Genome-wide association study of virologic response with efavirenz-containing or abacavir-containing regimens in AIDS clinical trials group protocols.

BACKGROUND: Efavirenz and abacavir are components of recommended first-line regimens for HIV-1 infection. We used genome-wide genotyping and clinical data to explore genetic associations with virologic failure among patients randomized to efavirenz-containing or abacavir-containing regimens in AIDS Clinical Trials Group (ACTG) protocols. PARTICIPANTS AND METHODS: Virologic response and genome-wide genotype data were available from treatment-naive patients randomized to efavirenz-containing (n=1596) or abacavir-containing (n=786) regimens in ACTG protocols 384, A5142, A5095, and A5202. RESULTS: Meta-analysis of association results across race/ethnic groups showed no genome-wide significant associations (P<5×10) with virologic response for either efavirenz or abacavir. Our sample size provided 80% power to detect a genotype relative risk of 1.8 for efavirenz and 2.4 for abacavir. Analyses focused on CYP2B genotypes that define the lowest plasma efavirenz exposure stratum did not show associations nor did analysis limited to gene sets predicted to be relevant to efavirenz and abacavir disposition. CONCLUSION: No single polymorphism is associated strongly with virologic failure with efavirenz-containing or abacavir-containing regimens. Analyses to better consider context, and that minimize confounding by nongenetic factors, may show associations not apparent here.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Evaluation of the use of high-density SNP genotyping to implement UPOV Model 2 for DUS testing in barley

Developments in high-throughput genotyping provide an opportunity to explore the application of marker technology in distinctness, uniformity and stability (DUS) testing of new varieties. We have used a large set of molecular markers to assess the feasibility of a UPOV Model 2 approach: “Calibration of threshold levels for molecular characteristics against the minimum distance in traditional characteristics”. We have examined 431 winter and spring barley varieties, with data from UK DUS trials comprising 28 characteristics, together with genotype data from 3072 SNP markers. Inter varietal distances were calculated and we found higher correlations between molecular and morphological distances than have been previously reported. When varieties were grouped by kinship, phenotypic and genotypic distances of these groups correlated well. We estimated the minimum marker numbers required and showed there was a ceiling after which the correlations do not improve. To investigate the possibility of breaking through this ceiling, we attempted genomic prediction of phenotypes from genotypes and higher correlations were achieved. We tested distinctness decisions made using either morphological or genotypic distances and found poor correspondence between each method.

Morphological and Genomic Differences Between Cutting and Racing Lines of Quarter Horses

To investigate morphological and genomic differences between cutting and racing lines of Quarter Horses, 120 racing and 68 cutting animals of both sexes, registered at the Brazilian Association of Quarter Horse Breeders, were used. Blood samples were collected, and the following physical traits were measured: weight; height at withers; body length; length of the shank, pastern, rump, head, and neck; and chest, shank, and hoof circumference. For analysis of genomic differences, 54,602 single-nucleotide polymorphisms (SNPs) were genotyped using the Equine SNP50 BeadChip, and the quality of individual and SNP genotype data were evaluated. The fixation index, FST, was used to identify genome regions that were altered in the lines by selection. The results showed significant differences between the lines in all physical traits. Quality control led to the exclusion of four cutting animals with a call rate of <0.95. After filtering, 12,544, 13,815, and 13,370 SNPs were excluded for the whole population (n = 184), the 120 racing animals, and the 64 cutting animals, respectively. The number of informative polymorphisms detected in each line and in the whole population indicated that the Equine SNP50 BeadChip can be used in genetic studies of Quarter Horses. The fixation index, FST, identified 2,558 genome regions that may have been modified by divergent selection. © 2013 Elsevier Inc.

USE OF HIDDEN MARKOV MODELS FOR QTL MAPPING

An important aspect of the QTL mapping problem is the treatment of missing genotype data. If complete genotype data were available, QTL mapping would reduce to the problem of model selection in linear regression. However, in the consideration of loci in the intervals between the available genetic markers, genotype data is inherently missing. Even at the typed genetic markers, genotype data is seldom complete, as a result of failures in the genotyping assays or for the sake of economy (for example, in the case of selective genotyping, where only individuals with extreme phenotypes are genotyped). We discuss the use of algorithms developed for hidden Markov models (HMMs) to deal with the missing genotype data problem.

TRIO LOGIC REGRESSION - DETECTION OF SNP - SNP INTERACTIONS IN CASE-PARENT TRIOS

Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al., 2001; Ruczinski et al., 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. In this manuscript we extend the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. Trio logic regression accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We also derive an efficient algorithm to simulate case-parent trios where genetic risk is determined via epistatic interactions.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Contribution of the genes of the renin-angiotensin system to interindividual differences in blood pressure levels in Caucasians from Rochester, Minnesota

The Renin-Angiotensin system (RAS) regulates blood pressure through its effects on vascular tone, renal hemodynamics, and renal sodium and fluid balance. The genes encoding the four major components of the RAS, angiotensinogen, renin, angiotensin I-converting enzyme (ACE), and angiotensin II receptor type 1 (AT1), have been investigated as candidate genes in the pathogenesis of essential hypertension. However, studies have primarily focused on small samples of diseased individuals, and, therefore, have provided little information about the determinants of interindividual variation in blood pressure (BP) in the general population.^ Using data from a large population-based sample from Rochester, MN, I have evaluated the contribution of variation in the region of the RAS genes to interindividual variation in systolic, diastolic, and mean arterial pressure in the population-at-large. Marker genotype data from four polymorphisms located within or very near these genes were first collected on 3,974 individuals from 583 randomly ascertained three-generation pedigrees. Haseman-Elston regression and variance component methods of linkage analysis were then carried out to estimate the proportion of interindividual variance in BP attributable to the effects of variation at these four measured loci.^ A significant effect of the ACE locus on interindividual variation in mean arterial pressure (MAP) was detected in a sample of siblings belonging to the youngest generation. After allowing for measured covariates, this effect accounted for 15-25% of the interindividual variance in MAP, and was even greater in a subset with a positive family history of hypertension. When gender-specific analyses were carried out, this effect was significant in males but not in females. Extended pedigree analyses also provided evidence for an effect of the ACE locus on interindividual variation in MAP, but no difference between males and females was observed. Circumstantial evidence suggests that the ACE gene itself may be responsible for the observed effects on BP, although the possibility that other genes in the region may be at play cannot be excluded.^ No definitive evidence for an effect of the renin, angiotensinogen, or AT1 loci on interindividual variation in BP was obtained in this study, suggesting that the impact of these genes on BP may not be great in the Caucasian population-at-large. However, this does not preclude a larger effect of these genes in some subsets of individuals, especially among those with clinically manifest hypertension or coronary heart disease, or in other populations. ^

The brown coat colour of Coppernecked goats is associated with a non-synonymous variant at the TYRP1 locus on chromosome 8

The recent development of a goat SNP genotyping microarray enables genome-wide association studies in this important livestock species. We investigated the genetic basis of the black and brown coat colour in Valais Blacknecked and Coppernecked goats. A genome-wide association analysis using goat SNP50 BeadChip genotypes of 22 cases and 23 controls allowed us to map the locus for the brown coat colour to goat chromosome 8. The TYRP1 gene is located within the associated chromosomal region, and TYRP1 variants cause similar coat colour phenotypes in different species. We thus considered TYRP1 as a strong positional and functional candidate. We resequenced the caprine TYRP1 gene by Sanger and Illumina sequencing and identified two non-synonymous variants, p.Ile478Thr and p.Gly496Asp, that might have a functional impact on the TYRP1 protein. However, based on the obtained pedigree and genotype data, the brown coat colour in these goats is not due to a single recessive loss-of-function allele. Surprisingly, the genotype distribution and the pedigree data suggest that the (496) Asp allele might possibly act in a dominant manner. The (496) Asp allele was present in 77 of 81 investigated Coppernecked goats and did not occur in black goats. This strongly suggests heterogeneity underlying the brown coat colour in Coppernecked goats. Functional experiments or targeted matings will be required to verify the unexpected preliminary findings.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

The role of parasite-driven selection in shaping landscape genomic structure in red grouse (Lagopus lagopus scotica)

This article is protected by copyright. All rights reserved. Acknowledgements This study was funded by a BBSRC studentship (MAW) and NERC grants NE/H00775X/1 and NE/D000602/1 (SBP). The authors are grateful to Mario Röder and Keliya Bai for fieldwork assistance, and all estate owners, factors and keepers for access to field sites, most particularly MJ Taylor and Mike Nisbet (Airlie), Neil Brown (Allargue), RR Gledson and David Scrimgeour (Delnadamph), Andrew Salvesen and John Hay (Dinnet), Stuart Young and Derek Calder (Edinglassie), Kirsty Donald and David Busfield (Glen Dye), Neil Hogbin and Ab Taylor (Glen Muick), Alistair Mitchell (Glenlivet), Simon Blackett, Jim Davidson and Liam Donald (Invercauld), Richard Cooke and Fred Taylor† (Invermark), Shaila Rao and Christopher Murphy (Mar Lodge), and Ralph Peters and Philip Astor (Tillypronie). Data accessibility • Genotype data (DataDryad: doi:10.5061/dryad.4t7jk) • Metadata (information on sampling sites, phenotypes and medication regimen) (DataDryad: doi:10.5061/dryad.4t7jk)

A Validation Study of Vascular Cognitive Impairment Genetics Meta-Analysis Findings in an Independent Collaborative Cohort

Vascular cognitive impairment (VCI), including its severe form, vascular dementia (VaD), is the second most common form of dementia. The genetic etiology of sporadic VCI remains largely unknown. We previously conducted a systematic review and meta-analysis of all published genetic association studies of sporadic VCI prior to 6 July 2012, which demonstrated that APOE (ɛ4, ɛ2) and MTHFR (rs1801133) variants were associated with susceptibility for VCI. De novo genotyping was conducted in a new independent relatively large collaborative European cohort of VaD (nmax = 549) and elderly non-demented samples (nmax = 552). Where available, genotype data derived from Illumina's 610-quad array for 1210 GERAD1 control samples were also included in analyses of genes examined. Associations were tested using the Cochran-Armitage trend test: MTHFR rs1801133 (OR = 1.36, 95% CI 1.16-1.58, p = <0.0001), APOE rs7412 (OR = 0.62, 95% CI 0.42-0.90, p = 0.01), and APOE rs429358 (OR = 1.59, 95% CI 1.17-2.16, p = 0.003). Association was also observed with APOE epsilon alleles; ɛ4 (OR = 1.85, 95% CI 1.35-2.52, p = <0.0001) and ɛ2 (OR = 0.67, 95% CI 0.46-0.98, p = 0.03). Logistic Regression and Bonferroni correction in a subgroup of the cohort adjusted for gender, age, and population maintained the association of APOE rs429358 and ɛ4 allele.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Reliable selfing rate estimates from imperfect population genetic data.

Genotypic frequencies at codominant marker loci in population samples convey information on mating systems. A classical way to extract this information is to measure heterozygote deficiencies (FIS) and obtain the selfing rate s from FIS = s/(2 - s), assuming inbreeding equilibrium. A major drawback is that heterozygote deficiencies are often present without selfing, owing largely to technical artefacts such as null alleles or partial dominance. We show here that, in the absence of gametic disequilibrium, the multilocus structure can be used to derive estimates of s independent of FIS and free of technical biases. Their statistical power and precision are comparable to those of FIS, although they are sensitive to certain types of gametic disequilibria, a bias shared with progeny-array methods but not FIS. We analyse four real data sets spanning a range of mating systems. In two examples, we obtain s = 0 despite positive FIS, strongly suggesting that the latter are artefactual. In the remaining examples, all estimates are consistent. All the computations have been implemented in a open-access and user-friendly software called rmes (robust multilocus estimate of selfing) available at http://ftp.cefe.cnrs.fr, and can be used on any multilocus data. Being able to extract the reliable information from imperfect data, our method opens the way to make use of the ever-growing number of published population genetic studies, in addition to the more demanding progeny-array approaches, to investigate selfing rates.

Tracking Invasion Histories in the Sea: Facing Complex Scenarios Using Multilocus Data

In recent years, new analytical tools have allowed researchers to extract historical information contained in molecular data, which has fundamentally transformed our understanding of processes ruling biological invasions. However, the use of these new analytical tools has been largely restricted to studies of terrestrial organisms despite the growing recognition that the sea contains ecosystems that are amongst the most heavily affected by biological invasions, and that marine invasion histories are often remarkably complex. Here, we studied the routes of invasion and colonisation histories of an invasive marine invertebrate Microcosmus squamiger (Ascidiacea) using microsatellite loci, mitochondrial DNA sequence data and 11 worldwide populations. Discriminant analysis of principal components, clustering methods and approximate Bayesian computation (ABC) methods showed that the most likely source of the introduced populations was a single admixture event that involved populations from two genetically differentiated ancestral regions - the western and eastern coasts of Australia. The ABC analyses revealed that colonisation of the introduced range of M. squamiger consisted of a series of non-independent introductions along the coastlines of Africa, North America and Europe. Furthermore, we inferred that the sequence of colonisation across continents was in line with historical taxonomic records - first the Mediterranean Sea and South Africa from an unsampled ancestral population, followed by sequential introductions in California and, more recently, the NE Atlantic Ocean. We revealed the most likely invasion history for world populations of M. squamiger, which is broadly characterized by the presence of multiple ancestral sources and non-independent introductions within the introduced range. The results presented here illustrate the complexity of marine invasion routes and identify a cause-effect relationship between human-mediated transport and the success of widespread marine non-indigenous species, which benefit from stepping-stone invasions and admixture processes involving different sources for the spread and expansion of their range.