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Using molecular markers to estimate quantitative trait locus parameters: power and genetic variances for unreplicated and replicated progeny.
Author(s) -
Steven J. Knapp,
William C. Bridges
Publication year - 1990
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1093/genetics/126.3.769
Subject(s) - quantitative trait locus , biology , locus (genetics) , genetics , genotype , sigma , inbred strain , population , trait , gene , physics , demography , quantum mechanics , sociology , computer science , programming language
Many of the progeny types used to estimate quantitative trait locus (QTL) parameters can be replicated, e.g., recombinant inbred, doubled haploid, and F3 lines. These parameters are estimated using molecular markers or QTL genotypes estimated from molecular markers as independent variables. Experiment designs for replicated progeny are functions of the number of replications per line (r) and the number of replications per QTL genotype (n). The value of n is determined by the size of the progeny population (N), the progeny type, and the number of simultaneously estimated QTL parameters (q - 1). Power for testing hypotheses about means of QTL genotypes is increased by increasing r and n, but the effects of these factors have not been quantified. In this paper, we describe how power is affected by r, n, and other factors. The genetic variance between lines nested in QTL genotypes (sigma 2n:q) is the fraction of the genetic variance between lines (sigma 2n) which is not explained by simultaneously estimated intralocus and interlocus QTL parameters (phi 2Q); thus, sigma 2n:q = sigma 2n - phi 2Q. If sigma 2n:q not equal to 0, then power is not efficiently increased by increasing r and is maximized by maximizing n and using r = 1; however, if sigma 2n:q = 0, then r and n affect power equally and power is efficiently increased by increasing r and is maximized by maximizing N.r. Increasing n efficiently increases power for a wide range of values of sigma 2n:q.sigma 2n:q = 0 when the genetic variance between lines is fully explained by QTL parameters (sigma 2n = phi 2Q).(ABSTRACT TRUNCATED AT 250 WORDS)

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