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A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

Author(s): Sapp Robyn | Spangler Matthew | Rekaya Romdhane | Bertrand J Keith

Journal: Genetics Selection Evolution
ISSN 0999-193X

Volume: 37;
Issue: 7;
Start page: 615;
Date: 2005;
Original page

Keywords: binary data | fertility | fuzzy logic | simulation | threshold model

Abstract A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.

Tango Jona
Tangokurs Rapperswil-Jona

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