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

Overview of attention for article published in Human Molecular Genetics, April 2014
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Title
Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data
Published in
Human Molecular Genetics, April 2014
DOI 10.1093/hmg/ddu174
Pubmed ID
Authors

Guo-Bo Chen, Sang Hong Lee, Marie-Jo A. Brion, Grant W. Montgomery, Naomi R. Wray, Graham L. Radford-Smith, Peter M. Visscher

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.

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Geographical breakdown

Country Count As %
United States 2 1%
Germany 1 <1%
Sweden 1 <1%
Netherlands 1 <1%
Finland 1 <1%
Israel 1 <1%
Unknown 146 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 21%
Student > Ph. D. Student 30 20%
Student > Master 12 8%
Other 8 5%
Professor 8 5%
Other 28 18%
Unknown 35 23%
Readers by discipline Count As %
Medicine and Dentistry 33 22%
Agricultural and Biological Sciences 29 19%
Biochemistry, Genetics and Molecular Biology 23 15%
Computer Science 6 4%
Immunology and Microbiology 5 3%
Other 12 8%
Unknown 45 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2017.
All research outputs
#19,125,393
of 23,698,019 outputs
Outputs from Human Molecular Genetics
#7,379
of 8,079 outputs
Outputs of similar age
#166,392
of 228,287 outputs
Outputs of similar age from Human Molecular Genetics
#86
of 106 outputs
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