A method to shed light on the genetic underpinnings of complex human traits
By Megan Mayerle, PhD
September 11, 2019
Human genetic studies have been highly instrumental in ushering in the current precision medicine era. Such initiatives have been profoundly successful at identifying genomic regions that cause or contribute to many diseases. However, there have been significant challenges in actually using such information to treat patients. Such challenges are often due to pleiotropy, or when one gene can have multiple, disparate effects, and to the fact that many complex diseases do not have simple underlying genetic causes, and instead arise from a constellation of genetic and environmental factors.
In an article published in Nature Communications, a team of researchers led by CVI member Dr. Erik Ingelsson and Dr. Manuel Rivas has developed and applied a new methodology to address this problem. Building on a mathematical technique known as Singular Value Decomposition, the authors developed DeGAs, or decomposition of genetic associations, and a freely available web app for using their methodology. The technique allows researchers to combine information from a wide variety of sources while preserving data interpretability.
Next, using data from the UK Biobank, the researchers applied DeGAs to try to understand body mass index (BMI), myocardial infarction (MI), and gallstones, all of which are complex human traits and diseases that can arise from a complex combination of genetic and environmental factors. The researchers identified loss-of-function variants in two genes that contribute to obesity, and followed that up with functional experiments in adipocytes to demonstrate a role for these genes in fat cell differentiation, presumably underlying their role in obesity development.
DeGAs provides a starting point that enables scientists to investigate genetic components, their functional relevance, and potential therapeutic targets and is an approach that can be applied to a wide variety of human diseases.
Yosuke Tanigawa, Jiehan Li, Johanne M. Justesen, Heiko Horn, Matthew Aguirre, Christopher DeBoever, Chris Chang, Balasubramanian Narasimhan, Kasper Lage, Trevor Hastie, Chong Y. Park, Gill Bejerano, Erik Ingelsson & Manuel A. Rivas contributed to this study. This work was supported by National Human Genome Research Institute (NHGRI) and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH), as well as a Funai Overseas Scholarship from the Funai Foundation for Information Technology, the Novo Nordisk Foundation, the Stanford Bio-X Program the Stanford Center for Computational, Evolutionary, and Human Genomics and the Stanford ChEM-H Institute.