Scientists at Johns Hopkins Bloomberg School of Public Health have developed a powerful method for characterizing the broad patterns of genetic contributions to traits and diseases. The new method provides a “big picture” of genetic influences that should be particularly helpful in designing future genetic studies and understanding potential for genetic risk prediction.
The scientists, in a study published on Aug. 13 in the journal Nature Genetics, mined existing data from genetic studies and used novel statistical techniques to obtain estimates of the numbers of DNA variations that contribute to different physical traits and diseases, including height, BMI, childhood IQ, Alzheimer’s disease, diabetes, heart disease and bipolar disorder.
“In terms of practical results, we can now use this method to estimate, for any trait or disease, the number of individuals we need to sample in future studies to identify the majority of the important genetic contributions,” says study senior author Dr. Nilanjan Chatterjee, the Bloomberg Distinguished Professor in the department of biostatistics.
Affordable DNA-sequencing technology became available around the turn of the millennium. With it, researchers have performed hundreds of genome-wide association studies (GWAS) to discover DNA variations that are linked to different diseases or traits. These variations — changes in DNA “letters” at various sites on the genome — are called single nucleotide polymorphisms (SNPs). Knowing which SNPs are linked to a disease or trait can be useful in gaining biological understanding about how diseases and other traits originate and further progress.
There is also enormous interest in using genetic markers to develop risk-scores which could be used to identify individuals at high or low risk for diseases and then use the information to develop a “precision medicine” approach to disease prevention through targeted interventions.