TY - BOOK ID - 30977734 TI - Applied statistical genetics with R : for population-based association studies PY - 2009 SN - 9780387895536 9780387895543 0387895531 9786612235528 1282235524 038789554X PB - New York Springer Verlag DB - UniCat KW - Genetics KW - R (Computer program language) KW - Genetics, Population KW - Epidemiologic Methods KW - Automatic Data Processing KW - Models, Statistical KW - Statistical methods KW - methods KW - Electronic Data Processing KW - GNU-S (Computer program language) KW - Domain-specific programming languages KW - Model, Statistical KW - Models, Binomial KW - Models, Polynomial KW - Statistical Model KW - Probabilistic Models KW - Statistical Models KW - Two-Parameter Models KW - Binomial Model KW - Binomial Models KW - Model, Binomial KW - Model, Polynomial KW - Model, Probabilistic KW - Model, Two-Parameter KW - Models, Probabilistic KW - Models, Two-Parameter KW - Polynomial Model KW - Polynomial Models KW - Probabilistic Model KW - Two Parameter Models KW - Two-Parameter Model KW - Statistics as Topic KW - Information Processing KW - Bar Codes KW - Computer Data Processing KW - Data Processing, Automatic KW - Information Processing, Automatic KW - Optical Readers KW - Automatic Information Processing KW - Bar Code KW - Codes, Bar KW - Data Processing, Computer KW - Data Processing, Electronic KW - Optical Reader KW - Processing, Automatic Data KW - Processing, Automatic Information KW - Processing, Computer Data KW - Processing, Electronic Data KW - Processing, Information KW - Computers KW - Epidemiologic Method KW - Epidemiological Methods KW - Methods, Epidemiologic KW - Epidemiological Method KW - Method, Epidemiologic KW - Method, Epidemiological KW - Methods, Epidemiological KW - Epidemiology KW - Genetics - Statistical methods KW - Genetics, Population - methods UR - https://www.unicat.be/uniCat?func=search&query=sysid:30977734 AB - "The vast array of molecular level information now available presents exciting opportunities to characterize the genetic underpinnings of complex diseases while discovering novel biological pathways to disease progression. In this introductory graduate level text, Dr. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations. Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field. Couched in the language of biostatistics, this text can be easily adopted for public health and medical school curricula. The text covers key genetic data concepts and statistical principles to provide the reader with a strong foundation in methods for candidate gene and genome-wide association studies. These include methods for unobservable haplotypic phase, multiple testing adjustments, and high-dimensional data analysis. Emphasis is on analysis of data arising from studies of unrelated individuals and the potential interplay among genetic factors and more traditional, epidemiological risk factors for disease. While theoretically rigorous, the analytic techniques are presented at a level that will appeal to researchers and students with limited knowledge of statistical genetics. The text assumes the reader has completed a first course in biostatistics, uses publicly available data sets for illustration, and provides extensive examples using the open source, publicly available statistical software environment R."--Publisher's website. ER -