Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data - PubMed (original) (raw)

. 2014 Sep 1;23(17):4710-20.

doi: 10.1093/hmg/ddu174. Epub 2014 Apr 11.

Collaborators, Affiliations

Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data

Guo-Bo Chen et al. Hum Mol Genet. 2014.

Abstract

As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61,251 and 38.550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci.

© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PubMed Disclaimer

Figures

Figure 1.

Figure 1.

Increase in the number of GWAS-significant signals for total sample size and the number of cases. Both the axes are on a logarithmic scale. The source of the data is in

Supplementary Material, Table S1

.

Figure 2.

Figure 2.

The comprehensive estimation of the explained variance in liability scale for CD and UC. For each disease, from left to right are the estimates from GWAS hits (12), iChip, generic GWAS array data, and twin studies pooling four published twins cohorts together (

Supplementary Material, Note

).

Figure 3.

Figure 3.

Partitioning of genetic variation in liability to CD and UC across chromosomes. The iChip sample was split into two subsets, and the averaged heritability was used in this figure.

Figure 4.

Figure 4.

Proportion of the genetic variation in liability to CD and UC explained by the selected regions across three types of partitioning of the genome.

Similar articles

Cited by

References

    1. Visscher P.M., Brown M.A., McCarthy M.I., Yang J. Five years of GWAS discovery. Am. J. Hum. Genet. 2012;90:7–24. - PMC - PubMed
    1. The Wellcome Trust Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–678. - PMC - PubMed
    1. Hindorff L.A., Sethupathy P., Junkins H.A., Ramos E.M., Mehta J.P., Collins F.S., Manolio T.A. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA. 2009;106:9362–9367. - PMC - PubMed
    1. Li L., Li Y., Browning S.R., Browning B.L., Slater A.J., Kong X., Aponte J.L., Mooser V.E., Chissoe S.L., Whittaker J.C., et al. Performance of genotype imputation for rare variants identified in exons and flanking regions of genes. PLoS One. 2011;6:e24945. - PMC - PubMed
    1. Mägi R., Asimit J.L., Day-Williams A.G., Zeggini E., Morris A.P. Genome-wide association analysis of imputed rare variants: application to seven common complex diseases. Genet. Epidemiol. 2012;796:785–796. - PMC - PubMed

Publication types

MeSH terms

Grants and funding

LinkOut - more resources