Novel gene associations for traits from electronic health records
Novel associations of several EHR-derived traits, primarily with rare variants in Generation Scotland: March 2017
Some diseases “run in the family”. That is, they are partially caused by complex genetic factors. A new study from the Quantitative Traits in Health and Disease group at the MRC Institute of Genetics and Molecular Medicine used the Generation Scotland: Scottish Family Health Study (GS:SFHS) to gain insight into the genetics that underlie medically-relevant traits such as blood pressure, heart rate, blood glucose and uric acid levels. The study, published in Genome Medicine, employed genome-wide association studies (GWAS) and confirms previously known associations between genetic regions and traits, but also boasts novel findings, mainly with rare genetic variants that may have become enriched in this population. The value of linking participants’ electronic health records (EHRs) is demonstrated by replicating an association with a well-established genetic region known to affect serum urate levels. GS:SFHS is a family-based population cohort with 20,000 genotyped individuals whose physiological and psychological traits were measured. Additionally, participants consented to linking their electronic health records, which allowed the researchers to study traits that were not specifically collected for research. Humans have, on average, 10 million genetic variants in their genome, but genotyping reveals less than one tenth of these, missing many rare variants. The researchers filled in these blanks using a technique called imputation, drawing on the fact that variants that are near each other tend to be inherited together. This study demonstrates the value of leveraging electronic health records (EHRs) and statistical methods to boost the power of genetic studies at no additional cost. It also reveals novel genetic associations that will be useful for predicting individuals’ risks for developing certain conditions such as hypertension or diabetes.
Nagy R, Boutin TS, Marten J, Huffman JE, Kerr SM, Campbell A, Evenden L, Gibson J, Amador C, Howard DM, Navarro P, Morris A, Deary IJ, Hocking LJ, Padmanabhan S, Smith BH, Joshi P, Wilson JF, Hastie ND, Wright AF, McIntosh AM, Porteous DJ, Haley CS, Vitart V, Hayward C.
Genome Med. 2017 Mar 7;9(1):23. doi: 10.1186/s13073-017-0414-4.