The overarching goal of the Biostatistics and Informatics Core is to provide statistical, informatic and data science leadership and support for the Program Project. The Core faculty and other researchers are engaged in mission-related research motivated by the Program research and the methodological challenges that arise from the three projects. The Core members have extensive and shared expertise and interests that unite their activities across Projects. By forming a Biostatistics and Informatics Core that functions across projects, we anticipate a more efficient, robust and profound level of support for Program research than that could be achieved if biostatisticians were nested within each project. It allows us to pool and share expertise and resources across the Program in statistical genetics and genomics, statistical and machine learning, and to draw on the broader resources available through multiple participating institutes, such as the Department of Biostatistics at Harvard TH Chan School of Public Health, Section of Epidemiology and Population Sciences at Baylor College of Medicine, and Department of Epidemiology and Biostatistics of Imperial College. Members of the Core have extensive experience in developing and applying methods, tools and resources for common variant analysis in multiethnic genome-wide association studies (GWAS), rare variant analysis of multi-ethnic whole genome sequencing studies, multi-ethnic polygenic risk scores, integrative analysis of different types of data, causal inference using Mendelian Randomization and mediation analysis, risk prediction, and statistical and machine learning methods, and statistical methods for epidemiological studies. They have worked together for years on studies relating to lung cancer in collaboration with the Program investigators. The Core provides an environment for coordinating statistical and data science research, study design, and data analysis across the Program projects, including (1) collaborating with the Administrative Core on data management and data sharing; (2) developing and applying state-of-the-art statistical and machine learning methods that meet the needs of the Program, especially studies in diverse and understudied populations; (3) providing statistical and and data science training of Program students, postdocs, and researchers.