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Biostatistics Research for Genetics and Genomics The challenges for genomics research and its projection to the bedside are (i) to develop biostatistical methods and strategies for incorporation of genetics and genomic factors into medical research ; (ii) to identify genetic variants that contribute to disease and drug response and to good health and resistance to diseases; (iii) to develop genome based approaches to prediction of disease susceptibility and drug response and (iv) to develop new therapeutic approaches using new understanding of genes and pathways. Statistically, these are problems of analysis of variance and statistical prediction. In the last 20 years, novel applications of statistics in gene mapping and new statistical methods have contributed greatly to mapping of hundreds of Mendelian traits and some of the complex diseases. However, as new data such as genome-wide SNPs and haplotypes are generated for identifying genetic variants, many traditional study designs and methods of analysis cannot be directly applied due to high-dimensionality of the data. New statistical designs and methods are required to analyze the ever-increasing high-dimensional genetic and genomics data, including correlating genetic variation to human health and disease using haplotype and comprehensive SNP information, correlating high-throughput genomic data such as microarray gene expression data to various clinical outcomes, and identifying gene-gene and gene-environment interactions. |