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198 Journey into genetics and genomicsRecent advances in genomic research have shown that such integratedtraining is critical. First, biological systems are complex. It is crucial to understandhow the biological systems work. Such knowledge facilitates resultinterpretation and new scientific discovery in population sciences and clinicalsciences. Computational biology plays a pivotal role in understanding the complexityof the biological system and integrating sources of different genomicdata. Statistical methods provide systematic and rigorous tools for analyzingcomplex biological data and allow for making statistical inference accountingfor randomness in data. Second, many complex diseases are likely to begoverned by the interplay of genes and environment. The 2003 IOM Committeeon “Assuring the Public’s Health” has argued that advances in healthwill require a population health perspective that integrates understanding ofbiological and mechanistic science, human behavior, and social determinantsof health. Analysis of GWAS and whole genome sequencing (WGS) data requiresdevelopment of advanced biostatistical, computational, and epidemiologicalmethods for big data. The top SNPs identified from the GWAS andWGS scan often have unknown functions. Interpretation of these findings requiresbioinformatics tools and data integration, e.g., connecting SNP datawith gene expression or RNA-seq data (eQTL data). Furthermore, to increaseanalysis power, integration with other genomic information, such as pathwaysand networks, in statistical analysis is important.Ground breaking research and discovery in the life sciences in the 21stcentury are more interdisciplinary than ever, and students studying withinthe life sciences today can expect to work with a wider range of scientistsand scholars than their predecessors could ever have imagined. One needsto recognize this approach to scientific advancement when training the nextgeneration of quantitative health science students. Rigorous training in thecore statistical theory and methods remains important. In addition, studentsmust have a broad spectrum of quantitative knowledge and skills, especiallyin the areas of statistical methods for analyzing big data, such as statisticaland machine learning methods, more training in efficient computational methodsfor large data, programming, and information sciences. Indeed, analysisof massive genomic data requires much stronger computing skills than whatis traditionally offered in biostatistics programs. Besides R, students are advantageousto learn other programming languages, such as scripts, pythonand perl.The next generation statistical genetic and genomic scientists should userigorous statistical methods to analyze the data, interpret results, harnessthe power of computational biology to inform scientific hypotheses, and workeffectively as leading quantitative scientists with subject-matter scientists engagedin genetic research in basic sciences, population science and clinicalscience. To train them, we need to develop an interdisciplinary curriculum,foster interactive research experiences in laboratory rotations ranging fromwet labs on biological sciences to dry labs (statistical genetics, computationalbiology, and genetic epidemiology), developing leadership and communication