Harvard School of Public Health
Genetics and Genomics in Population Sciences Research
Statistical methods have long played a central role in understanding the genetic basis of disease. The advent of modern technologies for obtaining high-throughput genomic and 'omic' data, have revolutionized the nature of research in statistical genetics. For example SNP chips containing information on up to a million genetic locations for an individual, whole genome sequence data on all base pairs in individual, whole genome scans detecting the loss or gain of genetic data in specific regions, and epigenetic data including DNA methylation, histome modification, gene expression and RNA sequence data, all offer the promise of better understanding the role of genetic variation in diseases and disorders. Methods for the analysis of these data on samples of individuals, related or in families, are rapidly proliferating.
Current controversies in this area include the role of rare versus common variants in determining the basis of disease, the role of environmental variables, the best approach to handling spurious association due to confounding factors, how to best predict disease risk based on genetic marker information, how to best handle the multiple testing problem and how genetic information can best inform personalized medicine.
Having access to a wealth of genomic data on individuals, as well as detailed clinical, disease and environmental information has led researchers to undertake large-scale studies to determine the genetic factors that influence many common, complex disorders, such as cancers, respiratory and heart disease, and mental illness.
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