Addition of multiple rare SNPs to known common variants improves the association between disease and gene in the Genetic Analysis Workshop 17 data

The upcoming release of new whole-genome genotyping technologies will shed new light on whether there is an associative effect of previously immeasurable rare variants on incidence of disease. For Genetic Analysis Workshop 17, our team focused on a statistical method to detect associations between gene-based multiple rare variants and disease status. We added a combination of rare SNPs to a common variant shown to have an influence on disease status. This method provides us with an enhanced ability to detect the effect of these rare variants, which, modeled alone, would normally be undetectable. Adjusting for significant clinical parameters, several genes were found to have multiple rare variants that were significantly associated with disease outcome

Genetic approaches to understanding the causes of stuttering

Stuttering is a common but poorly understood speech disorder. Evidence accumulated over the past several decades has indicated that genetic factors are involved, and genetic linkage studies have begun to identify specific chromosomal loci at which causative genes are likely to reside. A detailed investigation of one such region on chromosome 12 has identified mutations in the GNPTAB gene that are associated with stuttering in large families and in the general population. Subsequent studies identified mutations in the functionally related GNPTG and NAGPA genes. Mutations in these genes disrupt the lysosomal targeting pathway that generates the Mannose 6-phosphate signal, which directs a diverse group of enzymes to their target location in the lysosome of the cell. While mutations in these three genes can be identified in less than 10% of cases of familial stuttering, this knowledge allows a variety of new studies that can help identify the neuropathology that underlies this disorder

Common variants in the regulative regions of GRIA1 and GRIA3 receptor genes are associated with migraine susceptibility

<p>Abstract</p> <p>Background</p> <p>Glutamate is the principal excitatory neurotransmitter in the central nervous system which acts by the activation of either ionotropic (AMPA, NMDA and kainate receptors) or G-protein coupled metabotropic receptors. Glutamate is widely accepted to play a major role in the path physiology of migraine as implicated by data from animal and human studies. Genes involved in synthesis, metabolism and regulation of both glutamate and its receptors could be, therefore, considered as potential candidates for causing/predisposing to migraine when mutated.</p> <p>Methods</p> <p>The association of polymorphic variants of <it>GRIA1</it>-<it>GRIA4 </it>genes which encode for the four subunits (GluR1-GluR4) of the alpha-amino-3- hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptor for glutamate was tested in migraineurs with and without aura (MA and MO) and healthy controls.</p> <p>Results</p> <p>Two variants in the regulative regions of <it>GRIA1 </it>(rs2195450) and <it>GRIA3 </it>(rs3761555) genes resulted strongly associated with MA (P = 0.00002 and P = 0.0001, respectively), but not associated with MO, suggesting their role in cortical spreading depression. Whereas the rs548294 variant in <it>GRIA1 </it>gene showed association primarily with MO phenotype, supporting the hypothesis that MA and MO phenotypes could be genetically related. These variants modify binding sites for transcription factors altering the expression of <it>GRIA1 </it>and <it>GRIA3 </it>genes in different conditions.</p> <p>Conclusions</p> <p>This study represents the first genetic evidence of a link between glutamate receptors and migraine.</p

Preterm Birth in Caucasians Is Associated with Coagulation and Inflammation Pathway Gene Variants

Spontaneous preterm birth (<37 weeks gestation—PTB) occurs in ∼12% of pregnancies in the United States, and is the largest contributor to neonatal morbidity and mortality. PTB is a complex disease, potentially induced by several etiologic factors from multiple pathophysiologic pathways. To dissect the genetic risk factors of PTB a large-scale high-throughput candidate gene association study was performed examining 1536 SNP in 130 candidate genes from hypothesized PTB pathways. Maternal and fetal DNA from 370 US Caucasian birth-events (172 cases and 198 controls) was examined. Single locus, haplotype, and multi-locus association analyses were performed separately on maternal and fetal data. For maternal data the strongest associations were found in genes in the complement-coagulation pathway related to decidual hemorrhage in PTB. In this pathway 3 of 6 genes examined had SNPs significantly associated with PTB. These include factor V (FV) that was previously associated with PTB, factor VII (FVII), and tissue plasminogen activator (tPA). The single strongest effect was observed in tPA marker rs879293 with a significant allelic (p = 2.30×10−3) and genotypic association (p = 2.0×10−6) with PTB. The odds ratio (OR) for this SNP was 2.80 [CI 1.77–4.44] for a recessive model. Given that 6 of 8 markers in tPA were statistically significant, sliding window haplotype analyses were performed and revealed an associating 4 marker haplotype in tPA (p = 6.00×10−3). The single strongest effect in fetal DNA was observed in the inflammatory pathway at rs17121510 in the interleukin-10 receptor antagonist (IL-10RA) gene for allele (p = 0.01) and genotype (p = 3.34×10−4). The OR for the IL-10RA genotypic additive model was 1.92 [CI 1.15–3.19] (p = 2.00×10−3). Finally, exploratory multi-locus analyses in the complement and coagulation pathway were performed and revealed a potentially significant interaction between a marker in FV (rs2187952) and FVII (rs3211719) (p<0.001). These results support a role for genes in both the coagulation and inflammation pathways, and potentially different maternal and fetal genetic risks for PTB

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas)

PedHunter 2.0 and its usage to characterize the founder structure of the Old Order Amish of Lancaster County

<p>Abstract</p> <p>Background</p> <p>Because they are a closed founder population, the Old Order Amish (OOA) of Lancaster County have been the subject of many medical genetics studies. We constructed four versions of Anabaptist Genealogy Database (AGDB) using three sources of genealogies and multiple updates. In addition, we developed PedHunter, a suite of query software that can solve pedigree-related problems automatically and systematically.</p> <p>Methods</p> <p>We report on how we have used new features in PedHunter to quantify the number and expected genetic contribution of founders to the OOA. The queries and utility of PedHunter programs are illustrated by examples using AGDB in this paper. For example, we calculated the number of founders expected to be contributing genetic material to the present-day living OOA and estimated the mean relative founder representation for each founder. New features in PedHunter also include pedigree trimming and pedigree renumbering, which should prove useful for studying large pedigrees.</p> <p>Results</p> <p>With PedHunter version 2.0 querying AGDB version 4.0, we identified 34,160 presumed living OOA individuals and connected them into a 14-generation pedigree descending from 554 founders (332 females and 222 males) after trimming. From the analysis of cumulative mean relative founder representation, 128 founders (78 females and 50 males) accounted for over 95% of the mean relative founder contribution among living OOA descendants.</p> <p>Discussion/Conclusions</p> <p>The OOA are a closed founder population in which a modest number of founders account for the genetic variation present in the current OOA population. Improvements to the PedHunter software will be useful in future studies of both the OOA and other populations with large and computerized genealogies.</p

FTO gene polymorphisms and obesity risk: a meta-analysis

<p>Abstract</p> <p>Background</p> <p>The pathogenesis of obesity is reportedly related to variations in the fat mass and an obesity-associated gene (<it>FTO</it>); however, as the number of reports increases, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect sizes in each ethnic group. In addition, some reports have claimed ethnic-specific associations with alternative SNPs, and to that end there has been a degree of confusion.</p> <p>Methods</p> <p>We searched PubMed, MEDLINE, Web of Science, EMBASE, and BIOSIS Preview to identify studies investigating the associations between the five polymorphisms and obesity risk. Individual study odds ratios (OR) and their 95% confidence intervals (CI) were estimated using per-allele comparison. Summary ORs were estimated using a random effects model.</p> <p>Results</p> <p>We identified 59 eligible case-control studies in 27 articles, investigating 41,734 obesity cases and 69,837 healthy controls. Significant associations were detected between obesity risk and the five polymorphisms: rs9939609 (OR: 1.31, 95% CI: 1.26 to 1.36), rs1421085 (OR: 1.43, 95% CI: 1.33 to 1.53), rs8050136 (OR: 1.25, 95% CI: 1.13 to 1.38), rs17817449 (OR: 1.54, 95% CI: 1.41 to 1.68), and rs1121980 (OR: 1.34, 95% CI: 1.10 to 1.62). Begg's and Egger's tests provided no evidence of publication bias for the polymorphisms except rs1121980. There is evidence of higher heterogeneity, with <it>I</it>²test values ranging from 38.1% to 84.5%.</p> <p>Conclusions</p> <p>This meta-analysis suggests that <it>FTO </it>may represent a low-penetrance susceptible gene for obesity risk. Individual studies with large sample size are needed to further evaluate the associations between the polymorphisms and obesity risk in various ethnic populations.</p

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses