Valdar earns MIRA award from NIH for gene mapping

The National Institute of General Medical Sciences awarded Will Valdar, PhD, $2.95 million over five years to investigate the architecture of complex traits using the Collaborative Cross housed at UNC-Chapel Hill.

Valdar earns MIRA award from NIH for gene mapping click to enlarge Will Valdar, PhD

Will Valdar, PhD, associate professor of genetics, received an Outstanding Investigator Award from the National Institutes of Health to broaden our understanding the genetic architecture of complex traits that are clinically relevant. The grant, totaling nearly $3 million over five years, is a Maximizing Investigators’ Research Award (MIRA) from the National Institute for General Medical Sciences (NIGMS). MIRAs are intended to increase the efficiency of NIGMS funding by providing investigators with greater stability and flexibility to enhance scientific productivity and the chances for important breakthroughs.

The proposed research is designed to lead to improvements in the analysis and design of genetic studies on experimental models of human disease. Because the project focuses on statistical methodology applied to experimental model organism populations, including mouse, rats and fruit flies, the scientific output of the project is expected to be applicable to basic research focusing on any medical condition that can be studied in model organisms.

An important part of this project is the Collaborative Cross, the most genetically diverse panel of mouse strains in the world, which is generated by, housed at, and distributed from the University of North Carolina at Chapel Hill. Traditional lab mice are much more limited in their genetic diversity, and so they have limited use in studies that try to home in on important aspects of diseases in humans. The Collaborative Cross bred together various wild type mice to create wide diversity in the mouse genome. This diversity is comparable to the variation found in the human genome. This helps scientists study diseases that involve various levels of genetic expression across many different genes.

Genetic crosses in model organisms play an essential role in understanding how heritable factors affect medically relevant outcomes. Such crosses have traditionally tended to be on a small scale with limited power to detect genetic effects, limited ability to localize causal variants, and limited options for replication. But in the last decade the emergence of larger-scale interdisciplinary research, cheaper genotyping, and parallel advances in human genetics, has spurred the development of more sophisticated and powerful experimental designs. The most prominent research projects incorporate two modern genetic design concepts: the multiparental population (MPP), whereby each research model is descended from a small, well-characterized set of genetically diverse inbred strains, with the goal of efficiently exploring a wide genetic landscape; and the genetic reference population (GRP), whereby each model is drawn from a large and genetically diverse set of inbred strains, with the goal that the study population – and thereby the studies themselves – can be infinitely replicated. Their combination – the multiparental genetic reference population (MP-GRP) – represents the state-of-the-art in complex trait genetics and has been used in a number of model organisms, including plants, flies, and rodents.

Valdar’s research will focus on the development of statistical and computational tools to advance the design and analysis of studies using MPPs, GRPs and MP-GRPs. Progress on this research front will not only fill significant gaps in studies using MPPs, GRPs and MP-GRPs, but will also provide tools and insights that will allow these designs to be used in new and more powerful ways.

Check out this feature of Valdar's work on the genetics of drug side effects.

The MIRA program distributes funding more widely among the nation's highly talented and promising investigators. More information can be found at the NIGMS website.

Share This:
Filed under: ,