By Arnold Saxton

Arnold Saxton, Ph.D., the editor of this quantity, is a professor of animal technological know-how on the college of Tennessee, Knoxville. in the course of years of study and instructing in records and genetics, Dr. Saxton famous the necessity for a how-to creation to SAS computing device research for complex-trait genetics. He assembled sixteen coauthors from around the globe to create this distinctive compilation. Example-rich and experiment-driven, Genetic research of advanced features utilizing SAS demonstrates how one can use SAS and SAS/Genetics to extract solutions out of your quantitative and molecular genetics info. The e-book publications you thru the combination of genetic, statistical, and SAS talents which are wanted, allowing you to use what you will have discovered on your personal experimental info. you will find this a useful source even if you're a researcher, scientist, graduate pupil, bioinformatician, or statistician--or the other SAS consumer attracted to becoming a member of the hugely energetic and interesting box of genetic research.

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**Extra info for Genetic Analysis of Complex Traits Using SAS**

**Example text**

The GLM procedure is used here, as it allows multiple dependent variables to be analyzed, whereas PROC MIXED does not. For regression models with no random effects as in these examples, PROC GLM and PROC MIXED will produce identical results. 3 All possible regressions of two offspring traits on parent traits. The GLM Procedure Number of observations 17 Dependent Variables With Equivalent Missing Value Patterns Dependent Pattern Obs Variables 1 15 CurrSCC 2 12 CurrMilk NOTE: Variables in each group are consistent with respect to the presence or absence of missing values.

The example comes from a data set of Fry and Heinsohn (2002). Thirty-five second chromosome lines of Drosophila melanogaster were measured for larval viability at each of two temperatures, 18°C and 25°C (denoted “LT” and “R,” respectively). The lines were genetically identical except for spontaneous mutations accumulated over 31 generations. The authors were interested in whether mutations had different effects on viability at different temperatures; therefore one of our main goals is to estimate and make inferences about the cross-environment genetic correlation.

Lynch and Walsh (1998) outline other useful properties of least squares regression analysis. Biologically, the degree of resemblance of relatives depends on a variety of factors: the rearing environment of individuals, genetic relationships, etc. There are a variety of relationships among members of an extended family. It is reasonable to assume that closer relationship might lead to more phenotypic resemblance among relatives compared to more distant relationships. If there is no strong genetic relationship or no resemblance among relatives, then phenotype of one relative will not help predict the other.