menopause (ANM) is a complex trait with high heritability and is associated with several major hormonal-related diseases. Recently, several genome-wide association studies (GWAS), conducted exclusively among women of European ancestry, have discovered dozens of genetic loci influencing ANM. No study has been conducted to evaluate whether these findings can be generalized to Chinese women.

Methodology/Principal Findings

We evaluated the index single nucleotide polymorphisms (SNPs) in 19 GWAS-identified genetic susceptibility loci for ANM among 3,533 Chinese women who had natural menopause. We also investigated 3 additional SNPs which were in LD with the index SNP in European-ancestry but not in Asian-ancestry populations. Two genetic risk scores (GRS) were calculated to summarize SNPs across multiple loci one for all SNPs tested (GRSall), and one for SNPs which showed association in our study (GRSsel). All 22 SNPs showed the same association direction as previously reported. Eight SNPs were nominally statistically significant with P≤0.05: rs4246511 (RHBDL2), rs12461110 (NLRP11), rs2307449 (POLG), rs12611091 (BRSK1), rs1172822 (BRSK1), rs365132 (UIMC1), rs2720044 (ASH2L), and rs7246479 (TMEM150B). Especially, SNPs rs4246511, rs365132, rs1172822, and rs7246479 remained significant even after Bonferroni correction. Significant associations were observed for GRS. Women in the highest quartile began menopause 0.7 years (P = 3.24×10⁻⁹) and 0.9 years (P = 4.61×10⁻¹¹) later than those in the lowest quartile for GRSsel and GRSall, respectively.

Conclusions

Among the 22 investigated SNPs, eight showed associations with ANM (P<0.05) in our Chinese population. Results from this study extend some recent GWAS findings to the Asian-ancestry population and may guide future efforts to identify genetic determination of menopause.     

Extent of linkage disequilibrium in large-breed dogs: chromosomal and breed variation

The aim of this study was to better define the extent of linkage disequilibrium (LD) in populations of large-breed dogs and its variation by breed and chromosomal region. Understanding the extent of LD is a crucial component for successful utilization of genome-wide association studies and allows researchers to better define regions of interest and target candidate genes. Twenty-four Golden Retriever dogs, 28 Rottweiler dogs, and 24 Newfoundland dogs were genotyped for single-nucleotide polymorphism (SNP) data using a high-density SNP array. LD was calculated for all autosomes using Haploview. Decay of the squared correlation coefficient (r ²) was plotted on a per-breed and per-chromosome basis as well as in a genome-wide fashion. The point of 50 % decay of r ² was used to estimate the difference in extent of LD between breeds. Extent of LD was significantly shorter for Newfoundland dogs based upon 50 % decay of r ² data at a mean of 344 kb compared to Golden Retriever and Rottweiler dogs at 715 and 834 kb, respectively (P < 0.0001). Notable differences in LD by chromosome were present within each breed and not strictly related to the length of the corresponding chromosome. Extent of LD is breed and chromosome dependent. To our knowledge, this is the first report of SNP-based LD for Newfoundland dogs, the first report based on genome-wide SNPs for Rottweilers, and an almost tenfold improvement in marker density over previous genome-wide studies of LD in Golden Retrievers.     

Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg2) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg2 from imputed SNPs (5.1× enrichment; p = 3.7 × 10⁻¹⁷) and 38% (SE = 4%) of hg2 from genotyped SNPs (1.6× enrichment, p = 1.0 × 10⁻⁴). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg2 despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.     

Estimating FST and kinship for arbitrary population structures

F_ST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of F_ST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of F_ST to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing F_ST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and F_ST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and F_ST estimates.     

Genome-Wide Association Study of Parity in Bangladeshi Women

Human fertility is a complex trait determined by gene-environment interactions in which genetic factors represent a significant component. To better understand inter-individual variability in fertility, we performed one of the first genome-wide association studies (GWAS) of common fertility phenotypes, lifetime number of pregnancies and number of children in a developing country population. The fertility phenotype data and DNA samples were obtained at baseline recruitment from individuals participating in a large prospective cohort study in Bangladesh. GWAS analyses of fertility phenotypes were conducted among 1,686 married women. One SNP on chromosome 4 was non-significantly associated with number of children at P <10^-7 and number of pregnancies at P <10^-6. This SNP is located in a region without a gene within 1 Mb. One SNP on chromosome 6 was non-significantly associated with extreme number of children at P <10^-6. The closest gene to this SNP is HDGFL1, a hepatoma-derived growth factor. When we excluded hormonal contraceptive users, a SNP on chromosome 5 was non-significantly associated at P <10^-5 for number of children and number of pregnancies. This SNP is located near C5orf64, an open reading frame, and ZSWIM6, a zinc ion binding gene. We also estimated the heritability of these phenotypes from our genotype data using GCTA (Genome-wide Complex Trait Analysis) for number of children (h_g ² = 0.149, SE = 0.24, p-value = 0.265) and number of pregnancies (h_g ² = 0.007, SE = 0.22, p-value = 0.487). Our genome-wide association study and heritability estimates of number of pregnancies and number of children in Bangladesh did not confer strong evidence of common variants for parity variation. However, our results suggest that future studies may want to consider the role of 3 notable SNPs in their analysis.     

Connecting the Dots: Potential of Data Integration to Identify Regulatory SNPs in Late-Onset Alzheimer's Disease GWAS Findings

Late-onset Alzheimer''s disease (LOAD) is a multifactorial disorder with over twenty loci associated with disease risk. Given the number of genome-wide significant variants that fall outside of coding regions, it is possible that some of these variants alter some function of gene expression rather than tagging coding variants that alter protein structure and/or function. RegulomeDB is a database that annotates regulatory functions of genetic variants. In this study, we utilized RegulomeDB to investigate potential regulatory functions of lead single nucleotide polymorphisms (SNPs) identified in five genome-wide association studies (GWAS) of risk and age-at onset (AAO) of LOAD, as well as SNPs in LD (r²≥0.80) with the lead GWAS SNPs. Of a total 614 SNPs examined, 394 returned RegulomeDB scores of 1–6. Of those 394 variants, 34 showed strong evidence of regulatory function (RegulomeDB score <3), and only 3 of them were genome-wide significant SNPs (ZCWPW1/rs1476679, CLU/rs1532278 and ABCA7/rs3764650). This study further supports the assumption that some of the non-coding GWAS SNPs are true associations rather than tagged associations and demonstrates the application of RegulomeDB to GWAS data.     

FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium

Background  

Human genome contains millions of common single nucleotide polymorphisms (SNPs) and these SNPs play an important role in understanding the association between genetic variations and human diseases. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), thus it is not necessary to genotype all SNPs for association study. Many algorithms have been developed to find a small subset of SNPs called tag SNPs that are sufficient to infer all the other SNPs. Algorithms based on the r ² LD statistic have gained popularity because r ² is directly related to statistical power to detect disease associations. Most of existing r ² based algorithms use pairwise LD. Recent studies show that multi-marker LD can help further reduce the number of tag SNPs. However, existing tag SNP selection algorithms based on multi-marker LD are both time-consuming and memory-consuming. They cannot work on chromosomes containing more than 100 k SNPs using length-3 tagging rules.     

Effects of spatial autocorrelation,natal philopatry and phenotypic plasticity on the heritability of laying date

We investigated the effect of spatial autocorrelation on heritability (h²) estimates of laying date and clutch size in a population of great tits Parus major. We found that h² of laying date, but not clutch size, declined significantly with increasing distance between the nestbox of mothers and daughters. This decline was caused by a decreasing effect of spatial autocorrelation in laying date, rather than by the existence of genotype–environment interactions (GEI). After correcting for the effect of spatial autocorrelation, h² of laying date was low (0.16 ± 0.07), but significant, and surprisingly consistent with increasing distance between parental and offspring environments. The h² of clutch size was not much affected by spatial autocorrelation. Most previously published estimates of the heritability of laying date include various degrees of common environment effects, which can bias estimates both upwards and downwards. We suggest that using techniques that take spatial autocorrelation into account might be a fruitful approach to estimate h² of traits that show a high degree of plasticity.     

Use of Genome-wide Heterospecific Single-Nucleotide Polymorphisms to Estimate Linkage Disequilibrium in Rhesus and Cynomolgus Macaques

Rhesus and cynomolgus macaques are frequently used in biomedical research, and the availability of their reference genomes now provides for their use in genome-wide association studies. However, little is known about linkage disequilibrium (LD) in their genomes, which can affect the design and success of such studies. Here we studied LD by using 1781 conserved single-nucleotide polymorphisms (SNPs) in 183 rhesus macaques (Macaca mulatta), including 97 purebred Chinese and 86 purebred Indian animals, and 96 cynomolgus macaques (M. fascicularis fascicularis). Correlation between loci pairs decayed to 0.02 at 1146.83, 2197.92, and 3955.83 kb for Chinese rhesus, Indian rhesus, and cynomolgus macaques, respectively. Differences between the observed heterozygosity and minor allele frequency (MAF) of pairs of these 3 taxa were highly statistically significant. These 3 nonhuman primate taxa have significantly different genetic diversities (heterozygosity and MAF) and rates of LD decay. Our study confirms a much lower rate of LD decay in Indian than in Chinese rhesus macaques relative to that previously reported. In contrast, the especially low rate of LD decay in cynomolgus macaques suggests the particular usefulness of this species in genome-wide association studies. Although conserved markers, such as those used here, are required for valid LD comparisons among taxa, LD can be assessed with less bias by using species-specific markers, because conserved SNPs may be ancestral and therefore not informative for LD.Abbreviations: GWAS, genome-wide association study; LD, linkage disequilibrium; MAF, minor allele frequencyContributing to the widespread use of nonhuman primates in biomedical research, captive-breeding programs such as those of the National Primate Research Center system in the United States were established initially by using animals imported from Asia. The 2 most commonly used primates are rhesus macaques (Macaca mulatta) and long-tailed or cynomolgus macaques (M. fascicularis fascicularis).After humans, rhesus macaques are the most widely distributed primate species.^37,38 This species is found throughout mainland Asia, ranging from Afghanistan to India and eastward through Thailand and southern China to the Yellow Sea.^31,34 In addition to their significant morphological differences,⁹ rhesus macaques of Indian and Chinese origins have been demonstrated to exhibit significant phenotypic differences that are directly relevant to their use as biomedical models in experimental studies.^2,23,42 Cynomolgus macaques are found south of the subtropical and temperate geographic distributions of rhesus macaques, in the south and southeast Indo-Malayan regions.^8,10The 2 species share a common ancestor that lived 1 to 2 million years ago.^3,13,25 This ancestral population of rhesus macaques diverged from a fascicularis-like ancestor shared in common with both rhesus and cynomolgus macaques after cynomolgus macaques expanded from their homeland in Indonesia.³⁶ For this reason, genetic markers present in Indian rhesus macaques are either highly derived or are conserved as ancestral markers shared with Chinese rhesus macaques. The interspecific boundaries of rhesus and cynomolgus macaques are delineated by a narrow zone of parapatry in northern Indochina,^7,8,10 within which male-biased gene flow^37,39 and relatively high, but highly variable, levels of introgression of genes³² have occurred from rhesus to cynomolgus macaque groups.^37,39 Because cynomolgus macaques originated in Indonesia³⁶ and because rhesus macaques probably diverged from cynomolgus macaques in southwestern China,¹¹ genetic markers shared between Indonesian cynomolgus macaques and Chinese rhesus macaques comprise a unique set of markers that are conserved in both macaque species.The wide assortment of morphometric differences^8,9 and the broad geographic distribution of these 2 macaque species foster an expectation of high genetic diversity within and between them that could be exploited for mapping genes responsible for phenotypic differences between taxa. A better understanding of linkage disequilibrium (LD) in these nonhuman primate species can lead to a more informed selection of study subjects for, and more efficient conduct of, genome-wide association studies (GWAS) of particular diseases that macaques share in common with humans. LD is the nonrandom association of alleles at 2 or more adjacent loci that descend from single, ancestral chromosomes.²⁹ LD plays a critical role in gene mapping, both as a tool for fine mapping of complex disease genes and in GWAS-based approaches. GWAS facilitate the identification of genes associated with complex and common traits or diseases by examining LD estimates among large numbers of common genetic variants, typically single-nucleotide polymorphisms (SNPs), between pairs of different groups of subjects to determine whether any variant is associated with a trait or disease of interest. LD data make tightly linked variants strongly correlated to produce successful association studies. For instance, LD reduces the number of markers and sample size of study subjects required to map genes influencing phenotypes to the genome because markers in LD are linked and inherited together.¹³ In addition, differences in LD can be used to identify orthologs for detecting the signatures of selective sweeps,²¹ as defined by d_N/d_S ratios obtained through the McDonald–Kreitman neutrality test.²⁴ Furthermore, LD assessments can provide a more complete understanding of genome structure by defining the boundaries of haplotype blocks, within which recombination is rare or absent and which are separated by recombination ‘hotspots,’ in genomes.⁴³Evidence from a study based on 1476 SNPs identified in ENCODE regions of the Indian rhesus macaque genome¹³ indicated that the rate of LD decay is higher in Chinese than in Indian rhesus macaques due to an hypothesized genetic bottleneck experienced by Indian rhesus macaques after diverging from the eastern subspecies, and, therefore, that Indian rhesus macaques, having higher LD, may be more useful for GWAS than Chinese rhesus macaques. In that study,¹³ only 33% of the SNPs were shared in common between the 2 subspecies, with Chinese rhesus macaques contributing to more than 60% of the remaining rhesus SNPs. Conversely, another study⁴¹ reported a slower rate of decay of LD in 25 Chinese than in 25 Indian rhesus macaques on the basis of 4040 SNPs, only 2% of which fell in coding regions, but 68% of those SNPs were shared between the 2 subspecies, with Indian rhesus macaques contributing almost 60% of the remaining SNPs. The marked disparity between the 2 studies in the proportions of shared SNPs used, the subspecies with the most genetic diversity, the sample size of Chinese rhesus macaques, the proportions of SNPs located in or near coding regions that are subject to functional constraints, and the greater disparity in LD decay between the 2 subspecies of rhesus macaques might reflect biases in either or both studies. For example, the use of markers whose frequencies are uncharacteristically low in one subspecies relative to the other can underestimate the rate of LD decay because lower frequency alleles, on average, are younger and have experienced less time for recombination.²⁶ To avoid the influence of such ascertainment biases, comparisons of LD between 2 taxa should involve only SNPs conserved in both taxa. Moreover, because 2 points do not provide a phylogenetic or cladistic analysis to assign specific SNPs to origin on one phylogenetic line or another, comparing just the Indian and Chinese rhesus macaques without an additional primate taxon makes it is difficult to establish polarity and distinguish between derived and conserved SNPs. This limitation likely led to the contradictory conclusions of the 2 previously cited studies^13,41 regarding the rate of LD decay in Chinese and Indian rhesus macaques.Because rhesus and cynomolgus macaques share a common fascicularis-like ancestor, a comparison of heterospecific SNPs among cynomolgus, Indian rhesus, and Chinese rhesus macaques would likely be fundamental to inferences regarding genome-wide LD estimates. The objective of the present study was to evaluate the conclusions of previous studies^13,41 by using our panel of 1781 autosomal SNPs that are conserved in both rhesus and cynomolgus macaques to estimate the rates at which genome-wide LD decays in Indian and Chinese rhesus macaques and cynomolgus macaques, the species ancestral to rhesus macaques, and to evaluate the suitability of these populations for GWAS.     

Single Nucleotide Polymorphisms with Cis-Regulatory Effects on Long Non-Coding Transcripts in Human Primary Monocytes

We applied genome-wide allele-specific expression analysis of monocytes from 188 samples. Monocytes were purified from white blood cells of healthy blood donors to detect cis-acting genetic variation that regulates the expression of long non-coding RNAs. We analysed 8929 regions harboring genes for potential long non-coding RNA that were retrieved from data from the ENCODE project. Of these regions, 60% were annotated as intergenic, which implies that they do not overlap with protein-coding genes. Focusing on the intergenic regions, and using stringent analysis of the allele-specific expression data, we detected robust cis-regulatory SNPs in 258 out of 489 informative intergenic regions included in the analysis. The cis-regulatory SNPs that were significantly associated with allele-specific expression of long non-coding RNAs were enriched to enhancer regions marked for active or bivalent, poised chromatin by histone modifications. Out of the lncRNA regions regulated by cis-acting regulatory SNPs, 20% (n = 52) were co-regulated with the closest protein coding gene. We compared the identified cis-regulatory SNPs with those in the catalog of SNPs identified by genome-wide association studies of human diseases and traits. This comparison identified 32 SNPs in loci from genome-wide association studies that displayed a strong association signal with allele-specific expression of non-coding RNAs in monocytes, with p-values ranging from 6.7×10⁻⁷ to 9.5×10⁻⁸⁹. The identified cis-regulatory SNPs are associated with diseases of the immune system, like multiple sclerosis and rheumatoid arthritis.     

Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits

Very few studies have investigated the associations between genetic polymorphisms and gene expression on the X-chromosome. This is a major bottleneck when conducting functional follow-up studies of trait-associated variants, as those identified in genome-wide association studies (GWAS). We used a multivariate approach to test the association between individual single nucleotide polymorphisms (SNPs) and exon expression levels measured in 356 Epstein–Barr virus-transformed lymphoblastoid cell lines (LCLs) from the Geuvadis RNA sequencing project to identify SNPs associated with variation in gene expression on the X-chromosome, which we refer to as eSNPs. At an FDR of 5 %, we discovered 548 independent [linkage disequilibrium (LD) r ² < 0.1] eSNPs on the X-chromosome. Of these, 35 were in LD (r ² > 0.8) with previously published disease- or trait-associated variants identified through GWAS. One of the strongest eSNPs identified was rs35975601, which was associated with F8A1 expression (p value = 3 × 10^?20) and was in LD with a type 1 diabetes risk variant. Additionally, we identified a number of genes for which eSNPs were in LD with multiple diseases or traits, including DNASE1L1 which was mapped to bilirubin levels, type 1 diabetes and schizophrenia. Our results also indicate that multivariate exon-level analysis provides a more powerful approach than univariate gene-level analysis, particularly when SNPs influence the expression of different exons with different magnitude and/or direction of effect. The associations identified in our study may provide new insights into the molecular process by which gene expression may contribute to trait variation or disease risk in humans.