A three-stage approach for genome-wide association studies with family data for quantitative traits.
BACKGROUND
Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data.We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. 10-6 for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project.


RESULTS
Our simulations show that whether or not population admixture is present, the three-stage approach has no inflated type I error. In terms of power, using LME adjusting PC is only slightly more powerful than the three-stage approach. When applied to the GWA analyses of uric acid in the SHARe project of FHS, the three-stage approach successfully identified and confirmed three SNPs previously reported as genome-wide significant signals.


CONCLUSIONS
For GWA analyses of quantitative traits with family data, our three-stage approach provides another appealing solution to population admixture, in addition to LME adjusting for genetic PC.
Chen
Ming-Huei
MH
Department of Neurology and Framingham Heart Study, Boston University School of Medicine, MA, USA.
Larson
Martin G
MG
Hsu
Yi-Hsiang
YH
Peloso
Gina M
GM
Guo
Chao-Yu
CY
Fox
Caroline S
CS
Atwood
Larry D
LD
Yang
Qiong
Q
D005190:Family; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D019655:Quantitative Trait, Heritable
Genetica. 1995;96(1-2):3-12
7607457
Am J Hum Genet. 1998 May;62(5):1198-211
9545414
Am J Hum Genet. 2007 Sep;81(3):607-14
17701906
Hum Hered. 2000 Jul-Aug;50(4):211-23
10782012
Stat Methods Med Res. 2001 Feb;10(1):3-25
11329691
Am J Hum Genet. 2002 Dec;71(6):1330-41
12454799
Genet Epidemiol. 2001;21 Suppl 1:S341-6
11793695
Am J Hum Genet. 2007 Nov;81(5):895-905
17924333
BMC Genet. 2005 Dec 30;6 Suppl 1:S91
16451707
Am J Hum Genet. 2003 Oct;73(4):801-11
14502464
Theor Popul Biol. 2003 May;63(3):221-30
12689793
Science. 2006 Apr 14;312(5771):279-83
16614226
Nat Genet. 2006 Aug;38(8):904-9
16862161
Lancet. 2008 Dec 6;372(9654):1953-61
18834626
Bioinformatics. 2010 Feb 15;26(4):580-1
20040588
Am J Hum Genet. 1993 Mar;52(3):506-16
8447318