BRASS: Permutation methods for binary traits in genetic association studies with structured samples.

In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises, e.g, in tests of gene-set, pathway or genome-wide significance, or when the statistic is formed by machine learning or data adaptive methods. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and epistasis detection among many others. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary traits, for use in association mapping in structured samples. In addition to modeling structure in the sample, BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. In simulation studies, we compare BRASS to other permutation and resampling-based methods in a range of scenarios that include population structure, familial relatedness, ascertainment and phenotype model misspecification. In these settings, we demonstrate the superior control of type 1 error by BRASS compared to the other 6 methods considered. We apply BRASS to assess genome-wide significance for association analyses in domestic dog for elbow dysplasia (ED) and idiopathic epilepsy (IE). For both traits we detect previously identified associations, and in addition, for ED, we detect significant association with a SNP on chromosome 35 that was not detected by previous analyses, demonstrating the potential of the method.

Application of Equal Local Levels to Improve Q-Q Plot Testing Bands with R Package qqconf.

Quantile-Quantile (Q-Q) plots are often difficult to interpret because it is unclear how large the deviation from the theoretical distribution must be to indicate a lack of fit. Most Q-Q plots could benefit from the addition of meaningful global testing bands, but the use of such bands unfortunately remains rare because of the drawbacks of current approaches and packages. These drawbacks include incorrect global Type I error rate, lack of power to detect deviations in the tails of the distribution, relatively slow computation for large data sets, and limited applicability. To solve these problems, we apply the equal local levels global testing method, which we have implemented in the R Package qqconf, a versatile tool to create Q-Q plots and probability-probability (P-P) plots in a wide variety of settings, with simultaneous testing bands rapidly created using recently-developed algorithms. qqconf can easily be used to add global testing bands to Q-Q plots made by other packages. In addition to being quick to compute, these bands have a variety of desirable properties, including accurate global levels, equal sensitivity to deviations in all parts of the null distribution (including the tails), and applicability to a range of null distributions. We illustrate the use of qqconf in several applications: assessing normality of residuals from regression, assessing accuracy of p values, and use of Q-Q plots in genome-wide association studies.

Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2.

Muscle bulk in adult healthy humans is highly variable even after height, age, and sex are accounted for. Low muscle mass, due to fewer and/or smaller constituent muscle fibers, would exacerbate the impact of muscle loss occurring in aging or disease. Genetic variability substantially influences muscle mass differences, but causative genes remain largely unknown. In a genome-wide association study (GWAS) on appendicular lean mass (ALM) in a population of 85,750 middle-aged (aged 38-49 years) individuals from the UK Biobank (UKB), we found 182 loci associated with ALM (p < 5 × 10-8). We replicated associations for 78% of these loci (p < 5 × 10-8) with ALM in a population of 181,862 elderly (aged 60-74 years) individuals from UKB. We also conducted a GWAS on hindlimb skeletal muscle mass of 1,867 mice from an advanced intercross between two inbred strains (LG/J and SM/J); this GWAS identified 23 quantitative trait loci. Thirty-eight positional candidates distributed across five loci overlapped between the two species. In vitro studies of positional candidates confirmed CPNE1 and STC2 as modifiers of myogenesis. Collectively, these findings shed light on the genetics of muscle mass variability in humans and identify targets for the development of interventions for treatment of muscle loss. The overlapping results between humans and the mouse model GWAS point to shared genetic mechanisms across species.

Kinpute: using identity by descent to improve genotype imputation.

MOTIVATION: Genotype imputation, though generally accurate, often results in many genotypes being poorly imputed, particularly in studies where the individuals are not well represented by standard reference panels. When individuals in the study share regions of the genome identical by descent (IBD), it is possible to use this information in combination with a study-specific reference panel (SSRP) to improve the imputation results. Kinpute uses IBD information-due to recent, familial relatedness or distant, unknown ancestors-in conjunction with the output from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype probabilities. Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigree, and results in substantially improved imputation quality. RESULTS: Given initial estimates of average IBD between subjects in the study sample, Kinpute uses a novel algorithm to select an optimal set of individuals to sequence and use as an SSRP. Kinpute is designed to use as input both this SSRP and the genotype probabilities output from other LD-based imputation software, and uses a new method to combine the LD imputed genotype probabilities with IBD configurations to substantially improve imputation. We tested Kinpute on a human population isolate where 98 individuals have been sequenced. In half of this sample, whose sequence data was masked, we used Impute2 to perform LD-based imputation and Kinpute was used to obtain higher accuracy genotype probabilities. Measures of imputation accuracy improved significantly, particularly for those genotypes that Impute2 imputed with low certainty. AVAILABILITY AND IMPLEMENTATION: Kinpute is an open-source and freely available C++ software package that can be downloaded from. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci.

BACKGROUND: A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability. OBJECTIVES: We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence. METHODS: We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy. We tested each SNP for both association and linkage with MS, the linkage being explored in terms of identity-by-descent (IBD) sharing excess and using gene dropping to compute a corresponding empirical p-value. By targeting regions that are both associated and in linkage with MS, we increase chances of identifying interesting genomic regions. RESULTS: We identified 486 MS-associated (p < 1 × 10-4) and 18,426 MS-linked (p < 0.05) SNPs. A total of 111 loci were both linked and associated with MS, 18 of them pointing to 14 non-major histocompatibility complex (MHC) genes, and 93 of them located in the MHC region. CONCLUSION: We discovered new suggestive signals and confirmed some previously identified ones. We believe this to represent a significant step toward an understanding of the genetic basis of MS.

Permutation testing in the presence of polygenic variation.

This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set, or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture, or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation-based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation-based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population.

Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.

Genome-wide association study of lung function phenotypes in a founder population.

BACKGROUND: Lung function is a long-term predictor of mortality and morbidity. OBJECTIVE: We sought to identify single nucleotide polymorphisms (SNPs) associated with lung function. METHODS: We performed a genome-wide association study (GWAS) of FEV1, forced vital capacity (FVC), and FEV1/FVC in 1144 Hutterites aged 6 to 89 years, who are members of a founder population of European descent. We performed least absolute shrinkage and selection operation regression to select the minimum set of SNPs that best predict FEV1/FVC in the Hutterites and used the GRAIL algorithm to mine the Gene Ontology database for evidence of functional connections between genes near the predictive SNPs. RESULTS: Our GWAS identified significant associations between FEV1/FVC and SNPs at the THSD4-UACA-TLE3 locus on chromosome 15q23 (P = 5.7 × 10(-8) to 3.4 × 10(-9)). Nine SNPs at or near 4 additional loci had P < 10(-5) with FEV1/FVC. Only 2 SNPs were found with P < 10(-5) for FEV1 or FVC. We found nominal levels of significance with SNPs at 9 of the 27 previously reported loci associated with lung function measures. Among a predictive set of 80 SNPs, 6 loci were identified that had a significant degree of functional connectivity (GRAIL P < .05), including 3 clusters of ß-defensin genes, 2 chemokine genes (CCL18 and CXCL12), and TNFRSF13B. CONCLUSION: This study identifies genome-wide significant associations and replicates results of previous GWASs. Multimarker modeling implicated for the first time common variation in genes involved in antimicrobial immunity in airway mucosa that influences lung function.

The sex-specific genetic architecture of quantitative traits in humans.

Mapping genetically complex traits remains one of the greatest challenges in human genetics today. In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomplete penetrance make thorough genetic dissection of complex traits difficult, if not impossible. Sex could be considered an environmental factor that can modify both penetrance and expressivity of a wide variety of traits. Sex is easily determined and has measurable effects on recognizable morphology; neurobiological circuits; susceptibility to autoimmune disease, diabetes, asthma, cardiovascular and psychiatric disease; and quantitative traits like blood pressure, obesity and lipid levels, among others. In this study, we evaluated sex-specific heritability and genome-wide linkages for 17 quantitative traits in the Hutterites. The results of this study could have important implications for mapping complex trait genes.

ADELLE: A global testing method for Trans-eQTL mapping.

Understanding the genetic regulatory mechanisms of gene expression is a challenging and ongoing problem. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that ADELLE is more powerful than other methods at detecting SNPs that are associated with 0.2-2% of the traits. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons.

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.

GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations.

SUMMARY: Mixed model-based approaches to genome-wide association studies (GWAS) of binary traits in related individuals can account for non-genetic risk factors in an integrated manner. However, they are technically challenging. GLOGS (Genome-wide LOGistic mixed model/Score test) addresses such challenges with efficient statistical procedures and a parallel implementation. GLOGS has high power relative to alternative approaches as risk covariate effects increase, and can complete a GWAS in minutes. AVAILABILITY: Source code and documentation are provided at http://www.bioinformatics.org/~stanhope/GLOGS.

Colloquium papers: Heritability of reproductive fitness traits in a human population.

The genetic basis of fitness traits has been studied widely in animals, yet the contribution of genetic variation to these traits in humans is controversial. In particular, it is difficult to disentangle genetic versus environmental effects on fertility, because of within-family correlations of sociocultural, economic, and other nongenetic factors that influence family sizes. In this study, we investigated the genetic architecture of reproductive fitness traits in a fertile human population whose communal lifestyle assures uniform and equal access to resources. Our study revealed significant heritabilities for reproductive traits in both men and women, after accounting for common household effects shared among siblings and demographic changes in reproductive practices. Furthermore, our results indicate that both autosomal and X-linked additive and dominance variances contribute to these traits. We therefore propose that reproductive traits should be amenable to genetic mapping studies, and the results we present here will facilitate the search for the novel genes influencing natural fertility in humans.

The CFTR Met 470 allele is associated with lower birth rates in fertile men from a population isolate.

Although little is known about the role of the cystic fibrosis transmembrane regulator (CFTR) gene in reproductive physiology, numerous variants in this gene have been implicated in etiology of male infertility due to congenital bilateral absence of the vas deferens (CBAVD). Here, we studied the fertility effects of three CBAVD-associated CFTR polymorphisms, the (TG)m and polyT repeat polymorphisms in intron 8 and Met470Val in exon 10, in healthy men of European descent. Homozygosity for the Met470 allele was associated with lower birth rates, defined as the number of births per year of marriage (P = 0.0029). The Met470Val locus explained 4.36% of the phenotypic variance in birth rate, and men homozygous for the Met470 allele had 0.56 fewer children on average compared to Val470 carrier men. The derived Val470 allele occurs at high frequencies in non-African populations (allele frequency = 0.51 in HapMap CEU), whereas it is very rare in African population (Fst = 0.43 between HapMap CEU and YRI). In addition, haplotypes bearing Val470 show a lack of genetic diversity and are thus longer than haplotypes bearing Met470 (measured by an integrated haplotype score [iHS] of -1.93 in HapMap CEU). The fraction of SNPs in the HapMap Phase2 data set with more extreme Fst and iHS measures is 0.003, consistent with a selective sweep outside of Africa. The fertility advantage conferred by Val470 relative to Met470 may provide a selective mechanism for these population genetic observations.

Characterization of gene expression patterns in response to an orthotospovirus infection between two diploid peanut species and their hybrid.

Tomato spotted wilt orthotospovirus (TSWV) transmitted by thrips causes significant yield loss in peanut (Arachis hypogaea L.) production. Use of peanut cultivars with moderate field resistance has been critical for TSWV management. However, current TSWV resistance is often not adequate, and the availability of sources of tetraploid resistance to TSWV is very limited. Allotetraploids derived by crossing wild diploid species could help introgress alleles that confer TSWV resistance into cultivated peanut. Thrips-mediated TSWV screening identified two diploids and their allotetraploid possessing the AA, BB, and AABB genomes Arachis stenosperma V10309, Arachis valida GK30011, and [A. stenosperma × A. valida]4x (ValSten1), respectively. These genotypes had reduced TSWV infection and accumulation in comparison with peanut of pure cultivated pedigree. Transcriptomes from TSWV-infected and non-infected samples from A. stenosperma, A. valida, and ValSten1 were assembled, and differentially expressed genes (DEGs) following TSWV infection were assessed. There were 3,196, 8,380, and 1,312 significant DEGs in A. stenosperma, A. valida, and ValSten1, respectively. A higher proportion of genes decreased in expression following TSWV infection for A. stenosperma and ValSten1, whereas a higher proportion of genes increased in expression following infection in A. valida. The number of DEGs previously annotated as defense-related in relation to abiotic and biotic stress was highest in A. valida followed by ValSten1 and A. stenosperma. Plant phytohormone and photosynthesis genes also were differentially expressed in greater numbers in A. valida followed by ValSten1 and A. stenosperma, with over half of those exhibiting decreases in expression.

Genetic regulatory effects in response to a high cholesterol, high fat diet in baboons.

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWAS), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using nonhuman primate models.

Evaluation of Wild Peanut Species and Their Allotetraploids for Resistance against Thrips and Thrips-Transmitted Tomato Spotted Wilt Orthotospovirus (TSWV).

Thrips-transmitted tomato spotted wilt orthotospovirus (TSWV) causes spotted wilt disease in peanut (Arachis hypogaea L.) and limits yield. Breeding programs have been developing TSWV-resistant cultivars, but availability of sources of resistance against TSWV in cultivated germplasm is extremely limited. Diploid wild Arachis species can serve as important sources of resistance, and despite ploidy barriers (cultivated peanut is tetraploid), their usage in breeding programs is now possible because of the knowledge and development of induced interspecific allotetraploid hybrids. This study screened 10 wild diploid Arachis and six induced allotetraploid genotypes via thrips-mediated TSWV transmission assays and thrips' feeding assays in the greenhouse. Three parameters were evaluated: percent TSWV infection, virus accumulation, and temporal severity of thrips feeding injury. Results indicated that the diploid A. stenosperma accession V10309 and its derivative-induced allotetraploid ValSten1 had the lowest TSWV infection incidences among the evaluated genotypes. Allotetraploid BatDur1 had the lowest thrips-inflicted damage at each week post thrips release, while diploid A. batizocoi accession K9484 and A. duranensis accession V14167 had reduced feeding damage one week post thrips release, and diploids A. valida accession GK30011 and A. batizocoi had reduced feeding damage three weeks post thrips releasethan the others. Overall, plausible TSWV resistance in diploid species and their allotetraploid hybrids was characterized by reduced percent TSWV infection, virus accumulation, and feeding severity. Furthermore, a few diploids and tetraploid hybrids displayed antibiosis against thrips. These results document evidence for resistance against TSWV and thrips in wild diploid Arachis species and peanut-compatible-induced allotetraploids.

Identity by descent estimation with dense genome-wide genotype data.

We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of accommodating the background linkage disequilibrium (LD) that is present in high-density genotype data. We show that IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate the probability for each possible IBD sharing state at every SNP from a high-density genotyping array for hundreds of thousands of pairs of individuals. We use it to estimate point-wise and genomewide IBD sharing between 185,745 pairs of subjects all of whom are related through a single, large and complex 13-generation pedigree and genotyped with the Affymetrix 500 k chip. We find that we are able to identify the true pedigree relationship for individuals who were misidentified in the collected data and estimate empirical kinship coefficients that can be used in follow-up QTL mapping studies. IBDLD is implemented as an open source software package and is freely available.

Genetic variance components estimation for binary traits using multiple related individuals.

Understanding and modeling genetic or nongenetic factors that influence susceptibility to complex traits has been the focus of many genetic studies. Large pedigrees with known complex structure may be advantageous in epidemiological studies since they can significantly increase the number of factors whose influence on the trait can be estimated. We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree or an estimated covariance matrix. Our approach is based on a hierarchical model where we first assess the probability of each individual having the trait and then formulate a likelihood assuming conditional independence of individuals. The advantage of our formulation is that it easily incorporates information from pertinent covariates as fixed effects and at the same time takes into account the correlation between individuals that share genetic background or other random effects. The high dimensionality of the integration involved in the likelihood prohibits exact computations. Instead, an automated Monte Carlo expectation maximization algorithm is employed for obtaining the maximum likelihood estimates of the model parameters. Through a simulation study we demonstrate that our method can provide reliable estimates of the model parameters when the sample size is close to 500. Implementation of our method to data from a pedigree of 491 Hutterites evaluated for Type 2 diabetes (T2D) reveal evidence of a strong genetic component to T2D risk, particularly for younger and leaner cases.