NEW YORK (GenomeWeb News) – The genetic risk for common, complex disease likely lies in many common SNPs combined with a few rare causal variants, according to a Nature Genetics study by an international team that developed a new statistical strategy for delving into the genetic architecture of such diseases.
"Our study reinforces a common thread in the literature: that many subtle differences throughout the genome explain much of the differences in risk for individuals for all kinds of diseases," co-senior author Soumya Raychaudhuri, a researcher affiliated with the Broad Institute and Brigham and Women's Hospital in Boston, said in a statement.
"This has powerful implications for the genetic architecture of disease, for risk prediction and prognosis, as well as for basic biology and developing new drug targets," Raychaudhuri added.
The researchers used their polygenic risk score-based analysis method to look at how much complex disease heritability can be explained by common variants, starting with rheumatoid arthritis.
Based on genome-wide association data for almost 5,500 individuals with rheumatoid arthritis and more than 22,600 unaffected controls, they estimated that, on top of the risk loci already identified, there are still thousands of common rheumatoid arthritis-associated genetic variants yet to be found.
Specifically, their model suggested that nearly 20 percent of the rheumatoid arthritis risk that's not yet accounted for is spread across roughly 2,231 independent risk SNPs. If so, they reported, that means some 65 percent of overall rheumatoid arthritis heritability is due to additive effects associated with common variants.
The heritability attributed to additive SNPs appeared to be a bit higher when researchers used the same computational strategy to analyze other complex diseases, including celiac disease, myocardial infarction/coronary artery disease, and type 2 diabetes.
For each of these diseases, the investigators found clues that "many hundreds of common SNP associations remain to be identified, with total genetic contributions accounting for the majority of the heritability of disease."
"[O]ur statistical model was broadly applicable to several common diseases, not just rheumatoid arthritis," Broad Institute and Brigham and Women's Hospital researcher Robert Plenge, a co-corresponding author on the Nature Genetics study, said in a statement. "Our study provides a clear strategy for discovering additional risk alleles for these and likely many other common diseases."
In addition to GWAS-related applications, those involved in the study noted that the computational method they developed may also prove useful for helping to track down genetic factors that affect drug response and for interpreting whole-genome sequence data.
"Ideally, whole-genome sequencing in large case-control collections would capture all types of variants (SNPs, indels, and copy number variants) across the entire range of allele frequencies (common to low frequency to private)," they wrote. "However, such a study is prohibitively expensive at this time and comes with its own challenges, both computationally and in the interpretation of the results."
Consequently, they concluded, "the common variant GWAS approach will continue to be a highly productive method of identifying additional risk alleles for common disease."