SNP Selection for Association Studies: Maximizing Power across SNP Choice and Study Size

Efficiently identifying significant associations in genome-wide association studies

Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75.

Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.

Efficient association study design via power-optimized tag SNP selection

Discovering statistical correlation between causal genetic variation and clinical traits through association studies is an important method for identifying the genetic basis of human diseases. Since fully resequencing a cohort is prohibitively costly, genetic association studies take advantage of local correlation structure (or linkage disequilibrium) between single nucleotide polymorphisms (SNPs) by selecting a subset of SNPs to be genotyped (tag SNPs). While many current association studies are performed using commercially available high-throughput genotyping products that define a set of tag SNPs, choosing tag SNPs remains an important problem for both custom follow-up studies as well as designing the high-throughput genotyping products themselves. The most widely used tag SNP selection method optimizes the correlation between SNPs (r(2)). However, tag SNPs chosen based on an r(2) criterion do not necessarily maximize the statistical power of an association study. We propose a study design framework that chooses SNPs to maximize power and efficiently measures the power through empirical simulation. Empirical results based on the HapMap data show that our method gains considerable power over a widely used r(2)-based method, or equivalently reduces the number of tag SNPs required to attain the desired power of a study. Our power-optimized 100k whole genome tag set provides equivalent power to the Affymetrix 500k chip for the CEU population. For the design of custom follow-up studies, our method provides up to twice the power increase using the same number of tag SNPs as r(2)-based methods. Our method is publicly available via web server at http://design.cs.ucla.edu.

Leishmania and the leishmaniases: a parasite genetic update and advances in taxonomy, epidemiology and pathogenicity in humans

Leishmaniases remain a major public health problem today despite the vast amount of research conducted on Leishmania pathogens. The biological model is genetically and ecologically complex. This paper explores the advances in Leishmania genetics and reviews population structure, taxonomy, epidemiology and pathogenicity. Current knowledge of Leishmania genetics is placed in the context of natural populations. Various studies have described a clonal structure for Leishmania but recombination, pseudo-recombination and other genetic processes have also been reported. The impact of these different models on epidemiology and the medical aspects of leishmaniases is considered from an evolutionary point of view. The role of these parasites in the expression of pathogenicity in humans is also explored. It is important to ascertain whether genetic variability of the parasites is related to the different clinical expressions of leishmaniasis. The review aims to put current knowledge of Leishmania and the leishmaniases in perspective and to underline priority questions which 'leishmaniacs' must answer in various domains: epidemiology, population genetics, taxonomy and pathogenicity. It concludes by presenting a number of feasible ways of responding to these questions.

Exploring the perioptome: the role of genomics in thoracic surgery and anaesthesia

PURPOSE OF REVIEW

Genetic variation almost certainly affects every aspect of a patient's perioperative experience, yet almost nothing is known of the details involved. This review introduces the concept of static and dynamic genetic variation as a determinant of outcome after thoracic surgery and discusses some of the methodological issues involved in its study. Using a systems biology approach, it explores the ways in which data from many sources may be integrated into a common model of disease risk and outcome prediction.

RECENT FINDINGS

The incidence of mortality and morbidity after lung resection is unacceptably high, and has changed little in the last 20 years. New approaches to this problem are required if we are to improve outcomes after thoracic surgery.

SUMMARY

Patients seem to be predisposed to respond to a surgical stimulus in heterogeneous fashion, and this may be partly explained by variability in the genetic background with which they present for surgery. Although recent pilot data suggest that this is indeed the case, much more work remains to be done before this can be confirmed.