Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
BIOLOGICAL STATISTICS & COMPUTATIONAL BIOLOGY
Non Technical Summary
A grand challenge of the post-human genome sequence era is to develop a detailed understanding of the heritable variation in the human genome, particularly as it relates to complex traits such as height, weight, and disease susceptibility. The goals of this proposal focus on developing statistical, computational, and genetic resources for mapping disease loci in structured populations, and specifically, in human populations from sub-Saharan Africa.
Animal Health Component
(N/A)
Research Effort Categories
Basic
50%
Applied
(N/A)
Developmental
50%
Goals / Objectives
The recent completion of the International Haplotype Map (HapMap) project and the availability of new high-throughput genotyping technologies has ushered in the era of human complex genetic trait genomics. We propose a highly interdisciplinary collaboration among population and quantitative geneticists, statisticians, and molecular anthropologists to identify genes underlying seven heritable traits in 20 African populations. These include: lactase activity, bitter taste perception, height, weight, blood pressure, muscle strength, and handedness. The structure of these populations and the complexity of these traits present a set of challenges for conventional association mapping techniques.
Project Methods
The project will involve development of novel algorithms, statistical methodologies, and computational approaches for addressing the challenge of association mapping in structured population as well as data collection and analysis of populations currently underrepresented in complex trait genetics. The project has three major aims. Aim 1: Statistical methods for fine mapping in highly structured human populations. The goal of this aim is to develop robust approaches for identifying the location of genes underlying complex traits when phenotype may have spurious background correlations with genotypes due to ancestry. We will investigate the utility of Hidden Markov Models and Levy processes for solving these problems. Aim 2: Bayesian variable selection methods for graphical models. Many human traits of interest have non-linear environmental and genetic dependencies. We will develop a class of Graphical Models (GM) within a Bayesian variable selection framework that can
directly account for these effects. Aim 3: Association mapping of morphological traits in African populations. This aim involves high-throughput genotyping of 1,053 genetically, ethnically, and morphologically diverse individuals of African ancestry studied by Dr. Tishkoff. Nearly all of the DNA for this project has already been collected as part of a previously funded NSF project and a large subset of these individuals have already been phenotyped for lactase activity, bitter taste perception, height, weight, blood pressure, muscle strength, and handedness. We will use the methods developed in Aims 1 and 2 (as well as other approaches) to identify genomic regions and map genes associated with these traits.