Statistical Analysis of High-Throughput Genetic Data Jiahua Chen (University of British Columbia),

Recent years have seen the rapid accumulation of various types of genomic information due to concerted efforts by the scientific community and advances in molecular technologies. The publication of the human genome sequences and the sequences of many other species represent a great milestone in our scientific his-tory. In addition, a large number of genetic variants responsible for the diversity seen in a given organism have also been identified. For example, more than 10 million “common ” single nucleotide polymorphisms (SNPs) are estimated to be present in humans, and a many of these have been discovered and documented in the literature and public databases. Parallel to SNP discovery, microarrays have made it possible to examine gene expression levels at the genome level, to study genomic-wide DNA copy number changes, which are ubiquitous in human DNA, but especially in cancer tissue, to identify essentially all the binding targets of a transcription factor under different conditions, to evaluate epigenetic controls of genetic regulation, and to collect other types of information across the whole genome. All of these great successes in knowledge and data acquisition have created opportunities and challenges for statisticians, mathematicians, computer scientists, engineers, physicists, and other quantitative scientists to work closely with biologists and biomed-ical researchers. Mathematical scientists can assist biologists to utilize efficiently such enormous amounts of data, to identify genetic variants underlying human diseases, to dissect biological pathways and addres