The proposed research will develop new statistical analysis methods and computer software for interpreting patterns of genetic variation in human populations. The genome of every human (apart from identical twins) is unique and genetic variation explains much of the variation of features observed among individuals including differences in behavior, disease susceptibility, physical appearance, etc. New technologies are providing vast amounts of information concerning subtle DNA variations among individuals in human populations. A common form of variation arises from single base-pair changes in the DNA sequence caused by point mutation;this involves an accidental insertion of a mismatched base during DNA replication. Point mutations occur at a very low rate but accumulate in the genetic material transmitted from parents to offspring over thousands of generations. DNA variants produced by point mutation are termed single-nucleotide polymorphisms (SNPs) and are an important genetic component of individual variation. High-throughput molecular analysis technologies can produce """"""""genotypes"""""""" for millions of SNPs spread over the genome of an individual. The genotype refers to the combination of SNPs on chromosomes inherited from the mother and father. Patterns of SNPs on individual chromosomes are determined mainly by recombination -- exchanges of DNA segments between chromosomes during meiosis (transmission to eggs or sperm). The proposed research will develop computational tools to infer rates of chromosomal recombination on a fine scale. Complex genetic diseases (Crohn disease, for example) are relatively common and are thought to result from the combined effects of environment and genetics. Family-based disease transmission studies, which have worked well to locate genes causing simple genetic diseases (e.g., cystic fibrosis), have low power to locate genes influencing complex diseases. Another goal of the proposed research will be to provide statistical tools to locate genetic variants in the human genome that increase individual susceptibility to complex genetic disease, using as data population samples of SNPs from disease-affected cases and unaffected controls.
The proposed research will provide new statistical methods and computer software for interpreting data on human population genetic variation across the genome. Specifically, the proposed research will facilitate the development of a fine-scale map of local recombination rates over the human genome;recombination plays an important role in determining frequencies of occurrence of physical traits (and genetic diseases) in human populations. As well, the proposed research will provide new methods for locating genetic changes (mutations) in the human genome that increase individual susceptibility to complex genetic disease such as type II diabetes.
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