Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms

Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system with an important genetic component and strongest association driven by the HLA genes. We performed a pooling-based genome-wide association study of 500,000 SNPs in order to find new loci associated with the disease. After applying several criteria, 320 SNPs were selected from the microarrays and individually genotyped in a first and independent Spanish Caucasian replication cohort. The 8 most significant SNPs validated in this cohort were also genotyped in a second US Caucasian replication cohort for confirmation. The most significant association was obtained for SNP rs3129934, which neighbors the HLA-DRB/DQA loci and validates our pooling-based strategy. The second strongest association signal was found for SNP rs1327328, which resides in an unannotated region of chromosome 13 but is in linkage disequilibrium with nearby functional elements that may play important roles in disease susceptibility. This region of chromosome 13 has not been previously identified in MS linkage genome screens and represents a novel risk locus for the disease.     

A polymorphism in the HLA-DPB1 gene is associated with susceptibility to multiple sclerosis

We conducted an association study across the human leukocyte antigen (HLA) complex to identify loci associated with multiple sclerosis (MS). Comparing 1927 SNPs in 1618 MS cases and 3413 controls of European ancestry, we identified seven SNPs that were independently associated with MS conditional on the others (each P ≤ 4 x 10(-6)). All associations were significant in an independent replication cohort of 2212 cases and 2251 controls (P ≤ 0.001) and were highly significant in the combined dataset (P ≤ 6 x 10(-8)). The associated SNPs included proxies for HLA-DRB1*15:01 and HLA-DRB1*03:01, and SNPs in moderate linkage disequilibrium (LD) with HLA-A*02:01, HLA-DRB1*04:01 and HLA-DRB1*13:03. We also found a strong association with rs9277535 in the class II gene HLA-DPB1 (discovery set P = 9 x 10(-9), replication set P = 7 x 10(-4), combined P = 2 x 10(-10)). HLA-DPB1 is located centromeric of the more commonly typed class II genes HLA-DRB1, -DQA1 and -DQB1. It is separated from these genes by a recombination hotspot, and the association is not affected by conditioning on genotypes at DRB1, DQA1 and DQB1. Hence rs9277535 represents an independent MS-susceptibility locus of genome-wide significance. It is correlated with the HLA-DPB1*03:01 allele, which has been implicated previously in MS in smaller studies. Further genotyping in large datasets is required to confirm and resolve this association.     

GCTA: a tool for genome-wide complex trait analysis

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.     

Data integration workflow for search of disease driving genes and genetic variants

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/     

FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs

Single nucleotide polymorphisms (SNPs) are increasingly used to tag genetic loci associated with phenotypes such as risk of complex diseases. Technically, this is done genome-wide without prior restriction or knowledge of biological feasibility in scans referred to as genome-wide association studies (GWAS). Depending on the linkage disequilibrium (LD) structure at a particular locus, such tagSNPs may be surrogates for many thousands of other SNPs, and it is difficult to distinguish those that may play a functional role in the phenotype from those simply genetically linked. Because a large proportion of tagSNPs have been identified within non-coding regions of the genome, distinguishing functional from non-functional SNPs has been an even greater challenge. A strategy was recently proposed that prioritizes surrogate SNPs based on non-coding chromatin and epigenomic mapping techniques that have become feasible with the advent of massively parallel sequencing. Here, we introduce an R/Bioconductor software package that enables the identification of candidate functional SNPs by integrating information from tagSNP locations, lists of linked SNPs from the 1000 genomes project and locations of chromatin features which may have functional significance. Availability: FunciSNP is available from Bioconductor (bioconductor.org).     

A genome-wide survey reveals a deletion polymorphism associated with resistance to gastrointestinal nematodes in Angus cattle

Gastrointestinal (GI) nematode infections are a worldwide threat to human health and animal production. In this study, we performed a genome-wide association study between copy number variations (CNVs) and resistance to GI nematodes in an Angus cattle population. Using a linear regression analysis, we identified one deletion CNV which reaches genome-wide significance after Bonferroni correction. With multiple mapped human olfactory receptor genes but no annotated bovine genes in the region, this significantly associated CNV displays high population frequencies (58.26 %) with a length of 104.8 kb on chr7. We further investigated the linkage disequilibrium (LD) relationships between this CNV and its nearby single nucleotide polymorphisms (SNPs) and genes. The underlining haplotype blocks contain immune-related genes such as ZNF496 and NLRP3. As this CNV co-segregates with linked SNPs and associated genes, we suspect that it could contribute to the detected variations in gene expression and thus differences in host parasite resistance.     

Novel rank-based approaches for discovery and replication in genome-wide association studies

In recent years, genome-wide association studies (GWAS) have uncovered a large number of susceptibility variants. Nevertheless, GWAS findings provide only tentative evidence of association, and replication studies are required to establish their validity. Due to this uncertainty, researchers often focus on top-ranking SNPs, instead of considering strict significance thresholds to guide replication efforts. The number of SNPs for replication is often determined ad hoc. We show how the rank-based approach can be used for sample size allocation in GWAS as well as for deciding on a number of SNPs for replication. The basis of this approach is the "ranking probability": chances that at least j true associations will rank among top u SNPs, when SNPs are sorted by P-value. By employing simple but accurate approximations for ranking probabilities, we accommodate linkage disequilibrium (LD) and evaluate consequences of ignoring LD. Further, we relate ranking probabilities to the proportion of false discoveries among top u SNPs. A study-specific proportion can be estimated from P-values, and its expected value can be predicted for study design applications.     

The structure of linkage disequilibrium around a selective sweep

The fixation of advantageous mutations by natural selection has a profound impact on patterns of linked neutral variation. While it has long been appreciated that such selective sweeps influence the frequency spectrum of nearby polymorphism, it has only recently become clear that they also have dramatic effects on local linkage disequilibrium. By extending previous results on the relationship between genealogical structure and linkage disequilibrium, I obtain simple expressions for the influence of a selective sweep on patterns of allelic association. I show that sweeps can increase, decrease, or even eliminate linkage disequilibrium (LD) entirely depending on the relative position of the selected and neutral loci. I also show the importance of the age of the neutral mutations in predicting their degree of association and describe the consequences of such results for the interpretation of empirical data. In particular, I demonstrate that while selective sweeps can eliminate LD, they generate patterns of genetic variation very different from those expected from recombination hotspots.     

A genome-wide association study for racing performances in Thoroughbreds clarifies a candidate region near the MSTN gene

Using 1400 microsatellites, a genome-wide association study (GWAS) was performed to identify genomic regions associated with lifetime earnings and performance ranks, as determined by the Japan Racing Association (JRA). The minimum heritability (h(2) ) was estimated at 7-8% based on the quantitative trait model, suggesting that the racing performance is heritable. Following GWAS with microsatellites, fine mapping led to identification of three SNPs on ECA18, namely, g.65809482T>C (P=1.05E-18), g.65868604G>T (P=6.47E-17), and g.66539967A>G (P=3.35E-14) associated with these performance measures. The haplotype of these SNPs, together with a recently published nearby SNP, g.66493737C>T (P=9.06E-16) in strong linkage disequilibrium, also showed a very clear association with the performance (P<1E-05). The candidate genomic region contained eight genes annotated by ENSEMBL, including the myostatin gene (MSTN). These findings suggest the presence of a gene affecting the racing performance in Thoroughbred racehorses in this region on ECA18.     

Positive association of the human GABA-A-receptor beta 2 subunit gene haplotype with schizophrenia in the Chinese Han population

A converging body of evidence implicates the gamma-amino butyric acid neurotransmitter system in the pathogenesis of schizophrenia. Recently, Lo et al. reported strong positive association between schizophrenia and GABRB2, demonstrated by single markers and haplotypes of five markers in introns of GABRB2, rs6556547, rs1816071, rs194072, rs252944, and rs187269. To validate this linkage disequilibrium report, we genotyped these five SNPs and additional rs1816072 in 352 Chinese Han family trios. Though we failed to detect any positive results in single markers, we did find a significant haplotypic association (global p = 0.00157-0.00588) which had not been identified in Lo's study. Our data indicated that the haplotype 'GACTCT' (p = 0.00215, frequency = 53.6%) was overtransmitted which suggests that GABRB2 is in linkage disequilibrium with schizophrenia in the Chinese Han population. The difference between the two studies may be due to the respective analytic power of the two designs. These two independent studies highlighting linkage disequilibrium support the potential involvement of GABRB2 or a nearby gene in the genetic etiology of schizophrenia.     

HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms

Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs. AVAILABILITY: The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.     

Evaluating coverage of exons by HapMap SNPs

Genome-wide association (GWA) studies are currently one of the most powerful tools in identifying disease-associated genes or variants. In typical GWA studies, single-nucleotide polymorphisms (SNPs) are often used as genetic makers. Therefore, it is critical to estimate the percentage of genetic variations which can be covered by SNPs through linkage disequilibrium (LD). In this study, we use the concept of haplotype blocks to evaluate the coverage of five SNP sets including the HapMap and four commercial arrays, for every exon in the human genome. We show that although some Chips can reach similar coverage as the HapMap, only about 50% of exons are completely covered by haplotype blocks of HapMap SNPs. We suggest further high-resolution genotyping methods are required, to provide adequate genome-wide power for identifying variants.     

Predicting functionally important SNP classes based on negative selection

Background  

With the advent of cost-effective genotyping technologies, genome-wide association studies allow researchers to examine hundreds of thousands of single nucleotide polymorphisms (SNPs) for association with human disease. Recently, many researchers applying this strategy have detected strong associations to disease with SNP markers that are either not in linkage disequilibrium with any nonsynonymous SNP or large distances from any annotated gene. In such cases, no well-established standard practice for effective SNP selection for follow-up studies exists. We aim to identify and prioritize groups of SNPs that are more likely to affect phenotypes in order to facilitate efficient SNP selection for follow-up studies.     

Genome-wide association study identifies candidate markers for bull fertility in Holstein dairy cattle

The decline in the reproductive efficiency of dairy cattle has become a challenging problem worldwide. Female fertility is now taken into account in breeding goals while generally less attention is given to male fertility. The objective of this study was to perform a genome-wide association study in Holstein bulls to identify genetic variants significantly related to sire conception rate (SCR), a new phenotypic evaluation of bull fertility. The analysis included 1755 sires with SCR data and 38,650 single nucleotide polymorphisms (SNPs) spanning the entire bovine genome. Associations between SNPs and SCR were analyzed using a mixed linear model that included a random polygenic effect and SNP genotype either as a linear covariate or as a categorical variable. A multiple testing correction approach was used to account for the correlation between SNPs because of linkage disequilibrium. After genome-wide correction, eight SNPs showed significant association with SCR. Some of these SNPs are located close to or in the middle of genes with functions related to male fertility, such as the sperm acrosome reaction, chromatin remodeling during the spermatogenesis, and the meiotic process during male germ cell maturation. Some SNPs showed marked dominance effects, which provide more evidence for the relevance of non-additive effects in traits closely related to fitness such as fertility. The results could contribute to the identification of genes and pathways associated with male fertility in dairy cattle.     

A new approach to account for the correlations among single nucleotide polymorphisms in genome: wide association studies

In genetic association studies, such as genome-wide association studies (GWAS), the number of single nucleotide polymorphisms (SNPs) can be as large as hundreds of thousands. Due to linkage disequilibrium, many SNPs are highly correlated; assuming they are independent is not valid. The commonly used multiple comparison methods, such as Bonferroni correction, are not appropriate and are too conservative when applied to GWAS. To overcome these limitations, many approaches have been proposed to estimate the so-called effective number of independent tests to account for the correlations among SNPs. However, many current effective number estimation methods are based on eigenvalues of the correlation matrix. When the dimension of the matrix is large, the numeric results may be unreliable or even unobtainable. To circumvent this obstacle and provide better estimates, we propose a new effective number estimation approach which is not based on the eigenvalues. We compare the new method with others through simulated and real data. The comparison results show that the proposed method has very good performance.     

Genetic Variants and Multiple Sclerosis Risk Gene SLC9A9 Expression in Distinct Human Brain Regions

A recent genome-wide association study reported a significant association between rs9828519 (G) and nonresponsiveness to interferon-beta (IFN-β) treatment and dysregulation of SLC9A9 expression in multiple sclerosis (MS) cases. We hypothesize that disease-relevant tissues are necessary to detect the effects of rs9828519-tagged SNPs on SLC9A9 expression. Here, we investigated whether SLC9A9 expression is regulated by rs9828519-tagged SNPs in human brain tissue. We used HaploReg to identify the proxy SNPs of the rs9828519 variant based on linkage disequilibrium information from the 1000 Genomes Project. We evaluated the potential association between these SNPs and SLC9A9 expression using multiple expression quantitative trait loci datasets including 10 brain regions of 134 individuals from Braineac, 2 brain regions of 773 samples from brain expression GWAS datasets, and 12 brain regions from the GTEx. We discovered differential SLC9A9 expression in different brain regions and identified 15 rs9828519-tagged SNPs that significantly regulated SLC9A9 expression only in occipital cortex, intralobular white matter, and substantia nigra. Our results advance the understanding of the involvement of SLC9A9 and rs9828519 mechanisms in MS.     

Polymorphisms in the receptor tyrosine kinase MERTK gene are associated with multiple sclerosis susceptibility

Multiple sclerosis (MS) is a debilitating, chronic demyelinating disease of the central nervous system affecting over 2 million people worldwide. The TAM family of receptor tyrosine kinases (TYRO3, AXL and MERTK) have been implicated as important players during demyelination in both animal models of MS and in the human disease. We therefore conducted an association study to identify single nucleotide polymorphisms (SNPs) within genes encoding the TAM receptors and their ligands associated with MS. Analysis of genotype data from a genome-wide association study which consisted of 1618 MS cases and 3413 healthy controls conducted by the Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene) revealed several SNPs within the MERTK gene (Chromosome 2q14.1, Accession Number NG_011607.1) that showed suggestive association with MS. We therefore interrogated 28 SNPs in MERTK in an independent replication cohort of 1140 MS cases and 1140 healthy controls. We found 12 SNPs that replicated, with 7 SNPs showing p-values of less than 10(-5) when the discovery and replication cohorts were combined. All 12 replicated SNPs were in strong linkage disequilibrium with each other. In combination, these data suggest the MERTK gene is a novel risk gene for MS susceptibility.     

Association of short-term memory with a variant within DYX1C1 in developmental dyslexia

A substantial genetic contribution in the etiology of developmental dyslexia (DD) has been well documented with independent groups reporting a susceptibility locus on chromosome 15q. After the identification of the DYX1C1 gene as a potential candidate for DD, several independent association studies reported controversial results. We performed a family-based association study to determine whether the DYX1C1 single nucleotide polymorphisms (SNPs) that have been associated with DD before, that is SNPs '-3GA' and '1249GT', influence a broader phenotypic definition of DD. A significant linkage disequilibrium was observed with 'Single Letter Backward Span' (SLBS) in both single-marker and haplotype analyses. These results provide further support to the association between DD and DYX1C1 and it suggests that the linkage disequilibrium with DYX1C1 is more saliently explained in Italian dyslexics by short-term memory, as measured by 'SLBS', than by the categorical diagnosis of DD or other related phenotypes.     

Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study

Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on single-marker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance.