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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.

Congratulations also to Dr. Chad Huff upon his selection as a Leading Mentor in Cancer Prevention for 2015, an award he shares with fellow Epidemiology Professor, Dr. Shine Chang. The aim of this award is to acknowledge the extraordinary performance and dedication of mentors to trainees preparing for careers in cancer prevention.

Both award recipients were honored at the Cancer Prevention Grand Rounds on January 9th.

Click here for more photos from the party!

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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!)

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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.

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Learn more about pVAAST or click here to register to download pVAAST as part of the VAAST software package.

SGI (Somatic-Germline Interaction) was presented at PSB 2014 in January and is now freely-available for academic use. SGI is a software package designed to identify statistical interaction between germline variants and somatic mutational events from next-generation sequence data.