In this paper, we collected ancestry informative marker data from 4,662 Mexican Americans living in Houston, Texas. Our results show that individuals in this population with higher proportions of Native American ancestry were significantly less likely to be obese, but counter-intuitively, were at higher risk of developing diabetes. The study offers new insight into the complex relationship between obesity, genetic ancestry, and diabetes risk in Mexican Americans. Thanks to our collaborators and to the participants in the Mexican American Cohort at MD Anderson.
The Huff lab is happy to announce the advanced online publication of "A genetic mechanism for Tibetan high-altitude adaptation" in the journal Nature Genetics.
This paper provides strong functional evidence that two nonsynonymous variants are responsible for high-altitude adaptation at the EGLN1 locus in Tibet. To our knowledge, this is the only example of a polymorphic coadapted gene complex in a human population. (If anyone knows of other examples, please let us know!)
We are pleased to announce that the paper describing pVAAST (the pedigree Variant Annotation, Analysis, and Search Tool has just been published in Nature Biotechnology.
pVAAST is a software tool that searches whole-exome and whole-genome sequence data in families to identify genetic variants that directly influence disease risk. pVAAST analyzes the DNA sequences of patients, their relatives, and healthy people in a highly automated fashion to provide probabilistic predictions of the specific genetic variants and genes that are increasing the risk of developing disease. pVAAST combines the existing variant prioritization and case-control association features in VAAST with a new linkage analysis method specifically designed for sequence data. This model is broadly similar to traditional linkage analysis but is capable of modeling de novo mutations and is more sensitive in scenarios with incomplete penetrance or locus heterogeneity. pVAAST supports dominant, recessive, and de novo inheritance models, and maintains high power across a wide variety of study designs, from monogenic, Mendelian diseases in a single family to highly polygenic, common diseases involving hundreds of families.
In a separate paper published two weeks ago in Cancer Discovery and led by our collaborators at the University of Utah and the University of Melbourne, we used pVAAST to aid in the discovery that rare variants in the gene RINT1 increase the risk of developing breast cancer and Lynch-Syndrome spectrum cancers.