A0377
Title: Evaluating the impact of trait measurement error on genetic analysis of computer vision-based phenotypes
Authors: Gota Morota - The University of Tokyo (Japan) [presenting]
Abstract: Quantitative genetic analysis of image-derived phenotypes is increasingly being performed for a wide range of traits. Pig body weight estimated by a conventional approach or a computer vision system can be considered as two different measurements of the same trait, but with different sources of phenotyping error. Previous studies have shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, can be influenced by genetics, suggesting that the error is systematic rather than random and is more likely to lead to misleading quantitative genetic analysis results. The effect of trait measurement error is investigated on the genetic analysis of pig body weight (BW). Genomic heritability estimates for trait measurement error were consistently negligible, regardless of the choice of computer vision algorithm. In addition, genome-wide association analysis revealed no overlap between the top markers identified for scale-based BW and those associated with trait measurement error. No evidence is found that the BW trait measurement error could be influenced by genetic factors. This suggests that trait measurement error in pig BW does not contain systematic errors that could bias downstream genetic analysis.
