Expected Genetic Contributions and Their Impact on Gene Flow and Genetic Gain

Corresponding author: J. A. Woolliams, Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, United Kingdom., john.woolliams{at}bbsrc.ac.uk (E-mail)

Communicating editor: R. G. SHAW

Long-term genetic contributions (r_i) measure lasting gene flow from an individual i. By accounting for linkage disequilibrium generated by selection both within and between breeding groups (categories), assuming the infinitesimal model, a general formula was derived for the expected contribution of ancestor i in category q (µ_i(q)), given its selective advantages (s_i(q)). Results were applied to overlapping generations and to a variety of modes of inheritance and selection indices. Genetic gain was related to the covariance between r_i and the Mendelian sampling deviation (a_i), thereby linking gain to pedigree development. When s_i(q) includes a_i, gain was related to E[µ_i(q)a_i], decomposing it into components attributable to within and between families, within each category, for each element of s_i(q). The formula for µ_i(q) was consistent with previous index theory for predicting gain in discrete generations. For overlapping generations, accurate predictions of gene flow were obtained among and within categories in contrast to previous theory that gave qualitative errors among categories and no predictions within. The generation interval was defined as the period for which µ_i(q), summed over all ancestors born in that period, equaled 1. Predictive accuracy was supported by simulation results for gain and contributions with sib-indices, BLUP selection, and selection with imprinted variation.

This article has been cited by other articles:

				J. Engelstadter and G. D. D. Hurst The Impact of Male-Killing Bacteria on Host Evolutionary Processes Genetics, January 1, 2007; 175(1): 245 - 254. [Abstract] [Full Text] [PDF]

				L. Sanchez and J. A. Woolliams Impact of Nonrandom Mating on Genetic Variance and Gene Flow in Populations With Mass Selection Genetics, January 1, 2004; 166(1): 527 - 535. [Abstract] [Full Text] [PDF]

				S. Avendano, B. Villanueva, and J. A. Woolliams Expected increases in genetic merit from using optimized contributions in two livestock populations of beef cattle and sheep J Anim Sci, December 1, 2003; 81(12): 2964 - 2975. [Abstract] [Full Text] [PDF]

				L. Sanchez, P. Bijma, and J. A. Woolliams Minimizing Inbreeding by Managing Genetic Contributions Across Generations Genetics, August 1, 2003; 164(4): 1589 - 1595. [Abstract] [Full Text] [PDF]

				M. J. M. Rutten, P. Bijma, J. A. Woolliams, and J. A. M. van Arendonk SelAction: Software to Predict Selection Response and Rate of Inbreeding in Livestock Breeding Programs J. Hered., November 1, 2002; 93(6): 456 - 458. [Full Text] [PDF]

				P. Bijma and J. A. Woolliams Prediction of Rates of Inbreeding in Populations Selected on Best Linear Unbiased Prediction of Breeding Value Genetics, September 1, 2000; 156(1): 361 - 373. [Abstract] [Full Text]

				J. A. Woolliams and P. Bijma Predicting Rates of Inbreeding in Populations Undergoing Selection Genetics, April 1, 2000; 154(4): 1851 - 1864. [Abstract] [Full Text]

				P. Bijma, J. A. M. Van Arendonk, and J. A. Woolliams A General Procedure for Predicting Rates of Inbreeding in Populations Undergoing Mass Selection Genetics, April 1, 2000; 154(4): 1865 - 1877. [Abstract] [Full Text]