A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

Han, Buhm, Pouget, Jennie G., Slowikowski, Kamil, Stahl, Eli, Lee, Cue Hyunkyu, Diogo, Dorothee, Hu, Xinli, Park, Yu Rang, Kim, Eunji, Gregersen, Peter K., Dahlqvist, Solbritt Rantapaa, Worthington, Jane, Martin, Javier, Eyre, Steve, Klareskog, Lars, Huizinga, Tom, Chen, Wei-Min, Onengut-Gumuscu, Suna, Rich, Stephen S., Wray, Naomi R. and Raychaudhuri, Soumya (2016) A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nature Genetics, 48 7: 803-810. doi:10.1038/ng.3572


Author Han, Buhm
Pouget, Jennie G.
Slowikowski, Kamil
Stahl, Eli
Lee, Cue Hyunkyu
Diogo, Dorothee
Hu, Xinli
Park, Yu Rang
Kim, Eunji
Gregersen, Peter K.
Dahlqvist, Solbritt Rantapaa
Worthington, Jane
Martin, Javier
Eyre, Steve
Klareskog, Lars
Huizinga, Tom
Chen, Wei-Min
Onengut-Gumuscu, Suna
Rich, Stephen S.
Wray, Naomi R.
Raychaudhuri, Soumya
Title A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases
Journal name Nature Genetics   Check publisher's open access policy
ISSN 1546-1718
1061-4036
Publication date 2016-07-01
Year available 2016
Sub-type Article (original research)
DOI 10.1038/ng.3572
Open Access Status Not yet assessed
Volume 48
Issue 7
Start page 803
End page 810
Total pages 8
Place of publication New York, NY, United States
Publisher Nature Publishing Group
Collection year 2017
Language eng
Formatted abstract
There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10-4) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10-3). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10-9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10-4) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).
Keyword Pleiotropy
Autoimmune diseases
Neuropsychiatric diseases
Shared risk alleles
Genome-wide association study (GWAS) data
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
 
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