Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general ‘‘phase change’ ’ phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like F ST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.

Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000–100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable. Linking genetic variation to personal health is one of the major challenges and opportunities facing scientists today.

Population stratification can be a serious obstacle in the analysis of genomewide association studies. We propose a method for evaluating the significance of association scores in whole-genome cohorts with stratification. Our approach is a randomization test akin to a standard permutation test. It conditions on the genotype matrix and thus takes into account not only the population structure but also the complex linkage disequilibrium structure of the genome. As we show in simulation experiments, our method achieves higher power and significantly better control over false-positive rates than do existing methods. In addition, it can be easily applied to whole-genome association studies. One of the principal difficulties in drawing causal inferences from whole-genome case-control association studies is the confounding effect of population structure. Differences in allele frequencies between cases and controls may be due to systematic differences in ancestry rather than to association of genes with disease. 1–8 This issue needs careful attention in forthcoming large-scale association studies, given the lack of knowledge regarding relevant ancestral history throughout the genome and the need to

This is to certify that I have examined this copy of a doctoral dissertation by

ABSTRACT: The evidence for a genetic component in the aetiology of sarcoidosis includes familial aggregation, associations with genetic polymorphisms, and linkage to the major histocompatibility complex class region on chromosome 6p. Unfortunately, the majority of genetic associations with sarcoidosis have not been consistently replicated. In the present study, using a family-based study design, which controls for population stratification, the authors attempted to replicate previously reported associations between sarcoidosis and three attractive candidate genes studied primarily in casecontrol samples. In 225 nuclear families, ascertained through African Americans with a history of sarcoidosis, no evidence was found for an association between sarcoidosis susceptibility and polymorphisms in the angiotensin converting enzyme, vitamin D receptor and tumour necrosis factor-a genes. Further analyses of chronic and acute disease phenotypes failed to reveal any notable associations. Assuming an underlying inheritance model with an additive allelic effect on disease risk, the current study had

Shaking the tree: mapping complex disease genes with

genetics gains momentum, extraordinary information will be uncovered on the genetic architecture of the human brain. However, there are significant challenges to be addressed first. Not the least of these challenges is to accomplish the sample size necessary to detect subtle genetic influences on the morphometry and function of the healthy brain. Aside from sample size, image acquisition and analysis methods need to be refined in order to ensure optimum sensitivity to genetic and complementary environmental influences. Then there is the vexing issue of interpreting the resulting data. We describe how researchers from the east coast of Australia and the west coast of America have embarked upon a collaboration to meet these challenges using data currently being collected from a

Abstract not found

doi:10.1093/bib/bbm058 Three lectures on case^control genetic association analysis

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Social Networking and DTC Genomics Downloaded By: [Yale Univ Library] At: 17:05 3 March 2010 being socialized as governments in the United States and abroad, including those in Australia, the United Kingdom and Canada, bail out private financial institutions to avoid what is perceived to be the even greater social costs of letting them collapse. Regulatory responses to both issues must make private companies accountable for costs so there is a