상위피인용논문
S. Hong Lee1,2,∗, Denise Harold3, Dale R. Nyholt2, ANZGene Consortium†, International Endogene Consortium†, the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) Consortium†, Michael E. Goddard4, Krina T. Zondervan5, Julie Williams3, Grant W. Montgomery2, Naomi R. Wray1,2,‡ and Peter M. Visscher1,2,6,‡
1 The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia, 2 Queensland Institute of Medical Research, 300 Herston Road, Brisbane 4006, Australia, 3 Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK, 4 Department of Agriculture and Food Systems, University of Melbourne, Melbourne, Australia, 5 Nuffield Department of Obstetrics and Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford, UK and 6 The University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
∗ To whom correspondence should be addressed.
† Consortium membership lists are provided in the Supplementary Information.
‡ These authors contributed equally.
Abstract
Common diseases such as endometriosis (ED), Alzheimer's disease (AD) and multiple sclerosis (MS) account for a significant proportion of the health care burden in many countries. Genome-wide association studies (GWASs) for these diseases have identified a number of individual genetic variants contributing to the risk of those diseases. However, the effect size for most variants is small and collectively the known variants explain only a small proportion of the estimated heritability. We used a linear mixed model to fit all single nucleotide polymorphisms (SNPs) simultaneously, and estimated genetic variances on the liability scale using SNPs from GWASs in unrelated individuals for these three diseases. For each of the three diseases, case and control samples were not all genotyped in the same laboratory. We demonstrate that a careful analysis can obtain robust estimates, but also that insufficient quality control (QC) of SNPs can lead to spurious results and that too stringent QC is likely to remove real genetic signals. Our estimates show that common SNPs on commercially available genotyping chips capture significant variation contributing to liability for all three diseases. The estimated proportion of total variation tagged by all SNPs was 0.26 (SE 0.04) for ED, 0.24 (SE 0.03) for AD and 0.30 (SE 0.03) for MS. Further, we partitioned the genetic variance explained into five categories by a minor allele frequency (MAF), by chromosomes and gene annotation. We provide strong evidence that a substantial proportion of variation in liability is explained by common SNPs, and thereby give insights into the genetic architecture of the diseases.
논문정보
관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기