The Impact of Incomplete Linkage Disequilibrium and Genetic Model Choice on the Analysis and Interpretation of Genome-wide Association Studies

When conducting a genetic association study, it has previously been observed that a multiplicative risk model tends to fit better at a disease-associated marker locus than at the ungenotyped causative locus. This suggests that, while overall risk decreases as linkage disequilibrium breaks down, non-multiplicative components are more affected. This effect is investigated here, in particular the practical consequences it has on testing for trait/marker associations and the estimation of mode of inheritance and risk once an associated locus has been found. The extreme significance levels required for genome-wide association studies define a restricted range of detectable allele frequencies and effect sizes. For such parameters there is little to be gained by using a test that models the correct mode of inheritance rather than the multiplicative; thus the Cochran-Armitage trend test, which assumes a multiplicative model, is preferable to a more general model as it uses fewer degrees of freedom. Equally when estimating risk, it is likely that a multiplicative risk model will provide a good fit to the data, regardless of the underlying mode of inheritance at the true susceptibility locus. This may lead to problems in interpreting risk estimates

A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies

The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We propose a novel association test, complementary to the SNP-based approaches, that attempts to extract further signals of association by explicitly modeling and estimating both unknown SNPs and allelic heterogeneity at a locus. At each site we estimate the genealogy of the case-control sample by taking advantage of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by allowing more than one mutation on the branches of the genealogy. Our use of Bayesian methods allows us to assess directly the evidence for a causative SNP not well correlated with known SNPs and for allelic heterogeneity at each locus. Using simulated data and real data from the WTCCC project, we show that our method (i) produces a significant boost in signal and accurately identifies the form of the allelic heterogeneity in regions where it is known to exist, (ii) can suggest new signals that are not found by testing typed or imputed SNPs and (iii) can provide more accurate estimates of effect sizes in regions of association.Comment: Published in at http://dx.doi.org/10.1214/09-STS311 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Common DNA markers can account for more than half of the genetic influence on cognitive abilities

For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genomewide-association studies to identify the genetic variants that account for this heritability (i.e., the missing-heritability problem). However, a new approach, genomewide complex-trait analysis (GCTA), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays. In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers. We found that DNA markers tagged by the array accounted for .66 of the estimated heritability, reaffirming that cognitive abilities are heritable. Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities

Novel rheumatoid arthritis susceptibility locus at 22q12 identified in an extended UK genome-wide association study

© 2014 The Authors. Arthritis & Rheumatology is published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology.Peer reviewedPublisher PD

Synergy Disequilibrium Plots: graphical visualization of pairwise synergies and redundancies of SNPs with respect to a phenotype

Summary:We present a visualization tool applied on genome-wide association data, revealing disease-associated haplotypes, epistatically interacting loci, as well as providing visual signatures of multivariate correlations of genetic markers with respect to a phenotype

Ant Colony Optimisation for Exploring Logical Gene-Gene Associations in Genome Wide Association Studies.

In this paper a search for the logical variants of gene-gene interactions in genome-wide association study (GWAS) data using ant colony optimisation is proposed. The method based on stochastic algorithms is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover logical operations between combinations of single nucleotide polymorphisms that can discriminate Type II diabetes. A variety of logical combinations are explored and the best discovered associations are found within reasonable computational time and are shown to be statistically significantThis study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The work contained in this paper was funded by an EPSRC First Grant (EP/J007439/1) and we acknowledge their kind support

Subset-Based Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome Wide Association Studies

In this paper an ant colony optimisation approach for the discovery of gene-gene interactions in genome-wide association study (GWAS) data is proposed. The subset-based approach includes a novel encoding mechanism and tournament selection to analyse full scale GWAS data consisting of hundreds of thousands of variables to discover associations between combinations of small DNA changes and Type II diabetes. The method is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover combinations that are statistically significant and biologically relevant within reasonable computational time.The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the inves- tigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113

An inherited duplication at the gene p21 protein-activated Kinase 7 (PAK7) is a risk factor for psychosis

FUNDING Funding for this study was provided by the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z), the Wellcome Trust (072894/Z/03/Z, 090532/Z/09/Z and 075491/Z/04/B), NIMH grants (MH 41953 and MH083094) and Science Foundation Ireland (08/IN.1/B1916). We acknowledge use of the Trinity Biobank sample from the Irish Blood Transfusion Service; the Trinity Centre for High Performance Computing; British 1958 Birth Cohort DNA collection funded by the Medical Research Council (G0000934) and the Wellcome Trust (068545/Z/02) and of the UK National Blood Service controls funded by the Wellcome Trust. Chris Spencer is supported by a Wellcome Trust Career Development Fellowship (097364/Z/11/Z). Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust. ACKNOWLEDGEMENTS The authors sincerely thank all patients who contributed to this study and all staff who facilitated their involvement. We thank W. Bodmer and B. Winney for use of the People of the British Isles DNA collection, which was funded by the Wellcome Trust. We thank Akira Sawa and Koko Ishzuki for advice on the PAK7–DISC1 interaction experiment and Jan Korbel for discussions on mechanism of structural variation.Peer reviewedPublisher PD

MICL controls inflammation in rheumatoid arthritis

Acknowledgments We thank G Milne, D MacCallum, S Hardison, G Wilson, C Wallace, S Hadebe and A Richmond for assistance; H. El-Gabalawy for tissues and the animal facility staff for the care of our animals. Flow cytometry was undertaken in the Iain Fraser Cytometry Centre, University of Aberdeen. Funding: GDB was funded by the Wellcome Trust and MRC (UK). AA and CDB are supported by the Arthritis Research UK Tissue Engineering Centre (grant 19429). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk, and was funded by the Wellcome Trust (076113). MJGF was funded by The Arthritis Society and the Canadian Arthritis Network and J-ML by a scholarship from the Canadian Arthritis Network.Peer reviewedPublisher PD