UCLA

Over the past decade, billions of dollars from public and private funding institutions have been invested in the fields of neuroimaging and human genetics. Recently, researchers have realized that quantitative measures from imaging methods are a useful substrate for testing how genes influence brain structure, behavior, and susceptibility to disease. However, properly merging the two well-developed fields requires complex new methods and statistical models. Many studies in imaging genetics are simplistic in that they generally focus on individual association tests of a small set of genetic variants (usually single nucleotide polymorphisms, SNPs) on a single summary measure of the brain. These studies are great at bridging the gap between the two fields, but they often fail to utilize advanced methods from either field. For example, in genetics we know that our genes interact with each other in complex pathways, only in very rare circumstances is a single mutation or variant responsible for observable differences in phenotype. Modeling genetic associations using simple linear regression to test the effect of a single SNP at a time on an imaging phenotype is a good first step, but methods that more accurately represent the underlying biology will test the joint effect of multiple genetic variants simultaneously. The flipside of this is a similar problem, our brains are under strong genetic control, but our differences are extremely complex; using quantitative summary measures from imaging data will miss fine-grained differences between subjects.