Score: 5. Popular Books. SaplingPlus combines Sapling's acclaimed automatically graded online homework with an extensive suite of engaging multimedia learning resources. The website provides an online companion which helps students to review material in the text.
Griffiths, John Doebley, David A. Wassarman, Catherine Peichel. The new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level. The 12th edition also introduces SaplingPlus, the best online resource to teach students the problem solving skills.
The eighth edition of 'An Introduction to Genetic Analysis' has been extensively revised, shaping its coverage to match current research and thinking in genetics. Griffiths, Jeffrey H. Miller, David T. Students should read the list of learning outcomes before embarking on a chapter. Ideally, after reading a section of the chapter, it is a good idea for a student to go back to the list and match the material covered to an outcome.
This process should be repeated at the end of the chapter by scanning the sections and making a complete match with each outcome as far as possible. In solving the end-of-chapter problems, try to focus effort on the skills described in the learning outcomes. Students should use the learning outcomes for rapid review when studying for exams; they should try to imagine ways that they will be expected to demonstrate understanding through the application of the outcomes.
The general goal of a course in genetics is to learn how to think and work like a geneticist. The new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.
SaplingPlus combines Sapling's acclaimed automatically graded online homework with an extensive suite of engaging multimedia learning resources. The website provides an online companion which helps students to review material in the text. A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.
Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises.
It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management.
Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues.
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