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 Oslo and 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. By leveraging the genetic overlap between schizophrenia and bipolar disorder, the researchers were able to increase the number of discovered SNPs in schizophrenia and bipolar disorder by a factor of 14.5 and 17.5 respectively. Such a dramatic increase in power for gene discover may lead to a more complete and accurate understanding of the genetic underpinnings of a variety of diseases and may also provide clues on how best to treat them.
Highly heritable diseases and traits are influenced by a large number of genetic variants
“It’s increasingly evident that highly heritable diseases and traits are influenced by a large number of genetic variants in different parts of the genome, each with small effects,” said Ole Andreassen, MD PhD, a professor at the KG Jebsen Centre for Psychosis Research, University of Oslo. “Unfortunately, it’s also increasingly evident that existing statistical methods used for genome-wise association studies (GWAS) are severely underpowered and don’t adequately take advantage of new “Big Data” initiatives aimed at characterizing the function of the genome.”
In another example of the increased power for gene discovery, recently published in Nature Genetics, Tom Hemming Karlsen, leader of Norwegian PSC Research Center, University of Oslo, and colleagues applied these new methods to primary sclerosing cholangitis, a life-threatening liver disease. and more than tripled the number of discoveries relative to standard GWAS methods, identifying 45 new risk SNPs.
New methods boost researchers’ analytical powers
Generally speaking, the new methods boost researchers’ analytical powers by incorporating prior knowledge about the function of SNPs and their pleiotropic relationships among multiple phenotypes. Pleiotropy occurs when one gene influences multiple sets of observed traits or phenotypes.
Andreassen and colleagues believe the new methods could lead to a paradigm shift in GWAS analysis, with profound implications across a broad range of complex traits and disorders.
“There is ever-greater emphasis being placed on expensive whole genome sequencing efforts,”Anders M. Dale, University of California, San Diego School of Medicine said, “but as the science advances, the challenges become larger. The needle in the haystack of traditional GWAS involves searching through on the order of one million SNPs. This will increase 10- to 100-fold, up to 3 billion SNPs. We think these new methodologies allow us to more completely exploit our resources, to extract the most information possible, which has important implications for gene discovery, drug development and more accurately assessing a person’s overall genetic risk of developing a certain disease.”
Publications
- “All SNPs are not created equal: Genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs.”
- “Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional False Discovery Rate.”
- “Dense genotyping of immune-related disease regions identifies nine new risk loci for primary sclerosing cholangitis”