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.2013 Feb 15;22(4):832-41.
doi: 10.1093/hmg/dds491. Epub 2012 Nov 28.

Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis

Collaborators, Affiliations

Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis

S Hong Lee et al. Hum Mol Genet..

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.

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Figures

Figure 1.
Figure 1.
Joint analysis for each chromosome for estimating the genetic variance using SNP data. (A) ED.y = −0.0002 + 0.00009x,R2 = 0.37,P = 0.003. (B) AD.y = 0.0081 + 0.00003x,R2 = 0.024,P = 0.49 and omitting chromosome 19,y = 0.00061 + 0.00007x,R2 = 0.25,P = 0.02. (C) MS.y = −0.002509 + 0.00012x,R2 = 0.31,P = 0.007 and omitting chromosome 6,y = 0.0014 + 0.0001x,R2 = 0.45,P = 0.0009.
Figure 2.
Figure 2.
Histogram of the proportion of genotypes for each SNP that is called the same for the 1958A cohort called by the Illuminus or GenCall algorithm.
See this image and copyright information in PMC

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