Contribution of genome-wide significant single-nucleotide polymorphisms and antiretroviral therapy to dyslipidemia in HIV-infected individuals: a longitudinal study

BACKGROUND: HIV-infected individuals have an increased risk of myocardial infarction. Antiretroviral therapy (ART) is regarded as a major determinant of dyslipidemia in HIV-infected individuals. Previous genetic studies have been limited by the validity of the single-nucleotide polymorphisms (SNPs) interrogated and by cross-sectional design. Recent genome-wide association studies have reliably associated common SNPs to dyslipidemia in the general population. METHODS AND RESULTS: We validated the contribution of 42 SNPs (33 identified in genome-wide association studies and 9 previously reported SNPs not included in genome-wide association study chips) and of longitudinally measured key nongenetic variables (ART, underlying conditions, sex, age, ethnicity, and HIV disease parameters) to dyslipidemia in 745 HIV-infected study participants (n=34 565 lipid measurements; median follow-up, 7.6 years). The relative impact of SNPs and ART to lipid variation in the study population and their cumulative influence on sustained dyslipidemia at the level of the individual were calculated. SNPs were associated with lipid changes consistent with genome-wide association study estimates. SNPs explained up to 7.6% (non-high-density lipoprotein cholesterol), 6.2% (high-density lipoprotein cholesterol), and 6.8% (triglycerides) of lipid variation; ART explained 3.9% (non-high-density lipoprotein cholesterol), 1.5% (high-density lipoprotein cholesterol), and 6.2% (triglycerides). An individual with the most dyslipidemic antiretroviral and genetic background had an approximately 3- to 5-fold increased risk of sustained dyslipidemia compared with an individual with the least dyslipidemic therapy and genetic background. CONCLUSIONS: In the HIV-infected population treated with ART, the weight of the contribution of common SNPs and ART to dyslipidemia was similar. When selecting an ART regimen, genetic information should be considered in addition to the dyslipidemic effects of ART agents.

A Multi-Breed Genome-Wide Association Analysis for Canine Hypothyroidism Identifies a Shared Major Risk Locus on CFA12.

Hypothyroidism is a complex clinical condition found in both humans and dogs, thought to be caused by a combination of genetic and environmental factors. In this study we present a multi-breed analysis of predisposing genetic risk factors for hypothyroidism in dogs using three high-risk breeds-the Gordon Setter, Hovawart and the Rhodesian Ridgeback. Using a genome-wide association approach and meta-analysis, we identified a major hypothyroidism risk locus shared by these breeds on chromosome 12 (p = 2.1x10-11). Further characterisation of the candidate region revealed a shared ~167 kb risk haplotype (4,915,018-5,081,823 bp), tagged by two SNPs in almost complete linkage disequilibrium. This breed-shared risk haplotype includes three genes (LHFPL5, SRPK1 and SLC26A8) and does not extend to the dog leukocyte antigen (DLA) class II gene cluster located in the vicinity. These three genes have not been identified as candidate genes for hypothyroid disease previously, but have functions that could potentially contribute to the development of the disease. Our results implicate the potential involvement of novel genes and pathways for the development of canine hypothyroidism, raising new possibilities for screening, breeding programmes and treatments in dogs. This study may also contribute to our understanding of the genetic etiology of human hypothyroid disease, which is one of the most common endocrine disorders in humans.

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

Genome-wide association and genome partitioning reveal novel genomic regions underlying variation in gastrointestinal nematode burden in a wild bird

Acknowledgements This study was funded by a BBSRC studentship (MA Wenzel) and NERC grants NE/H00775X/1 and NE/D000602/1 (SB Piertney). The authors are grateful to Fiona Leckie, Andrew MacColl, Jesús Martínez-Padilla, François Mougeot, Steve Redpath, Pablo Vergara† and Lucy M.I. Webster for samples; Keliya Bai, Daisy Brickhill, Edward Graham, Alyson Little, Daniel Mifsud, Lizzie Molyneux and Mario Röder for fieldwork assistance; Gillian Murray-Dickson and Laura Watt for laboratory assistance; Heather Ritchie for helpful comments on manuscript drafts; and all estate owners, factors and keepers for access to field sites, most particularly Stuart Young and Derek Calder (Edinglassie), Simon Blackett, Jim Davidson and Liam Donald (Invercauld and Glas Choille), Richard Cooke and Fred Taylor† (Invermark) and T. Helps (Catterick).

Genome-wide association studies in oesophageal adenocarcinoma and Barrett's oesophagus: a large-scale meta-analysis

Background: Esophageal adenocarcinoma (EA) is one of the fastest rising cancers in western countries. Barrett’s Esophagus (BE) is the premalignant precursor of EA. However, only a subset of BE patients develop EA, which complicates the clinical management in the absence of valid predictors. Genetic risk factors for BE and EA are incompletely understood. This study aimed to identify novel genetic risk factors for BE and EA.Methods: Within an international consortium of groups involved in the genetics of BE/EA, we performed the first meta-analysis of all genome-wide association studies (GWAS) available, involving 6,167 BE patients, 4,112 EA patients, and 17,159 representative controls, all of European ancestry, genotyped on Illumina high-density SNP-arrays, collected from four separate studies within North America, Europe, and Australia. Meta-analysis was conducted using the fixed-effects inverse variance-weighting approach. We used the standard genome-wide significant threshold of 5×10-8 for this study. We also conducted an association analysis following reweighting of loci using an approach that investigates annotation enrichment among the genome-wide significant loci. The entire GWAS-data set was also analyzed using bioinformatics approaches including functional annotation databases as well as gene-based and pathway-based methods in order to identify pathophysiologically relevant cellular pathways.Findings: We identified eight new associated risk loci for BE and EA, within or near the CFTR (rs17451754, P=4·8×10-10), MSRA (rs17749155, P=5·2×10-10), BLK (rs10108511, P=2·1×10-9), KHDRBS2 (rs62423175, P=3·0×10-9), TPPP/CEP72 (rs9918259, P=3·2×10-9), TMOD1 (rs7852462, P=1·5×10-8), SATB2 (rs139606545, P=2·0×10-8), and HTR3C/ABCC5 genes (rs9823696, P=1·6×10-8). A further novel risk locus at LPA (rs12207195, posteriori probability=0·925) was identified after re-weighting using significantly enriched annotations. This study thereby doubled the number of known risk loci. The strongest disease pathways identified (P<10-6) belong to muscle cell differentiation and to mesenchyme development/differentiation, which fit with current pathophysiological BE/EA concepts. To our knowledge, this study identified for the first time an EA-specific association (rs9823696, P=1·6×10-8) near HTR3C/ABCC5 which is independent of BE development (P=0·45).Interpretation: The identified disease loci and pathways reveal new insights into the etiology of BE and EA. Furthermore, the EA-specific association at HTR3C/ABCC5 may constitute a novel genetic marker for the prediction of transition from BE to EA. Mutations in CFTR, one of the new risk loci identified in this study, cause cystic fibrosis (CF), the most common recessive disorder in Europeans. Gastroesophageal reflux (GER) belongs to the phenotypic CF-spectrum and represents the main risk factor for BE/EA. Thus, the CFTR locus may trigger a common GER-mediated pathophysiology.

Increasing the power of genome wide association studies in natural populations using repeated measures : evaluation and implementation

1. Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful. 2. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden. 3. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response. 4. The method is available in the R package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.

Genome-wide detection of CNVs and their association with meat tenderness in Nelore cattle.

Brazil is one of the largest beef producers and exporters in the world with the Nelore breed representing the vast majority of Brazilian cattle (Bos taurus indicus). Despite the great adaptability of the Nelore breed to tropical climate, meat tenderness (MT) remains to be improved. Several factors including genetic composition can influence MT. In this article, we report a genome-wide analysis of copy number variation (CNV) inferred from Illumina1 High Density SNP-chip data for a Nelore population of 723 males. We detected >2,600 CNV regions (CNVRs) representing 6.5% of the genome. Comparing our results with previous studies revealed an overlap in 1400 CNVRs (>50%). A total of 1,155 CNVRs (43.6%) overlapped 2,750 genes. They were enriched for processes involving guanosine triphosphate (GTP), previously reported to influence skeletal muscle physiology and morphology. Nelore CNVRs also overlapped QTLs for MT reported in other breeds (8.9%, 236 CNVRs) and from a previous study with this population (4.1%, 109 CNVRs). Two CNVRs were also proximal to glutathione metabolism genes that were previously associated with MT. Genome-wide association study of CN state with estimated breeding values derived from meat shear force identified 6 regions, including a region on BTA3 that contains genes of the cAMP and cGMP pathway. Ten CNVRs that overlapped regions associated with MT were successfully validated by qPCR. Our results represent the first comprehensive CNV study in Bos taurus indicus cattle and identify regions in which copy number changes are potentially of importance for the MT phenotype.

A meta-analysis of genome-wide association studies to identify prostate cancer susceptibility loci associated with aggressive and non-aggressive disease

Genome-wide association studies (GWAS) have identified multiple common genetic variants associated with an increased risk of prostate cancer (PrCa), but these explain less than one-third of the heritability. To identify further susceptibility alleles, we conducted a meta-analysis of four GWAS including 5953 cases of aggressive PrCa and 11 463 controls (men without PrCa). We computed association tests for approximately 2.6 million SNPs and followed up the most significant SNPs by genotyping 49 121 samples in 29 studies through the international PRACTICAL and BPC3 consortia. We not only confirmed the association of a PrCa susceptibility locus, rs11672691 on chromosome 19, but also showed an association with aggressive PrCa [odds ratio = 1.12 (95% confidence interval 1.03-1.21), P = 1.4 × 10(-8)]. This report describes a genetic variant which is associated with aggressive PrCa, which is a type of PrCa associated with a poorer prognosis.

A genome-wide analysis of 'Bounty' descendants implicates several novel variants in migraine susceptibility

Migraine is a common neurological disease with a complex genetic aetiology. The disease affects ~12% of the Caucasian population and females are three times more likely than males to be diagnosed. In an effort to identify loci involved in migraine susceptibility, we performed a pedigree-based genome-wide association study of the isolated population of Norfolk Island, which has a high prevalence of migraine. This unique population originates from a small number of British and Polynesian founders who are descendents of the Bounty mutiny and forms a very large multigenerational pedigree (Bellis et al.; Human Genetics, 124(5):543-5542, 2008). These population genetic features may facilitate disease gene mapping strategies (Peltonen et al.; Nat Rev Genet, 1(3):182-90, 2000. In this study, we identified a high heritability of migraine in the Norfolk Island population (h (2) = 0.53, P = 0.016). We performed a pedigree-based GWAS and utilised a statistical and pathological prioritisation approach to implicate a number of variants in migraine. An SNP located in the zinc finger protein 555 (ZNF555) gene (rs4807347) showed evidence of statistical association in our Norfolk Island pedigree (P = 9.6 × 10(-6)) as well as replication in a large independent and unrelated cohort with >500 migraineurs. In addition, we utilised a biological prioritisation to implicate four SNPs, in within the ADARB2 gene, two SNPs within the GRM7 gene and a single SNP in close proximity to a HTR7 gene. Association of SNPs within these neurotransmitter-related genes suggests a disrupted serotoninergic system that is perhaps specific to the Norfolk Island pedigree, but that might provide clues to understanding migraine more generally.

Association study of calcitonin gene-related polypeptide-alpha (CALCA) gene polymorphism with migraine

Migraine is a neurological disorder that is associated with increased levels of calcitonin gene-related peptide (CGRP) in plasma. CGRP, being one of the mediators of neurogenic inflammation and a phenomenon implicated in the pathogenesis of migraine headache, is thus suggested to have an important role in migraine pathophysiology. Polymorphisms of the CALCA gene have been linked to Parkinson's disease, ovarian cancer and essential hypertension, suggesting a functional role for these polymorphisms. Given the strong evidence linking CGRP and migraine, it is hypothesised that polymorphisms in the CALCA gene may play a role in migraine pathogenesis. Seemingly non functional intronic polymorphisms are capable of disrupting normal RNA processing or introducing a splice site in the transcript. A 16 bp deletion in the first intron of the CALCA gene has been reported to be a good match for the binding site for a transcription factor expressed strongly in neural crest derived cells, AP-2. This deletion also eliminates an intron splicing enhancer (ISE) that may potentially cause exon skipping. This study investigated the role of the 16 bp intronic deletion in the CALCA gene in migraineurs and matched control individuals. Six hundred individuals were genotyped for the deletion by polymerase chain reaction followed by fragment analysis on the 3130 Genetic Analyser. The results of this study showed no significant association between the intronic 16 bp deletion in the CALCA gene and migraine in the tested Australian Caucasian population. However, given the evidence linking CGRP and migraine, further investigation of variants with this gene may be warranted.

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Genetic variants underlying risk of endometriosis: Insights from meta-analysis of eight genome-wide association and replication datasets

BACKGROUND Endometriosis is a heritable common gynaecological condition influenced by multiple genetic and environmental factors. Genome-wide association studies (GWASs) have proved successful in identifying common genetic variants of moderate effects for various complex diseases. To date, eight GWAS and replication studies from multiple populations have been published on endometriosis. In this review, we investigate the consistency and heterogeneity of the results across all the studies and their implications for an improved understanding of the aetiology of the condition. METHODS Meta-analyses were conducted on four GWASs and four replication studies including a total of 11 506 cases and 32 678 controls, and on the subset of studies that investigated associations for revised American Fertility Society (rAFS) Stage III/IV including 2859 cases. The datasets included 9039 cases and 27 343 controls of European (Australia, Belgium, Italy, UK, USA) and 2467 cases and 5335 controls of Japanese ancestry. Fixed and Han and Elkin random-effects models, and heterogeneity statistics (Cochran's Q test), were used to investigate the evidence of the nine reported genome-wide significant loci across datasets and populations. RESULTS Meta-analysis showed that seven out of nine loci had consistent directions of effect across studies and populations, and six out of nine remained genome-wide significant (P < 5 × 10(-8)), including rs12700667 on 7p15.2 (P = 1.6 × 10(-9)), rs7521902 near WNT4 (P = 1.8 × 10(-15)), rs10859871 near VEZT (P = 4.7 × 10(-15)), rs1537377 near CDKN2B-AS1 (P = 1.5 × 10(-8)), rs7739264 near ID4 (P = 6.2 × 10(-10)) and rs13394619 in GREB1 (P = 4.5 × 10(-8)). In addition to the six loci, two showed borderline genome-wide significant associations with Stage III/IV endometriosis, including rs1250248 in FN1 (P = 8 × 10(-8)) and rs4141819 on 2p14 (P = 9.2 × 10(-8)). Two independent inter-genic loci, rs4141819 and rs6734792 on chromosome 2, showed significant evidence of heterogeneity across datasets (P < 0.005). Eight of the nine loci had stronger effect sizes among Stage III/IV cases, implying that they are likely to be implicated in the development of moderate to severe, or ovarian, disease. While three out of nine loci were inter-genic, the remaining were in or near genes with known functions of biological relevance to endometriosis, varying from roles in developmental pathways to cellular growth/carcinogenesis. CONCLUSIONS Our meta-analysis shows remarkable consistency in endometriosis GWAS results across studies, with little evidence of population-based heterogeneity. They also show that the phenotypic classifications used in GWAS to date have been limited. Stronger associations with Stage III/IV disease observed for most loci emphasize the importance for future studies to include detailed sub-phenotype information. Functional studies in relevant tissues are needed to understand the effect of the variants on downstream biological pathways.

Genetic clustering on the hippocampal surface for genome-wide association studies

Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (r(g)) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.

Correction: Genome-wide association studies of asthma in population-based cohorts confirm known and suggested loci and identify an additional association near HLA

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