With two new methods, UC San Diego scientists hope to
improve genome-wide association studies
As scientists probe and parse the genetic bases of what
makes a human a human (or one human different from another), and vigorously
push for greater use of whole genome sequencing, they find themselves
increasingly threatened by the unthinkable: Too much data to make full sense
of.
In a pair of papers published in the April 25, 2013 issue of
PLOS Genetics, two diverse teams of scientists, both headed by researchers at
the University of California, San Diego School of Medicine, describe novel
statistical models that more broadly and deeply identify associations between
bits of sequenced DNA called single nucleotide polymorphisms or SNPs and say
lead to a more complete and accurate understanding of the genetic underpinnings
of many diseases and how best to treat them.