The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20\% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43\% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48\%) and type 2 diabetes (49\%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis.

RIBICHINI, Flavio Luciano;
2012

Abstract

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20\% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43\% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48\%) and type 2 diabetes (49\%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.
Arthritis; Rheumatoid; genetics, Bayes Theorem, Cardiovascular Diseases; genetics, Case-Control Studies, Celiac Disease; genetics, Diabetes Mellitus; Type 2; genetics, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism; Single Nucleotide; genetics
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/475153
Citazioni
  • ???jsp.display-item.citation.pmc??? 199
  • Scopus 309
  • ???jsp.display-item.citation.isi??? 294
social impact