UCLA

Many challenges related to understanding the mystery of missing heritability and discovering the variants involved in human disease require analysis of large datasets that present opportunities for computer scientists. majority of these discoveries were made using a type of genetic study called a genome-wide association study (GWAS). In a GWAS, data from a large number of individuals is collected, including both a measurement of the disease-related trait as well as information on genetic variants from the individual. The field of genetics assumes a standard mathematical model for the relationship between genetic variation and traits or phenotypes. This model is called the polygenic model. An insight into missing heritability emerged from what initially seemed like an unrelated development addressing an orthogonal problem in association studies. GWAS statistics make the same assumptions as linear regression, which assumes the phenotype of each individual is independently distributed. The basis of the mixed model approach to population structure is the insight the proportion of the genome shared corresponds to the expected similarity in the values of the unmodeled factors. The developments in mixed models provide interesting opportunities for phenotype prediction, which is a problem with a rich history in genetics, particularly in the literature on the best linear unbiased predictor (BLUP).