Weighted pooling—practical and cost-effective techniques for pooled high-throughput sequencing

Motivation: Despite the rapid decline in sequencing costs, sequencing large cohorts of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of pools, thus dramatically reducing the cost of such large-scale sequencing projects. These approaches use combinatorial pooling designs where each individual is either present or absent from a pool. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers

A framework for interpreting genome-wide association studies of psychiatric disorders

Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and >40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes

Survival bias and drug interaction can attenuate cross-sectional case-control comparisons of genes with health outcomes. An example of the kinesin-like protein 6 (KIF6) Trp719Arg polymorphism and coronary heart disease

<p>Abstract</p> <p>Background</p> <p>Case-control studies typically exclude fatal endpoints from the case set, which we hypothesize will substantially underestimate risk if survival is genotype-dependent. The loss of fatal cases is particularly nontrivial for studies of coronary heart disease (CHD) because of significantly reduced survival (34% one-year fatality following a coronary attack). A case in point is the <it>KIF6 </it>Trp719Arg polymorphism (rs20455). Whereas six prospective studies have shown that carriers of the <it>KIF6 </it>Trp719Arg risk allele have 20% to 50% greater CHD risk than non-carriers, several cross-sectional case-control studies failed to show that carrier status is related to CHD. Computer simulations were therefore employed to assess the impact of the loss of fatal events on gene associations in cross-sectional case-control studies, using <it>KIF6 </it>Trp719Arg as an example.</p> <p>Results</p> <p>Ten replicates of 1,000,000 observations each were generated reflecting Canadian demographics. Cardiovascular disease (CVD) risks were assigned by the Framingham equation and events distributed among <it>KIF6 </it>Trp719Arg genotypes according to published prospective studies. Logistic regression analysis was used to estimate odds ratios between <it>KIF6 </it>genotypes. Results were examined for 33%, 41.5%, and 50% fatality rates for incident CVD.</p> <p>In the absence of any difference in percent fatalities between genotypes, the odds ratios (carriers vs. noncarriers) were unaffected by survival bias, otherwise the odds ratios were increasingly attenuated as the disparity between fatality rates increased between genotypes. Additional simulations demonstrated that statin usage, shown in four clinical trials to substantially reduce the excess CHD risk in the <it>KIF6 </it>719Arg variant, should also attenuate the <it>KIF6 </it>719Arg odds ratio in case-control studies.</p> <p>Conclusions</p> <p>These computer simulations show that exclusions of prior CHD fatalities attenuate odds ratios of case-control studies in proportion to the difference in the percent fatalities between genotypes. Disproportionate CHD survival for <it>KIF6 </it>Trip719Arg carriers is suggested by their 50% greater risk for recurrent myocardial infarction. This, and the attenuation of <it>KIF6 </it>719Arg carrier risk with statin use, may explain the genotype's weak association with CHD in cross-sectional case-control studies. The results may be relevant to the underestimation of risk in cross-sectional case-control studies of other genetic CHD-risk factors affecting survival.</p

Chromosome 1p13 genetic variants antagonize the risk of myocardial infarction associated with high ApoB serum levels

PMCID: PMC3480949This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Rare coding SNP in DZIP1 gene associated with late-onset sporadic Parkinson's disease

We present the first application of the hypothesis-rich mathematical theory to genome-wide association data. The Hamza et al. late-onset sporadic Parkinson's disease genome-wide association study dataset was analyzed. We found a rare, coding, non-synonymous SNP variant in the gene DZIP1 that confers increased susceptibility to Parkinson's disease. The association of DZIP1 with Parkinson's disease is consistent with a Parkinson's disease stem-cell ageing theory.Comment: 14 page

Do rare variant genotypes predict common variant genotypes?

The synthetic association hypothesis proposes that common genetic variants detectable in genome-wide association studies may reflect the net phenotypic effect of multiple rare polymorphisms distributed broadly within the focal gene rather than, as often assumed, the effect of common functional variants in high linkage disequilibrium with the focal marker. In a recent study, Dickson and colleagues demonstrated synthetic association in simulations and in two well-characterized, highly polymorphic human disease genes. The converse of this hypothesis is that rare variant genotypes must be correlated with common variant genotypes often enough to make the phenomenon of synthetic association possible. Here we used the exome genotype data provided for Genetic Analysis Workshop 17 to ask how often, how well, and under what conditions rare variant genotypes predict the genotypes of common variants within the same gene. We found nominal evidence of correlation between rare and common variants in 21-30% of cases examined for unrelated individuals; this rate increased to 38-44% for related individuals, underscoring the segregation that underlies synthetic association

The Impact of Errors in Copy Number Variation Detection Algorithms on Association Results

The inaccuracy of copy number variation (CNV) detection on single nucleotide polymorphism (SNP) arrays has recently been brought to attention. Such high error rates will undoubtedly have ramifications on downstream association testing. We examined this effect for a wide range of scenarios and found a noticeable decrease in power for error rates typical of CNV calling algorithms. We compared power using CNV calls to the log relative ratio and found the latter to be superior when error rates are moderate to large or when the CNV size is small. It is our recommendation that CNV researchers use intensity measurements as an alternative to CNV calls in these scenarios

The PhenX Toolkit: Get the Most From Your Measures

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions