Review and evaluation of methods correcting for population stratification with a focus on underlying statistical principles

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis

Background

Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.

Methods

We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.

Results

SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.

Conclusions

This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.

Influence of polymorphisms in toll-like receptors (TLRs) on malaria susceptibility in low-endemic area of the Atlantic Forest, São Paulo, Brazil

In low-endemic areas for malaria transmission, asymptomatic individuals play an important role as reservoirs for malarial infection. Understanding the dynamics of asymptomatic malaria is crucial for its efficient control in these regions. Genetic host factors such as Toll-like receptor (TLR) polymorphisms may play a role in the maintenance or elimination of infection. In this study, the effect of TLR polymorphisms on the susceptibility to malaria was investigated among individuals living in the Atlantic Forest of São Paulo, Southern Brazil. A hundred and ninety-five Brazilian individuals were enrolled and actively followed up for malaria for three years. Twenty-four polymorphisms in five toll-like receptor (TLR) genes were genotyped by RFLP, direct sequencing or fragment analysis. The genotypes were analyzed for the risk of malaria. Ongoing Plasmodium vivax or P. malariae infection, was identified by the positive results in PCR tests and previous P. vivax malaria, was assumed when antiplasmodial antibodies against PvMSP1₁₉ were detected by ELISA. An evaluation of genomic ancestry was conducted using biallelic ancestry informative markers and the results were used as correction in the statistical analysis. Nine SNPs and one microsatellite were found polymorphic and three variant alleles in TLR genes were associated to malaria susceptibility. The regression coefficient estimated for SNP TLR9.-1237.T/C indicated that the presence of at least one allele C increased, on average, 2.3 times the malaria odds, compared to individuals with no allele C in this SNP. However, for individuals with the same sex, age and household, the presence of at least one allele C in SNP TLR9.-1486.T/C reduced, on average, 1.9 times the malaria odds, compared to individuals with no allele C. Moreover, this allele C plus an S allele in TLR6.P249S in individuals with same sex, age and ancestry, reduced, on average, 4.4 times the malaria odds. Our findings indicate a significant association of TLR9.-1237.T/C gene polymorphism with malarial infection and contribute to a better knowledge of the role of TLRs in malaria susceptibility in an epidemiological setting different from other settings.

Genome-Wide Association Studies of Chemotherapeutic Toxicities: Genomics of Inequality

With an estimated global population of cancer survivors exceeding 32 million and growing, there is a heightened awareness of the long-term toxicities resulting from cancer treatments and their impact on quality of life. Unexplained heterogeneity in the persistence and development of toxicities, as well as an incomplete understanding of their mechanisms, have generated a growing need for the identification of predictive pharmacogenomic markers. Early studies addressing this need used a candidate gene approach; however, over the last decade, unbiased and comprehensive genome-wide association studies (GWAS) have provided markers of phenotypic risk and potential targets to explore the mechanistic and regulatory pathways of biological functions associated with chemotherapeutic toxicity. In this review, we provide the current status of GWAS of chemotherapeutic toxicities with an emphasis on examining the ancestral diversity of the representative cohorts within these studies. Persistent calls to incorporate both ancestrally diverse and/or admixed populations into genomic efforts resulted in a recent rise in the number of studies utilizing cohorts of East Asian descent; however, few pharmacogenomic studies to date include cohorts of African, Indigenous American, Southwest Asian, and admixed populations. Through comprehensively evaluating sample size, composition by ancestry, genome-wide significant variants, and population-specific minor allele frequencies as reported by HapMap/dbSNP using NCBI PubMed and the NHGRI-EBI GWAS Catalog, we illustrate how allele frequencies and effect sizes tend to vary among individuals of differing ancestries. In an era of personalized medicine, the lack of diversity in genome-wide studies of anticancer agent toxicity may contribute to the health disparity gap. Clin Cancer Res; 23(15); 4010-9. ©2017 AACR.

A statistical framework for testing the causal effects of fetal drive

Maternal genetic and phenotypic characteristics (e.g., metabolic and behavioral) affect both the intrauterine milieu and lifelong health trajectories of their fetuses. Yet at the same time, fetal genotype may affect processes that alter pre and postnatal maternal physiology, and the subsequent health of both fetus and mother. We refer to these latter effects as 'fetal drive.' If fetal genotype is driving physiologic, metabolic, and behavioral phenotypic changes in the mother, there is a possibility of differential effects with different fetal genomes inducing different long-term effects on both maternal and fetal health, mediated through intrauterine environment. This proposed mechanistic path remains largely unexamined and untested. In this study, we offer a statistical method to rigorously test this hypothesis and make causal inferences in humans by relying on the (conditional) randomization inherent in the process of meiosis. For illustration, we apply this method to a dataset from the Framingham Heart Study.

Genetic psychophysiology: advances, problems, and future directions

This paper presents an overview of historical advances and the current state of genetic psychophysiology, a rapidly developing interdisciplinary research linking genetics, brain, and human behavior, discusses methodological problems, and outlines future directions of research. The main goals of genetic psychophysiology are to elucidate the neural pathways and mechanisms mediating genetic influences on cognition and emotion, identify intermediate brain-based phenotypes for psychopathology, and provide a functional characterization of genes being discovered by large association studies of behavioral phenotypes. Since the initiation of this neurogenetic approach to human individual differences in the 1970s, numerous twin and family studies have provided strong evidence for heritability of diverse aspects of brain function including resting-state brain oscillations, functional connectivity, and event-related neural activity in a variety of cognitive and emotion processing tasks, as well as peripheral psychophysiological responses. These data indicate large differences in the presence and strength of genetic influences across measures and domains, permitting the selection of heritable characteristics for gene finding studies. More recently, candidate gene association studies began to implicate specific genetic variants in different aspects of neurocognition. However, great caution is needed in pursuing this line of research due to its demonstrated proneness to generate false-positive findings. Recent developments in methods for physiological signal analysis, hemodynamic imaging, and genomic technologies offer new exciting opportunities for the investigation of the interplay between genetic and environmental factors in the development of individual differences in behavior, both normal and abnormal.

Gene-environment interactions in genome-wide association studies: current approaches and new directions

BACKGROUND

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge.

METHODS

We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually.

RESULTS

Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research.

CONCLUSIONS

Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS

Background

The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.

Results

We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.

Conclusions

Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

The utility of mitochondrial and y chromosome phylogenetic data to improve correction for population stratification

Genome-wide association (GWA) studies have become a standard approach for discovering and validating genomic polymorphisms putatively associated with phenotypes of interest. Accounting for population structure in GWA studies is critical to attain unbiased parameter measurements and control Type I error. One common approach to accounting for population structure is to include several principal components derived from the entire autosomal dataset, which reflects population structure signal. However, knowing which components to include is subjective and generally not conclusive. We examined how phylogenetic signal from mitochondrial DNA (mtDNA) and chromosome Y (chr:Y) markers is concordant with principal component data based on autosomal markers to determine whether mtDNA and chr:Y phylogenetic data can help guide principal component selection. Using HAPMAP and other original data from individuals of multiple ancestries, we examined the relationships of mtDNA and chr:Y phylogenetic signal with the autosomal PCA using best subset logistic regression. We show that while the two approaches agree at times, this is independent of the component order and not completely represented in the Eigen values. Additionally, we use simulations to demonstrate that our approach leads to a slightly reduced Type I error rate compared to the standard approach. This approach provides preliminary evidence to support the theoretical concept that mtDNA and chr:Y data can be informative in locating the PCs that are most associated with evolutionary history of populations that are being studied, although the utility of such information will depend on the specific situation.

Rare and low frequency variant stratification in the UK population: description and impact on association tests

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.

European American stratification in ovarian cancer case control data: the utility of genome-wide data for inferring ancestry

We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available.

Common variation in vitamin D pathway genes predicts circulating 25-hydroxyvitamin D Levels among African Americans

Vitamin D is implicated in a wide range of health outcomes, and although environmental predictors of vitamin D levels are known, the genetic drivers of vitamin D status remain to be clarified. African Americans are a group at particularly high risk for vitamin D insufficiency but to date have been virtually absent from studies of genetic predictors of circulating vitamin D levels. Within the Southern Community Cohort Study, we investigated the association between 94 single nucleotide polymorphisms (SNPs) in five vitamin D pathway genes (GC, VDR, CYP2R1, CYP24A1, CYP27B1) and serum 25-hydroxyvitamin D (25(OH)D) levels among 379 African American and 379 Caucasian participants. We found statistically significant associations with three SNPs (rs2298849 and rs2282679 in the GC gene, and rs10877012 in the CYP27B1 gene), although only for African Americans. A genotype score, representing the number of risk alleles across the three SNPs, alone accounted for 4.6% of the variation in serum vitamin D among African Americans. A genotype score of 5 (vs. 1) was also associated with a 7.1 ng/mL reduction in serum 25(OH)D levels and a six-fold risk of vitamin D insufficiency (<20 ng/mL) (odds ratio 6.0, p = 0.01) among African Americans. With African ancestry determined from a panel of 276 ancestry informative SNPs, we found that high risk genotypes did not cluster among those with higher African ancestry. This study is one of the first to investigate common genetic variation in relation to vitamin D levels in African Americans, and the first to evaluate how vitamin D-associated genotypes vary in relation to African ancestry. These results suggest that further evaluation of genetic contributors to vitamin D status among African Americans may help provide insights regarding racial health disparities or enable the identification of subgroups especially in need of vitamin D-related interventions.

An improved delta-centralization method for population stratification

Dadd et al. [Hum Hered 2010;69:285-294] recently criticized our delta-centralization (DC) method of controlling for population stratification (PS) and concluded that DC does not work. To explore our method, the authors simulated data under the Balding-Nichols (BN) model, which is more general than the model we had used in our simulations. They determined that the DC method underestimated the PS parameter (δ) and inflated the type I error rates when applied to BN-simulated data, and from this they concluded that the DC method is invalid. However, we argue that this conclusion is premature. In this paper, we (1) show why δ is underestimated and type I error rates are inflated when BN-simulated data are used, and (2) present a simple adjustment to DC that works reasonably well for data from both kinds of simulations. We also show that the adjusted DC method has appropriate power under a range of scenarios.

Replication of GWAS "Hits" by Race for Breast and Prostate Cancers in European Americans and African Americans

In this study, we assessed association of genome-wide association studies (GWAS) “hits” by race with adjustment for potential population stratification (PS) in two large, diverse study populations; the Carolina Breast Cancer Study (CBCS; N total = 3693 individuals) and the University of Pennsylvania Study of Clinical Outcomes, Risk, and Ethnicity (SCORE; N total = 1135 individuals). In both study populations, 136 ancestry information markers and GWAS “hits” (CBCS: FGFR2, 8q24; SCORE: JAZF1, MSMB, 8q24) were genotyped. Principal component analysis was used to assess ancestral differences by race. Multivariable unconditional logistic regression was used to assess differences in cancer risk with and without adjustment for the first ancestral principal component (PC1) and for an interaction effect between PC1 and the GWAS “hit” (SNP) of interest. PC1 explained 53.7% of the variance for CBCS and 49.5% of the variance for SCORE. European Americans and African Americans were similar in their ancestral structure between CBCS and SCORE and cases and controls were well matched by ancestry. In the CBCS European Americans, 9/11 SNPs were significant after PC1 adjustment, but after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs1219648 in FGFR2); for CBCS African Americans, 6/11 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, all six SNPs remained significant and an additional SNP now became significant. In the SCORE European Americans, 0/9 SNPs were significant after PC1 adjustment and no changes were seen after additional adjustment for the PC1 by SNP interaction effect; for SCORE African Americans, 2/9 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs16901979 at 8q24). We show that genetic associations by race are modified by interaction between individual SNPs and PS.

Mapping of disease-associated variants in admixed populations

Recent developments in high-throughput genotyping and whole-genome sequencing will enhance the identification of disease loci in admixed populations. We discuss how a more refined estimation of ancestry benefits both admixture mapping and association mapping, making disease loci identification in admixed populations more powerful.

High-throughput genotyping and sequencing will enable refined estimation of ancestry, thus enhancing disease loci identification in admixed populations

A flexible model for association analysis in sibships with missing genotype data

A common design in family-based association studies consists of siblings without parents. Several methods have been proposed for analysis of sibship data, but they mostly do not allow for missing data, such as haplotype phase or untyped markers. On the other hand, general methods for nuclear families with missing data are computationally intensive when applied to sibships, since every family has missing parents that could have many possible genotypes. We propose a computationally efficient model for sibships by conditioning on the sets of alleles transmitted into the sibship by each parent. This means that the likelihood can be written only in terms of transmitted alleles and we do not have to sum over all possible untransmitted alleles when they cannot be deduced from the siblings. The model naturally accommodates missing data and admits standard theory of estimation, testing, and inclusion of covariates. Our model is quite robust to population stratification and can test for association in the presence of linkage. We show that our model has similar power to FBAT for single marker analysis and improved power for haplotype analysis. Compared to summing over all possible untransmitted alleles, we achieve similar power with considerable reductions in computation time.

Capitalizing on admixture in genome-wide association studies: a two-stage testing procedure and application to height in African-Americans

As genome-wide association studies expand beyond populations of European ancestry, the role of admixture will become increasingly important in the continued discovery and fine-mapping of variation influencing complex traits. Although admixture is commonly viewed as a confounding influence in association studies, approaches such as admixture mapping have demonstrated its ability to highlight disease susceptibility regions of the genome. In this study, we illustrate a powerful two-stage testing strategy designed to uncover trait-associated single nucleotide polymorphisms in the presence of ancestral allele frequency differentiation. In the first stage, we conduct an association scan by using predicted genotypic values based on regional admixture estimates. We then select a subset of promising markers for inclusion in a second-stage analysis, where association is tested between the observed genotype and the phenotype conditional on the predicted genotype. We prove that, under the null hypothesis, the test statistics used in each stage are orthogonal and asymptotically independent. Using simulated data designed to mimic African-American populations in the case of a quantitative trait, we show that our two-stage procedure maintains appropriate control of the family wise error rate and has higher power under realistic effect sizes than the one-stage testing procedure in which all markers are tested for association simultaneously with control of admixture. We apply the proposed procedure to a study of height in 201 African-Americans genotyped at 108 ancestry informative markers. The two-stage procedure identified two statistically significant markers rs1985080 (PTHB1/BBS9) and rs952718 (ABCA12). PTHB1/BBS9 is downregulated by parathyroid hormone in osteoblastic cells and is thought to be involved in parathyroid hormone action in bones and may play a role in height. ABCA12 is a member of the superfamily of ATP binding cassette transporters and its potential involvement in height is unclear.

Family-based association studies

Family-based association methods are useful because they offer improved matching of controls to cases, with the result that they are not susceptible to confounding by population stratification. They also allow analysis of parent-of-origin effects and maternal-fetal interactions. The transmission/disequilibrium test (TDT) is a test of linkage and association that is equivalent to a matched case/control analysis, from which various extensions are possible. A logistic regression formulation leads to modifications for multiallelic markers, haplotypes, and quantitative traits. Some pitfalls are described, for the situations in which one parent is missing, genotyping errors have occurred, and haplotype phase is uncertain. The problem of testing association in general pedigrees is discussed, with particular reference to sib pairs without parents.

Principal-component analysis for assessment of population stratification in mitochondrial medical genetics

Although inherited mitochondrial genetic variation can cause human disease, no validated methods exist for control of confounding due to mitochondrial population stratification (PS). We sought to identify a reliable method for PS assessment in mitochondrial medical genetics. We analyzed mitochondrial SNP data from 1513 European American individuals concomitantly genotyped with the use of a previously validated panel of 144 mitochondrial markers as well as the Affymetrix 6.0 (n = 432), Illumina 610-Quad (n = 458), or Illumina 660 (n = 623) platforms. Additional analyses were performed in 938 participants in the Human Genome Diversity Panel (HGDP) (Illumina 650). We compared the following methods for controlling for PS: haplogroup-stratified analyses, mitochondrial principal-component analysis (PCA), and combined autosomal-mitochondrial PCA. We computed mitochondrial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondrial ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondrial PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondrial PCA in controlling for PS. Correlation between nuclear and mitochondrial principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondrial analysis of simulated phenotypes. Mitochondrial PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondrial marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondrial PCA.

Quality control for genome-wide association studies

This chapter is a comprehensive review of quality control (QC) methods for SNP-based genotyping panels used in genome-wide association studies. These include QC on individuals for missingness, gender checks, duplicates and cryptic relatedness, population outliers, heterozygosity and inbreeding, and QC on SNPs for missingness, minor allele frequency and Hardy-Weinberg equilibrium. The emphasis is on the reasons behind each QC step and on the use of intelligent approaches rather than arbitrary QC thresholds. Scripts and code for performing these QC steps are available at www.kcl.ac.uk/mmg/gwascode/ .

Genome-wide association studies in diverse populations

Genome-wide association (GWA) studies have identified a large number of SNPs associated with disease phenotypes. As most GWA studies have been performed in populations of European descent, this Review examines the issues involved in extending the consideration of GWA studies to diverse worldwide populations. Although challenges exist with issues such as imputation, admixture and replication, investigation of a greater diversity of populations could make substantial contributions to the goal of mapping the genetic determinants of complex diseases for the human population as a whole.

Genetics in psychiatry: common variant association studies

Many psychiatric conditions and traits are associated with significant heritability. Genetic risk for psychiatric conditions encompass rare variants, identified due to major effect, as well as common variants, the latter analyzed by association analyses. We review guidelines for common variant association analyses, undertaking after assessing evidence of heritability. We highlight the importance of: suitably large sample sizes; an experimental design that controls for ancestry; careful data cleaning; correction for multiple testing; small P values for positive findings; assessment of effect size for positive findings; and, inclusion of an independent replication sample. We also note the importance of a critical discussion of any prior findings, biological follow-up where possible, and a means of accessing the raw data.

A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors

Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors.

Genetic variants in association studies--review of strengths and weaknesses in study design and current knowledge of impact on cancer risk

Sequencing of the human genome has recently been completed and mapping of the complete genomic variation is ongoing. During the last decade there has been a huge expansion of studies of genetic variants, both with respect to association studies of disease risk and for studies of genetic factors of prognosis and treatments response, i.e., pharmacogenomics. The use of genetics to predict a patient's risk of disease or treatment response is one step toward an improved personalised prevention and screening modality for the prevention of cancer and treatment selection. The technology and statistical methods for completing whole genome tagging of variants and genome wide association studies has developed rapidly over the last decade. After identifying the genetic loci with the strongest, statistical associations with disease risk, future studies will need to further characterise the genotype-phenotype relationship to provide a biological basis for prevention and treatment decisions according to genetic profile. This review discusses some of the general issues and problems of study design; we also discuss challenges in conducting valid association studies in rare cancers such as paediatric brain tumours, where there is support for genetic susceptibility but difficulties in assembling large sample sizes. The clinical interpretation and implementation of genetic association studies with respect to disease risk and treatment is not yet well defined and remains an important area of future research.

Combining case-control and case-trio data from the same population in genetic association analyses: overview of approaches and illustration with a candidate gene study

In genetic association studies, investigators compare allele or genotype frequencies in unrelated case and control subjects or examine preferential allele transmissions from parents to affected offspring. In many genetic case-control studies, the collection of DNA material extends to relatives such as parents of cases. Thus, case-control and case-parent trio association analyses are possible. Whereas the goal of collecting genetic information from family members in a study initially designed as a case-control study is to enrich the genetic analysis, increase power, or address concern about population structure bias, methods of combining genetic data from unrelated case and control subjects with genetic trio data from the same study population are not well known. A number of hybrid approaches have been developed that utilize such data together. In this paper, the authors describe key features of genetic case-control and case-parent trio studies and review commonly used methods of genetic analysis for case-parent trio designs. In addition, they provide a pragmatic review of statistical methods and available software for existing hybrid approaches that combine various components of case-control and genetic trio data. The application of all methods is illustrated using a candidate gene study of childhood leukemia that included case-control subjects and their parents.

A kinship-based modification of the armitage trend test to address hidden population structure and small differential genotyping errors

BACKGROUND/AIMS

We propose a modification of the well-known Armitage trend test to address the problems associated with hidden population structure and hidden relatedness in genome-wide case-control association studies.

METHODS

The new test adopts beneficial traits from three existing testing strategies: the principal components, mixed model, and genomic control while avoiding some of their disadvantageous characteristics, such as the tendency of the principal components method to over-correct in certain situations or the failure of the genomic control approach to reorder the adjusted tests based on their degree of alignment with the underlying hidden structure. The new procedure is based on Gauss-Markov estimators derived from a straightforward linear model with an imposed variance structure proportional to an empirical relatedness matrix. Lastly, conceptual and analytical similarities to and distinctions from other approaches are emphasized throughout.

RESULTS

Our simulations show that the power performance of the proposed test is quite promising compared to the considered competing strategies. The power gains are especially large when small differential differences between cases and controls are present; a likely scenario when public controls are used in multiple studies.

CONCLUSION

The proposed modified approach attains high power more consistently than that of the existing commonly implemented tests. Its performance improvement is most apparent when small but detectable systematic differences between cases and controls exist.

Statistical issues in the analysis of DNA Copy Number Variations

Approaches to assess copy number variation have advanced rapidly and are being incorporated into genetic studies. While the technology exists for CNV genotyping, a further understanding and discussion of how to use the CNV data for association analyses is warranted. We present the options available for processing and analysing CNV data. We break these steps down into choice of genotyping platform, normalisation of the array data, calling algorithm, and statistical analysis.