Commingling and segregation analyses are statistical methods that explore whether the variation in a trait is caused by a single gene. Prior to the relatively recent era of widely available DNA markers, these methods, which statistically infer the presence of major genes, were considered to be some of the most important genetic analyses and were even used by Mendel to first discover the basic laws of heredity. These methods were most useful when the trait was due primarily to a single (monogenic) segregating diallelic locus. However, when several loci and/or environmental factors impacted on the trait, they were less successful, and this led to a great deal of methodological development in expanding the “Mendelian” model so that multiple determinants (both genetic and environmental) were considered. This was extremely helpful for analyzing “complex traits” like the obesities, which are known to be determined by multiple susceptibility genes whose effects can be modified by a variety of “environmental” factors. In this chapter, we briefly describe the basic methods and models for detecting commingling and segregation and then review the available obesity literature using these models.
|Title of host publication||Obesity|
|Subtitle of host publication||Genomics and Postgenomics|
|Number of pages||18|
|State||Published - Jan 1 2007|