Statistical Methods for Relating Sequence Data to Phenotype

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DESCRIPTION (provided by applicant): One of the most important challenges facing biology today is to make sense of genetic variation. Understanding how genotypic variation translates into phenotypic variation is fundamental to our understanding of evolution, and has enormous practical implications for human health as well as for agriculture and conservation. Witness the large number of genome-wide association studies now underway. The long-term objective of this project is to develop methods for association mapping methods that exploit the power of sequence-level data. The project has 3 main aims: First, the development of theoretical methods to allow efficient analysis of sequence- level genetic. We propose to investigate the effect of different experimental designs and data imputation methods on the power of the study, aiming to find designs that optimize the ability to detect genetic variation that is associated with phenotypic variation. We also propose to develop methods that allow for the unique challenges and opportunities presented by sequence-level data. Second, the development of population genetics models for the evolution of copy number variation [CNV] data. Our proposal will develop models that will allow us to assess the utility of proposed mechanisms for change in copy number, the effects of patterns of copy number variation on patterns of polymorphism in nearby sequence, and will also provide key theoretical under-pinnings for future model-based methods for haplotype inference, for example. Third, the development of theoretical methods to allow efficient analysis of sequence- level data in situations where the distribution of traits of interest is correlated with global features of the data (such as genetic ancestry or location). Our focus is on the integration of mixed-models and cluster-based methods. One of the most important challenges facing biology today is to understand how genetic variation between individuals translates into variation we can see or measure, like blood pressure in humans, or drought tolerance in rice. Our proposal seeks to develop methods that will help us use DNA sequence-level data to understand the genetic causes of human phenotypes such as disease susceptibility.