|SANTOS, DANIEL - University Of Maryland|
|Liu, Ge - George|
|MA, LI - University Of Maryland|
Submitted to: BMC Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/17/2020
Publication Date: 3/6/2020
Citation: Santos, D.J., Cole, J.B., Liu, G., Van Raden, P.M., Ma, L. 2020. Gamevar.f90: A software package for calculating individual gametic diversity. BMC Bioinformatics. 21:100. https://doi.org/10.1186/s12859-020-3417-x.
Interpretive Summary: Traditional selection in livestock and crops chooses parents with the best breeding values, but differences between gametes within individuals have received little attention. Use of genomic selection now allows estimation and selection for gametic variation to discover which parents may produce the most elite progeny. A new, user-friendly software package gamevar.f90 was developed to efficiently estimate gametic variance for each individual in large populations. The program was applied to several traits for U.S. Holstein bulls to demonstrate use in selection. This new information on gametic variation will be useful in future animal and crop breeding programs.
Technical Abstract: Background: Traditional selection in livestock and crops focuses on additive genetic values or breeding values of the individuals. While traditional selection utilizes variation between individuals, differences between gametes within individuals have been less exploited in selection programs. With the successful implementation of genomic selection in livestock and crops, estimation and selection for gametic variation is becoming possible. Results: The gamevar.f90 software is designed to estimate individual-level variance of genetic values of gametes for complex traits in large populations. The software estimates the (co)variances of gametic diversity as well as other diversity parameters that are useful for selection programs and mating designs. The calculation is carried out chromosome by chromosome and can be easily parallelized. The gamevar.f90 program is written in Fortran with efficient computing algorithms in a user-friendly software package with easily-handling input and output files. Finally, we applied the program to estimate gametic variance for hundreds of bulls for net merit, productive life, and livability. The RPTA assuming a future selection intensity (i_f) of 1.5, showed greater variance than GEBV/2, indicating that greater future genetic gains can be obtained with index. Using the relative coefficient of variation, we also estimated with 95% confidence, the sample sizes required to observe 90% variability of the progeny for net merit (or to allow at maximum 10% of change in the EBV predicted from progeny data). Conclusions: Collectively, we develop an efficient computer program package, gamevar.f90, for estimating gametic variance for large numbers of individuals. The novel information on gametic variation will be useful in future animal and crop breeding programs.