Journal article

Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood

SH Lee, J Yang, ME Goddard, PM Visscher, NR Wray

Bioinformatics | OXFORD UNIV PRESS | Published : 2012

Abstract

SUMMARY: Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary trait..

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University of Melbourne Researchers

Grants

Awarded by Netherlands Scientific Organization


Awarded by Wellcome Trust


Awarded by Australian National Health and Medical Research Council


Awarded by Australian Research Council


Awarded by US National Institute of Health


Awarded by NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES


Funding Acknowledgements

We thank QBI IT team. S. H. L. acknowledges the use of the Genetic Cluster Computer for carrying out a part of simulations. The cluster is financially supported by the Netherlands Scientific Organization (NOW 480-05-003). 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 WTCCC data is available from www.wtccc.org.uk. Funding for the WTCCC project was provided by the Wellcome Trust under award 076113.The Australian National Health and Medical Research Council (613672, 613601, 613608 and 1011506), the Australian Research Council (DP1093502 and FT0991360) and the US National Institute of Health (GM075091).