Dataset Information

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

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).

SUBMITTER: Han B

PROVIDER: S-EPMC4925284 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Han Buhm B Pouget Jennie G JG Slowikowski Kamil K Stahl Eli E Lee Cue Hyunkyu CH Diogo Dorothee D Hu Xinli X Park Yu Rang YR Kim Eunji E Gregersen Peter K PK Dahlqvist Solbritt Rantapää SR Worthington Jane J Martin Javier J Eyre Steve S Klareskog Lars L Huizinga Tom T Chen Wei-Min WM Onengut-Gumuscu Suna S Rich Stephen S SS Wray Naomi R NR Raychaudhuri Soumya S

Nature genetics 20160516 7

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 ...[more]

PMID: 27182969

Similar Datasets

Project description:ImportanceRecent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes.ObjectivesTo test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach.Design, setting, and participantsUsing the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017.Main outcomes and measuresThe primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases.ResultsGenome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK, HLA-DRB5, LRRK2, and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA, LRRK2, MAPT, TRIM10, and SETD1A in PD. Among the novel genes discovered, WNT3, KANSL1, CRHR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes.Conclusions and relevanceThe study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets