Genome-wide Association Testing Research Articles

Identification of novel genetic risk loci in Maltese dogs with necrotizing meningoencephalitis and evidence of a shared genetic risk across toy dog breeds.

Necrotizing meningoencephalitis (NME) affects toy and small breed dogs causing progressive, often fatal, inflammation and necrosis in the brain. Genetic risk loci for NME previously were identified in pug dogs, particularly associated with the dog leukocyte antigen (DLA) class II complex on chromosome 12, but have not been investigated in other susceptible breeds. We sought to evaluate Maltese and Chihuahua dogs, in addition to pug dogs, to identify novel or shared genetic risk factors for NME development. Genome-wide association testing of single nucleotide polymorphisms (SNPs) in Maltese dogs with NME identified 2 regions of genome-wide significance on chromosomes 4 (chr4:74522353T>A, p = 8.1×10−7) and 15 (chr15:53338796A>G, p = 1.5×10−7). Haplotype analysis and fine-mapping suggests that ILR7 and FBXW7, respectively, both important for regulation of immune system function, could be the underlying associated genes. Further evaluation of these regions and the previously identified DLA II locus across all three breeds, revealed an enrichment of nominal significant SNPs associated with chromosome 15 in pug dogs and DLA II in Maltese and Chihuahua dogs. Meta-analysis confirmed effect sizes the same direction in all three breeds for both the chromosome 15 and DLA II loci (p = 8.6×10–11 and p = 2.5×10−7, respectively). This suggests a shared genetic background exists between all breeds and confers susceptibility to NME, but effect sizes might be different among breeds. In conclusion, we identified the first genetic risk factors for NME development in the Maltese, chromosome 4 and chromosome 15, and provide evidence for a shared genetic risk between breeds associated with chromosome 15 and DLA II. Last, DLA II and IL7R both have been implicated in human inflammatory diseases of the central nervous system such as multiple sclerosis, suggesting that similar pharmacotherapeutic targets across species should be investigated.

Association mapping of germination traits in Arabidopsis thaliana under light and nutrient treatments: searching for G×E effects.

In the natural world, genotype expression is influenced by an organism’s environment. Identifying and understanding the genes underlying phenotypes in different environments is important for making advances in fields ranging from evolution to medicine to agriculture. With the availability of genome-wide genetic-marker datasets, it is possible to look for genes that interact with the environment. Using the model organism, Arabidopsis thaliana, we looked for genes underlying phenotypes as well as genotype-by-environment interactions in four germination traits under two light and two nutrient conditions. We then performed genome-wide association tests to identify candidate genes underlying the observed phenotypes and genotype-by-environment interactions. Of the four germination traits examined, only two showed significant genotype-by-environment interactions. While genome-wide association analyses did not identify any markers or genes explicitly linked to genotype-by-environment interactions, we did identify a total of 55 markers and 71 genes associated with germination differences. Of the 71 genes, four—ZIGA4, PS1, TOR, and TT12—appear to be strong candidates for further study of germination variation under different environments.

Genome-wide association tests of inversions with application to psoriasis

Although inversions have occasionally been found to be associated with disease susceptibility through interrupting a gene or its regulatory region, or by increasing the risk for deleterious secondary rearrangements, no association study has been specifically conducted for risks associated with inversions, mainly because existing approaches to detecting and genotyping inversions do not readily scale to a large number of samples. Based on our recently proposed approach to identifying and genotyping inversions using principal components analysis (PCA), we herein develop a method of detecting association between inversions and disease in a genome-wide fashion. Our method uses genotype data for single nucleotide polymorphisms (SNPs), and is thus cost-efficient and computationally fast. For an inversion polymorphism, local PCA around the inversion region is performed to infer the inversion genotypes of all samples. For many inversions, we found that some of the SNPs inside an inversion region are fixed in the two lineages of different orientations and thus can serve as surrogate markers. Our method can be applied to case-control and quantitative trait association studies to identify inversions that may interrupt a gene or the connection between a gene and its regulatory agents. Our method also offers a new venue to identify inversions that are responsible for disease-causing secondary rearrangements. We illustrated our proposed approach to case-control data for psoriasis and identified novel associations with a few inversion polymorphisms.

Mapping eQTLs in the Norfolk Island Genetic Isolate Identifies Candidate Genes for CVD Risk Traits

Cardiovascular disease (CVD) affects millions of people worldwide and is influenced by numerous factors, including lifestyle and genetics. Expression quantitative trait loci (eQTLs) influence gene expression and are good candidates for CVD risk. Founder-effect pedigrees can provide additional power to map genes associated with disease risk. Therefore, we identified eQTLs in the genetic isolate of Norfolk Island (NI) and tested for associations between these and CVD risk factors. We measured genome-wide transcript levels of blood lymphocytes in 330 individuals and used pedigree-based heritability analysis to identify heritable transcripts. eQTLs were identified by genome-wide association testing of these transcripts. Testing for association between CVD risk factors (i.e., blood lipids, blood pressure, and body fat indices) and eQTLs revealed 1,712 heritable transcripts (p < 0.05) with heritability values ranging from 0.18 to 0.84. From these, we identified 200 cis-acting and 70 trans-acting eQTLs (p < 1.84 × 10(-7)) An eQTL-centric analysis of CVD risk traits revealed multiple associations, including 12 previously associated with CVD-related traits. Trait versus eQTL regression modeling identified four CVD risk candidates (NAAA, PAPSS1, NME1, and PRDX1), all of which have known biological roles in disease. In addition, we implicated several genes previously associated with CVD risk traits, including MTHFR and FN3KRP. We have successfully identified a panel of eQTLs in the NI pedigree and used this to implicate several genes in CVD risk. Future studies are required for further assessing the functional importance of these eQTLs and whether the findings here also relate to outbred populations.

Genome-wide association testing reveals quantitative trait loci for fillet texture and fat content in Atlantic salmon

Within the aquaculture industry downgrading losses due to poor quality represent large costs to the producers, and an interest in implementing quality parameters in breeding goals has therefore emerged. Through genome-wide association studies (GWAS) one can identify genetic variation affecting quality parameters, which can in turn facilitate efficient implementation in breeding schemes by use of marker-assisted selection.This study aimed to identify genetic variation affecting fillet fat content and fillet firmness in farmed Norwegian Atlantic salmon using 5650 genome-wide distributed SNPs. Phenotypic records from instrumental analyses of fillet fat content and fillet firmness were retrieved from fish provided by a commercial Norwegian breeding company and implemented in GWAS. Further, genome partitioning by estimation of variance components for individual chromosomes based on SNPs was conducted for the purpose of validating results from GWAS.Combined the results from GWAS and genome partitioning suggested that genetic variation affecting fillet fat content is present on Atlantic salmon chromosomes 9 and 10, and that genetic variation affecting fillet firmness is present on Atlantic salmon chromosomes 3 and 11.

Residual association at C9orf72 suggests an alternative amyotrophic lateral sclerosis-causing hexanucleotide repeat

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of motor neurons. Single-nucleotide polymorphism rs3849942 is associated with ALS, tagging a hexanucleotide repeat mutation in the C9orf72 gene. It is possible that there is more than 1 disease-causing genetic variation at this locus, in which case association might remain after removal of cases carrying the mutation. DNA from patients with ALS was therefore tested for the mutation. Genome-wide association testing was performed first using all samples, and then restricting the analysis to samples not carrying the mutation. rs3849942 and rs903603 were strongly associated with ALS when all samples were included (rs3849942, p = [3 × 2] × 10−6, rank 7/442,057; rs903603, p = [7 × 6] × 10−8, rank 2/442,057). Removal of the mutation-carrying cases resulted in loss of association for rs3849942 (p = [2 × 6] × 10−3, rank 1225/442,068), but had little effect on rs903603 (p = [1 × 9] × 10−5, rank 8/442,068). Those with a risk allele of rs903603 had an excess of apparent homozygosity for wild type repeat alleles, consistent with polymerase chain reaction failure of 1 allele because of massive repeat expansion. These results indicate residual association at the C9orf72 locus suggesting a second disease-causing repeat mutation.

SNP selection for predicting a quantitative trait

Molecular markers combined with powerful statistical tools have made it possible to detect and analyze multiple loci on the genome that are responsible for the phenotypic variation in quantitative traits. The objectives of the study presented in this paper are to identify a subset of single nucleotide polymorphism (SNP) markers that are associated with a particular trait and to construct a model that can best predict the value of the trait given the genotypic information of the SNPs using a three-step strategy. In the first step, a genome-wide association test is performed to screen SNPs that are associated with the quantitative trait of interest. SNPs with p-values of less than 5% are then analyzed in the second step. In the second step, a large number of randomly selected models, each consisting of a fixed number of randomly selected SNPs, are analyzed using the least angle regression method. This step will further remove redundant SNPs due to the complicated association among SNPs. A subset of SNPs that are shown to have a significant effect on the response trait more often than by chance are considered for the third step. In the third step, two alternative methods are considered: the least angle shrinkage and selection operation and sparse partial least squares regression. For both methods, the predictive ability of the fitted model is evaluated by an independent test set. The performance of the proposed method is illustrated by the analysis of a real data set on Canadian Holstein cattle.

Genome-wide ancestry association testing identifies a common European variant on 6q14.1 as a risk factor for asthma in African American subjects

Genetic variants that contribute to asthma susceptibility might be present at varying frequencies in different populations, which is an important consideration and advantage for performing genetic association studies in admixed populations. We sought to identify asthma-associated loci in African American subjects. We compared local African and European ancestry estimated from dense single nucleotide polymorphism genotype data in African American adults with asthma and nonasthmatic control subjects. Allelic tests of association were performed within the candidate regions identified, correcting for local European admixture. We identified a significant ancestry association peak on chromosome 6q. Allelic tests for association within this region identified a single nucleotide polymorphism (rs1361549) on 6q14.1 that was associated with asthma exclusively in African American subjects with local European admixture (odds ratio, 2.2). The risk allele is common in Europe (42% in the HapMap population of Utah residents with Northern and Western European ancestry from the Centre d'Etude du Polymorphisme Humain collection) but absent in West Africa (0% in the HapMap population of Yorubans in Ibadan, Nigeria), suggesting the allele is present in African American subjects because of recent European admixture. We replicated our findings in Puerto Rican subjects and similarly found that the signal of association is largely specific to subjects who are heterozygous for African and non-African ancestry at 6q14.1. However, we found no evidence for association in European American or Puerto Rican subjects in the absence of local African ancestry, suggesting that the association with asthma at rs1361549 is due to an environmental or genetic interaction. We identified a novel asthma-associated locus that is relevant to admixed populations with African ancestry and highlight the importance of considering local ancestry in genetic association studies of admixed populations.

Detecting disease rare alleles using single SNPs in families and haplotyping in unrelated subjects from the Genetic Analysis Workshop 17 data

We present an evaluation of discovery power for two association tests that work well with common alleles but are applied to the Genetic Analysis Workshop 17 simulations with rare causative single-nucleotide polymorphisms (SNPs) (minor allele frequency [MAF] < 1%). The methods used were genome-wide single-SNP association tests based on a linear mixed-effects model for discovery and applied to the familial sample and sliding windows haplotype association tests for replication, implemented within causative genes in the unrelated individuals sample. Both methods are evaluated with respect to the simulated trait Q2. The linear mixed-effects model and haplotype association tests failed to detect the rare alleles of the simulated associations. In contrast, the linear mixed-effects model and haplotype association tests detected effects for the most important simulated SNPs with MAF > 1%. We conclude that these findings reflect inadequate statistical power (the result of small simulated samples) for the complex genetic model that underlies these data.

A genome-wide association study of osteochondritis dissecans in the Thoroughbred.

Osteochondrosis is a developmental orthopaedic disease that occurs in horses, other livestock species, companion animal species, and humans. The principal aim of this study was to identify quantitative trait loci (QTL) associated with osteochondritis dissecans (OCD) in the Thoroughbred using a genome-wide association study. A secondary objective was to test the effect of previously identified QTL in the current population. Over 300 horses, classified as cases or controls according to clinical findings, were genotyped for the Illumina Equine SNP50 BeadChip. An animal model was first implemented in order to adjust each horse's phenotypic status for average relatedness among horses and other potentially confounding factors which were present in the data. The genome-wide association test was then conducted on the residuals from the animal model. A single SNP on chromosome 3 was found to be associated with OCD at a genome-wide level of significance, as determined by permutation. According to the current sequence annotation, the SNP is located in an intergenic region of the genome. The effects of 24 SNPs, representing QTL previously identified in a sample of Hanoverian Warmblood horses, were tested directly in the animal model. When fitted alongside the significant SNP on ECA3, two of these SNPs were found to be associated with OCD. Confirmation of the putative QTL identified on ECA3 requires validation in an independent sample. The results of this study suggest that a significant challenge faced by equine researchers is the generation of sufficiently large data sets to effectively study complex diseases such as osteochondrosis.

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

OBJECTIVEProinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.RESEARCH DESIGN AND METHODSWe have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.RESULTSNine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.CONCLUSIONSWe have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

Just in Time: An Update on Continuum Neurogenetics

The field of neurogenetics has been revolutionized by the advances in genome-wide association testing. Recent gene discoveries for disorders of neurodegeneration, CNS demyelination, and neurodevelopmental and cerebrovascular syndromes begin to shape our understanding of the complexity of their underlying genetic architecture. In the future, this knowledge should advance risk prediction, prevention, diagnosis, and treatment strategies for patients with neurologic disorders and their families.

Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.

Candidate genes versus genome-wide associations: which are better for detecting genetic susceptibility to infectious disease?

Technological developments allow increasing numbers of markers to be deployed in case-control studies searching for genetic factors that influence disease susceptibility. However, with vast numbers of markers, true 'hits' may become lost in a sea of false positives. This problem may be particularly acute for infectious diseases, where the control group may contain unexposed individuals with susceptible genotypes. To explore this effect, we used a series of stochastic simulations to model a scenario based loosely on bovine tuberculosis. We find that a candidate gene approach tends to have greater statistical power than studies that use large numbers of single nucleotide polymorphisms (SNPs) in genome-wide association tests, almost regardless of the number of SNPs deployed. Both approaches struggle to detect genetic effects when these are either weak or if an appreciable proportion of individuals are unexposed to the disease when modest sample sizes (250 each of cases and controls) are used, but these issues are largely mitigated if sample sizes can be increased to 2000 or more of each class. We conclude that the power of any genotype-phenotype association test will be improved if the sampling strategy takes account of exposure heterogeneity, though this is not necessarily easy to do.

Genome-wide association study identifies genes that may contribute to risk for developing heroin addiction

We have used genome-wide association studies to identify variants that are associated with vulnerability to develop heroin addiction. DNA from 325 methadone stabilized, former severe heroin addicts and 250 control individuals were pooled by ethnicity (Caucasian and African-American) and analyzed using the Affymetrix GeneChip Mapping 100 K Set. Genome-wide association tests were conducted. The strongest association with vulnerability to develop heroin addiction, with experiment-wise significance (P=0.035), was found in Caucasians with the variant rs10494334, a variant in an unannotated region of the genome (1q23.3). In African Americans, the variant most significantly associated with the heroin addiction vulnerability was rs950302, found in the cytosolic dual specificity phosphatase 27 gene DUSP27 (point-wise P=0.0079). Furthermore, analysis of the top 500 variants with the most significant associations (point-wise P< or =0.0036) in Caucasians showed that three of these variants are clustered in the regulating synaptic membrane exocytosis protein 2 gene RIMS2. Of the top 500 variants in African-Americans (point-wise P< or =0.0238), three variants are in the cardiomyopathy associated 3 gene CMYA3. This study identifies new genes and variants that may increase an individual's vulnerability to develop heroin addiction.

PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing

In genome-wide association studies (GWAS) examining hundreds of thousands of genetic markers, the potentially high number of false positive findings requires statistical correction for multiple testing. Permutation tests are considered the gold standard for multiple testing correction in GWAS, because they simultaneously provide unbiased type I error control and high power. At the same time, they demand heavy computational effort, especially with large-scale datasets of modern GWAS. In recent years, the computational problem has been circumvented by using approximations to permutation tests, which, however, may be biased. We have tackled the original computational problem of permutation testing in GWAS and herein present a permutation test algorithm one or more orders of magnitude faster than existing implementations, which enables efficient permutation testing on a genome-wide scale. Our algorithm does not rely on any kind of approximation and hence produces unbiased results identical to a standard permutation test. A noteworthy feature of our algorithm is a particularly effective performance when analyzing high-density marker sets. Freely available on the web at http://www.permory.org.

Abstract 2011: Separating the signal from the noise: DNA variation as a perturbagen distinguishes between causal and follower genes associated with tumorigenesis and progression in a heptacellular carcinoma (HCC) cohort

Abstract Given genomic instability as a defining characteristic of cancer, one of the central problems in identifying those genes that drive cancer onset and progression has been distinguishing these causal genes from the massive number of other genes that change as a result of the instability, but that are not central to the biological processes associated with cancer onset and progression. The study presented here integrates DNA variation with gene expression and RNA sequencing data in normal and matched tumor tissues collected from hepatocellular carcinoma (HCC) patients to distinguish between genes that cause tumorigenesis and cancer progression and those that respond to these events or that tag along with the causal genes as a consequence of genomic instability. Tumor and adjacent normal tissues were collected from an HCC cohort, and both DNA and RNA isolated from these tissues were profiled using gene expression and SNP genotyping arrays. On a smaller subset of individuals, we characterized the HCC transcriptome in both tumor and adjacent normal tissues using long reads from Pacific Biosciences’ single-molecule real-time (SMRT) sequencing platform. Transcript sequencing has previously been limited to messages less than ∼1100 bases in length for sequencing in one contiguous read. However, the SMRT platform's ability to sequence &amp;gt;3,000-base transcripts in a single read provided the ability to characterize aberrant cancer transcripts of this length. An integrative genomics analysis of these data revealed that the process of tumorigenesis resulted in rearrangements of gene networks in which networks specific to normal tissue function were disrupted and networks critical to tumor formation/progression were created. These changes were predicted to be driven by somatic copy number variation (CNV) that also associated with disease free survival and other outcome parameters. Validation of the inferred causal relationships were carried out by genetically mapping the recurrence rate and magnitude of somatic CNV using genome-wide genetic association testing in the HCC cohort, where CNVs were considered as a quantitative trait and tested for association to germ line SNP genotypes. By associating SNP genotypes with somatic CNVs, statistical procedures to infer causal relationships between somatic CNVs and associated gene expression traits could be applied to validate that changes in copy number lead to changes in expression that in turn drive changes in network states associated with tumorigenesis. These causal relationships were further confirmed using a treatment that promotes HCC tumorigenesis. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2011.

Accounting for population structure and relatedness in gene expression genome-wide association testing using a mixed-model approach

Accounting for population structure and relatedness in gene expression genome-wide association testing using a mixed-model approach

Assessing the impact of global versus local ancestry in association studies.

To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs. Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates. The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs. Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.

Toward the molecular dissection of primary biliary cirrhosis # †

Toward the molecular dissection of primary biliary cirrhosis # †