Project description:PurposeTo present genome-wide association analyses of genotypic and environmental risks on age-related macular degeneration (AMD) using 593 subjects from the age-related eye disease study (AREDS), after adjusting for population stratification and including questionable controls.MethodsSingle nucleotide polymorphism (SNP) associations with AMD for the non-Hispanic white population were investigated using a log-additive model after adjusting for population stratification. Replication of possible SNP-disease association was performed by genotyping an independent group of 444 AMD case and 300 control subjects. Logistic regression models were used to assess interaction effects between smoking and SNPs associated with AMD. Independent genetic risk effects among the disease-associated SNPs were also investigated using multiple logistic regression models.ResultsPopulation stratification was observed among the individuals having a self-reported race of non-Hispanic white. Risk allele frequencies at established AMD loci demonstrated that questionable control subjects were similar to control subjects in the AREDS, suggesting that they could be used as true controls in the analyses. Genetic loci (complement factor H [CFH], complement factor B [CFB], the age-related maculopathy susceptibility 2 locus containing the hypothetical gene [LOC387715]/the high-temperature requirement A-1 [HTRA1], and complement component 3 [C3]) that were already known to be associated with AMD were identified. An additional 26 novel SNPs potentially associated with AMD were identified, but none were definitely replicated in a second independent group of subjects. Smoking did not interact with known AMD loci, but was associated with late AMD. Statistically independent genetic signals were observed within the Pleckstrin homology domain-containing family A member 1 (PLEKHA1) region near LOC387715/HTRA1 and within a haplotype spanning exon 19 of the C3 gene.ConclusionsPopulation stratification among Caucasian subjects from the multicentered AREDS was observed, suggesting that it should be adjusted for in future studies. The AREDS questionable control subjects can be used as control subjects in the AREDS genome-wide association study (GWAS). Smoking was an independent risk factor for advanced AMD in the AREDS subjects. There continues to be evidence that the 10q26 (age-related maculopathy susceptibility 2 gene [ARMS2]) locus spanning PLEKHA1-LOC387715-HTRA1 and the C3 gene may contain multiple independent genetic risks contributing to AMD.

Project description:Migraine is associated with an increased risk for cardiovascular disease (CVD). Both migraine and CVD are highly heritable. However, the genetic liability for CVD among migraineurs is unclear.We performed a genome-wide association study for incident CVD events during 12 years of follow-up among 5,122 migraineurs participating in the population-based Women's Genome Health Study. Migraine was self-reported and CVD events were confirmed after medical records review. We calculated odds ratios (OR) and 95% confidence intervals (CI) and considered a genome-wide p-value <5×10(-8) as significant.Among the 5,122 women with migraine 164 incident CVD events occurred during follow-up. No SNP was associated with major CVD, ischemic stroke, myocardial infarction, or CVD death at the genome-wide level; however, five SNPs showed association with p<5×10(-6). Among migraineurs with aura rs7698623 in MEPE (OR?=?6.37; 95% CI 3.15-12.90; p?=?2.7×10(-7)) and rs4975709 in IRX4 (OR?=?5.06; 95% CI 2.66-9.62; p?=?7.7×10(-7)) appeared to be associated with ischemic stroke, rs2143678 located close to MDF1 with major CVD (OR?=?3.05; 95% CI 1.98-4.69; p?=?4.3×10(-7)), and the intergenic rs1406961 with CVD death (OR?=?12.33; 95% CI 4.62-32.87; p?=?5.2×10(-7)). Further, rs1047964 in BACE1 appeared to be associated with CVD death among women with any migraine (OR?=?4.67; 95% CI 2.53-8.62; p?=?8.0×10(-7)).Our results provide some suggestion for an association of five SNPs with CVD events among women with migraine; none of the results was genome-wide significant. Four associations appeared among migraineurs with aura, two of those with ischemic stroke. Although our population is among the largest with migraine and incident CVD information, these results must be treated with caution, given the limited number of CVD events among women with migraine and the low minor allele frequencies for three of the SNPs. Our results await independent replication and should be considered hypothesis generating for future research.

Project description:Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.

Project description:A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.

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A genome-wide association study of treated A1C: a genetic needle in an environmental haystack?

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