Laura K. Vaughan

Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model.

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form “semiparametric” method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.

Review and Evaluation of Methods Correcting for Population Stratification with a Focus on Underlying Statistical Principles

When two or more populations have been separated by geographic or cultural boundaries for many generations, drift, spontaneous mutations, differential selection pressures and other factors may lead to allele frequency differences among populations. If these ‘parental’ populations subsequently come together and begin inter-mating, disequilibrium among linked markers may span a greater genetic distance than it typically does among populations under panmixia [see glossary]. This extended disequilibrium can make association studies highly effective and more economical than disequilibrium mapping in panmictic populations since less marker loci are needed to detect regions of the genome that harbor phenotype-influencing loci. However, under some circumstances, this process of intermating (as well as other processes) can produce disequilibrium between pairs of unlinked loci and thus create the possibility of confounding or spurious associations due to this population stratification. Accordingly, researchers are advised to employ valid statistical tests for linkage disequilibrium mapping allowing conduct of genetic association studies that control for such confounding. Many recent papers have addressed this need. We provide a comprehensive review of advances made in recent years in correcting for population stratification and then evaluate and synthesize these methods based on statistical principles such as (1) randomization, (2) conditioning on sufficient statistics, and (3) identifying whether the method is based on testing the genotype-phenotype covariance (conditional upon familial information) and/or testing departures of the marginal distribution from the expected genotypic frequencies.

Expression Signature of IFN/STAT1 Signaling Genes Predicts Poor Survival Outcome in Glioblastoma Multiforme in a Subtype-Specific Manner

Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.

Genetic Variation in NCAM1 Contributes to Left Ventricular Wall Thickness in Hypertensive Families

Rationale: Left ventricular (LV) mass and related phenotypes are heritable, important predictors of cardiovascular disease, particularly in hypertensive individuals. Objective: Identify genetic predictors of echocardiographic phenotypes in hypertensive families. Methods and Results: A multistage genome-wide association study (GWAS) was conducted in hypertensive-ascertained black families (HyperGEN, stage I; GENOA, stage II); findings were replicated in HyperGEN white families (stage III). Echocardiograms were collected using a common protocol, and participants were genotyped with the Affymetrix Genome-Wide Human SNP 6.0 Array. The following were analyzed using mixed models adjusted for ancestry: in stages I and II, 1258 and 989 blacks, respectively; and in stage III, 1316 whites. Phenotypes included LV mass, LV internal dimension (LVID), wall thicknesses (posterior [PWT] and intraventricular septum [IVST]), and relative wall thickness (RWT). In stage I, 5 single nucleotide polymorphisms (SNPs) had P⩽10−6. In stage II, 1 SNP (rs1436109; NCAM1 intron 1) replicated with the same phenotype (PWT, P=0.025) in addition to RWT (P=0.032). In stage III, rs1436109 was associated with RWT (P=5.47×10−4) and LVID (P=1.86×10−4). Fisher combined probability value for all stages was RWT=3.80×10−9, PWT=3.12×10−7, IVST=8.69×10−7, LV mass=2.52×10−3, and LVID=4.80×10−4. Conclusions: This GWAS conducted in hypertensive families identified a variant in NCAM1 associated with LV wall thickness and RWT. NCAM is upregulated during the remodeling period of hypertrophy to heart failure in Dahl salt–sensitive rats. Our initial screening in hypertensive blacks may have provided the context for this novel locus.

Correcting for Measurement Error in Individual Ancestry Estimates in Structured Association Tests

We present theoretical explanations and show through simulation that the individual admixture proportion estimates obtained by using ancestry informative markers should be seen as an error-contaminated measurement of the underlying individual ancestry proportion. These estimates can be used in structured association tests as a control variable to limit type I error inflation or reduce loss of power due to population stratification observed in studies of admixed populations. However, the inclusion of such error-containing variables as covariates in regression models can bias parameter estimates and reduce ability to control for the confounding effect of admixture in genetic association tests. Measurement error correction methods offer a way to overcome this problem but require an a priori estimate of the measurement error variance. We show how an upper bound of this variance can be obtained, present four measurement error correction methods that are applicable to this problem, and conduct a simulation study to compare their utility in the case where the admixed population results from the intermating between two ancestral populations. Our results show that the quadratic measurement error correction (QMEC) method performs better than the other methods and maintains the type I error to its nominal level.

Comparing self-reported ethnicity to genetic background measures in the context of the Multi-Ethnic Study of Atherosclerosis (MESA)

BackgroundQuestions remain regarding the utility of self-reported ethnicity (SRE) in genetic and epidemiologic research. It is not clear whether conditioning on SRE provides adequate protection from inflated type I error rates due to population stratification and admixture. We address this question using data obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), which enrolled individuals from 4 self-reported ethnic groups. We compare the agreement between SRE and genetic based measures of ancestry (GBMA), and conduct simulation studies based on observed MESA data to evaluate the performance of each measure under various conditions.ResultsFour clusters are identified using 96 ancestry informative markers. Three of these clusters are well delineated, but 30% of the self-reported Hispanic-Americans are misclassified. We also found that MESA SRE provides type I error rates that are consistent with the nominal levels. More extensive simulations revealed that this finding is likely due to the multi-ethnic nature of the MESA. Finally, we describe situations where SRE may perform as well as a GBMA in controlling the effect of population stratification and admixture in association tests.ConclusionsThe performance of SRE as a control variable in genetic association tests is more nuanced than previously thought, and may have more value than it is currently credited with, especially when smaller replication studies are being considered in multi-ethnic samples.

The use of plasmodes as a supplement to simulations: A simple example evaluating individual admixture estimation methodologies

With the advent of powerful computers, simulation studies are becoming an important tool in statistical methodology research. However, computer simulations of a specific process are only as good as our understanding of the underlying mechanisms. An attractive supplement to simulations is the use of plasmode datasets. Plasmodes are data sets that are generated by natural biologic processes, under experimental conditions that allow some aspect of the truth to be known. The benefit of the plasmode approach is that the data are generated through completely natural processes, thus circumventing the common concern of the realism and accuracy of computer simulated data. The estimation of admixture, or the proportion of an individuals genome that originates from different founding populations, is a particularly difficult research endeavor that is well suited to the use of plasmodes. Current methods have been tested with simulations of complex populations where the underlying mechanisms such as the rate and distribution of recombination are not well understood. To demonstrate the utility of this method data derived from mouse crosses is used to evaluate the effectiveness of several admixture estimation methodologies. Each cross shares a common founding population so that the ancestry proportion for each individual is known, allowing for the comparison of true and estimated individual admixture values. Analysis shows that the different estimation methodologies (Structure, AdmixMap and FRAPPE) examined all perform well with simple datasets. However, the performance of the estimation methodologies varied greatly when applied to a plasmode consisting of three founding populations. The results of these examples illustrate the utility of plasmodes in the evaluation of statistical genetics methodologies.

Genes Linked to Energy Metabolism and Immunoregulatory Mechanisms are Associated with Subcutaneous Adipose Tissue Distribution in HIV-infected Men

Objective Genetic studies may help explain abnormalities of fat distribution in HIV-infected patients treated with antiretroviral therapy (ARV). Methods Subcutaneous adipose tissue (SAT) volume measured by MRI in the leg, the lower trunk, the upper trunk, and the arm was examined in 192 HIV-infected White men, ARV-treated from the Fat Redistribution and Metabolic Change in HIV infection study. Single-nucleotide polymorphisms were assayed using the Illumina Human CNV370-quad beadchip. Multivariate and univariate genome-wide association analyses of the four SAT depots were implemented in PLINK software adjusted for age and ARV duration. Functional annotation analysis using Ingenuity Systems Pathway Analysis tool was carried out for markers with P lower than 10−3 near known genes identified by multivariate analysis. Results Loci (rs10504906, rs13267998, rs921231) in or near the anion exchanger solute carrier family 26, member 7 isoform a (SLC26A7) were strongly associated with the upper trunk and the arm SAT (9.8×10−7⩽P<7.8×10−6). Loci (rs193139, rs7523050, rs1761621) in and near a gene-rich region including G-protein-signaling modulator 2 (GPSM2) and syntaxin-binding protein 3 (STXBP3) were significantly associated with the lower body SAT depots (9.9×10−7⩽P<9.5×10−6). GPSM2 is associated with cell division and cancer whereas STXBP3 is associated with glucose metabolism in adipoctyes. Ingenuity Systems Pathway Analysis identified atherosclerosis, mitochondrial function, and T-cell-mediated apoptosis as processes related to SAT volume in HIV-infected individuals (P<5×10−3). Conclusion Our results are limited by the small sample size and replication is needed; however, this genomic scan uncovered new genes associated with metabolism and inflammatory pathways that may affect SAT volume in ARV-treated HIV-infected patients.

A unified framework for multi-locus association analysis of both common and rare variants

BackgroundCommon, complex diseases are hypothesized to result from a combination of common and rare genetic variants. We developed a unified framework for the joint association testing of both types of variants. Within the framework, we developed a union-intersection test suitable for genome-wide analysis of single nucleotide polymorphisms (SNPs), candidate gene data, as well as medical sequencing data. The union-intersection test is a composite test of association of genotype frequencies and differential correlation among markers.ResultsWe demonstrated by computer simulation that the false positive error rate was controlled at the expected level. We also demonstrated scenarios in which the multi-locus test was more powerful than traditional single marker analysis. To illustrate use of the union-intersection test with real data, we analyzed a publically available data set of 319,813 autosomal SNPs genotyped for 938 cases of Parkinson disease and 863 neurologically normal controls for which no genome-wide significant results were found by traditional single marker analysis. We also analyzed an independent follow-up sample of 183 cases and 248 controls for replication.ConclusionsWe identified a single risk haplotype with a directionally consistent effect in both samples in the gene GAK, which is involved in clathrin-mediated membrane trafficking. We also found suggestive evidence that directionally inconsistent marginal effects from single marker analysis appeared to result from risk being driven by different haplotypes in the two samples for the genes SYN3 and NGLY1, which are involved in neurotransmitter release and proteasomal degradation, respectively. These results illustrate the utility of our unified framework for genome-wide association analysis of common, complex diseases.

Is dietary fat "fattening"? A comprehensive research synthesis.

The goal of this research synthesis was to separate and articulate questions that had clear meaning, were empirically addressable, and were germane to the broad question “Is fat fattening?” Four such questions addressing the effect of varying the proportion of dietary fat on body weight and body fat were formulated. A comprehensive review of electronic citation databases was conducted to identify studies that addressed each question. The results of the studies addressing each question were tabulated and summarized, and an answer for each question was formulated. The results indicated that whether “fat is fattening” depends on exactly what one means by the question. It is apparent that under conditions of energy deficit, high-fat diets lead to greater weight loss than low-fat diets, but under ad libitum feeding conditions, instructing persons to follow a low-fat diet promotes loss of body weight and body fat. For one question, studies were few but convincing that altering the proportion of energy from fat in daily snacks has no effect on weight, while for another there were not enough studies available to answer the question with confidence. General recommendations to reduce dietary fat to promote weight loss or maintenance in all circumstances may merit reconsideration.