[[{"fid":"405","view_mode":"embedded_left","fields":{"format":"embedded_left","field_file_image_alt_text[und][0][value]":"Barbara Engelhardt","field_file_image_title_text[und][0][value]":"","field_file_caption_credit[und][0][value]":"%3Cp%20class%3D%5C%22p1%5C%22%3EBarbara%20Engelhardt%3C%2Fp%3E%0A","field_file_caption_credit[und][0][format]":"full_html"},"type":"media","link_text":null,"attributes":{"alt":"Barbara Engelhardt","height":250,"width":250,"class":"media-element file-embedded-left"}}]]Consider sequencing the genome of a newborn, and selecting targeted therapeutics early in life to reduce her lifetime risk of addiction, obesity, type II diabetes, or pancreatic cancer. While genome-wide association studies (GWAS) have unquestionably been successful in identifying reproducible genomic risk factors for complex human diseases, the promise of developing therapeutics to reduce the heritable portion of disease risk is far from fulfillment. The essential technological developments to fulfill this promise, however, are mainly in statistics and computation rather than in genomic experimental methods. I describe three genomic studies from my recent work. First, I identified a genetic variant that behaves differently depending on whether or not an individual takes cholesterol-reducing drugs. Second, I found that genetic variants that are associated with different cell traits are co-localized with a large variety of different regulatory mechanisms more often than expected. Third, I developed a model to uncover genetic variants that affect many traits simultaneously, where the trait measurements have substantial technical noise. Throughout, I emphasize statistical and computational challenges, and innovations necessary to fulfill this promise of genomic studies.