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X. Lin 191Ilearnedwhilemakingmyjourneyintothisfield.Iwilldiscussafewopenand challenging problems to demonstrate statistical genetics and genomics isa stimulating field with many opportunities for statisticians to make methodologicaland scientific contributions. I will also discuss training the next generationquantitative scientists in the ’omics era.18.2 My move into statistical genetics and genomicsMoving into statistical genetics and genomics is a significant turn in my career.My dissertation work was on GLMMs, i.e., generalized linear mixed models(Breslow and Clayton, 1993). In the first twelve years out of graduate school,I had been primarily working on developing statistical methods for analysis ofcorrelated data, such as mixed models, measurement errors, and nonparametricand semiparametric regression for longitudinal data. When I obtained myPhD degree, I had quite limited knowledge about nonparametric and semiparametricregression using kernels and splines. Learning a new field is challenging.One is more likely to be willing to invest time and energy to learna new field when stimulated by problems in an open new area and identifyingniches. I was fascinated by the opportunities of developing nonparametricand semiparametric regression methods for longitudinal data as little workhad been done in this area and there were plenty of open problems. Such anexperience can be rewarding if timing and environment are right and goodcollaborators are found. One is more likely to make unique contributions toafieldwhenitisstillatanearlystageofdevelopment.Thisexperiencealsospeaks well of the lessons I learned in my journey into statistical genetics andgenomics.After I moved to the Harvard School of Public Health in 2005, I wasinterested in exploring new areas of research. My collaborative projects turnedout to be mainly in genetic epidemiological studies and environmental geneticstudies, a field I had little knowledge about. In the next several years, I wasgradually engaged in several ongoing genome-wide association studies, DNAmethylation studies, and genes and environment studies. I was fascinated bythe challenges in the analysis of large genetic and genomic data, and richmethodological opportunities for addressing many open statistical problemsthat are likely to facilitate new genetic discovery and advance science. Atthe same time, I realized that to make contributions in this field, one has tounderstand genetics and biology well enough in order to identify interestingproblems and develop methods that are practically relevant and useful. In mysabbatical year in 2008, I decided to audit a molecular biology course, whichwas very helpful for me to build a foundation in genetics and understand thegenetic jargon in my ongoing collaborative projects. This experience preparedme to get started working on methodological research in statistical genetics