Multi-trait phenotypic modeling through factor analysis and bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers

Abstract

Despite high-throughput and large-scale phenotyping becoming easier, interpretation of such data in cattle production remains challenging due to the complex and highly correlated nature of many traits. Underlying biological traits (UBT) of economic importance are defined by a subset of easy-to-measure traits, leading to challenges in making appropriate selection decisions on them. Research on UBT in beef cattle is limited. In this study, the phenotypic data of admixed beef heifers (n = 336) for reproductive, body conformation, and carcass-related traits (traits, t = 35) were used to identify latent variables from factor analysis (FA) that can be characterized as UBT. Given sample size constraints for carcass (n = 161) and other body size-related traits (n = 336), two models were explored. In Model 1, all individual traits were considered (n = 161), while in Model 2, the dataset was split into body size (n = 336) and carcass (n = 161) traits to maximize available heifers per dataset. A combination of FA and Bayesian network (BN) learning was adopted to develop UBT and infer BN structure for subsequent analyses. All heifers (n = 336) were genotyped using GeneSeek Genomic Profiler 150K for Beef Cattle. Following quality checks, 117,373 autosomal SNP markers were retained and used for genomic estimated breeding values (gEBV) in BN learning steps. Using exploratory and confirmatory FA, Body Size (BS) and Body Composition (BC) were identified as UBT for Model 1, explaining 14 phenotypic traits (t = 14). For Model 2, BS, Ovary Size, and Yield Grade (YG) were identified as UBT, explaining 12 phenotypic traits (t = 12). When using gEBV, the causal network structure inferred showed BS contributed to BC in Model 1 and to Ovary Size in Model 2. Therefore, a structure equation-based approach should be used in subsequent modeling for these traits. From Model 2, YG should be modeled univariately. This study is the first to identify UBT in growing admixed heifers using body size, conformation, and carcass traits. We also identified that BC and YG did not explain intra-muscular fat and body density, indicating these two traits should also be modeled univariately.

Keywords: Bayesian network; factor analytic models; heifer development; latent phenotypes; multi-trait modeling; phenomics.

FIGURE 1

Phenotypic measurements (direct and calculated) of yearling beef heifers available for use in exploratory factor analysis. ^*Initial, final, average, and daily gain measurements were recorded for these parameters but only initial measurements were used for the rest of the analyses; ¹Radius at end girth = end girth/2π; ²Radius in the middle = mid-girth/2π; ³Volume (L) = ((π × body length × (radius at end girth ^∧2 + (2 × radius in the middle ^∧2)))/3)/1000; ⁴Density (kg/L) = body weight/volume (L).

FIGURE 2

Heatmap of factor loading scores for Model 1 (all adequate variables) identifying latent variables (ML 1–4) from exploratory factor analysis. Abbreviations: MG, mid girth; HG, heart girth; FG, flank girth; BL, body length; HH, hip height, BWT, body weight; HW, hip width; DENS, body density; MF, intramuscular fat; REA, rib eye area; RMP, rump fat; UHD, uterine horn diameter; LOD, left ovary diameter; AFC, antral follicular count; YG, yield grade; and ROD, right ovary diameter.

FIGURE 3

Factor diagram for Model 1 (all adequate variables) when assigning traits to their highest loading latent variable (ML).

FIGURE 4

Heatmap of factor loading scores for Model 2 combine data for body conformation and ovarian traits identifying latent variables (ML 1–5) from exploratory factor analysis. Abbreviations: DENS, body density; AFC, antral follicular count; UHD, uterine horn diameter; LOD, left ovary diameter; ROD, right ovary diameter; BL, body length; MG, mid girth; HW, hip width; HG, heart girth; BWT, body weight; and HH, hip height.

FIGURE 5

Factor diagram for Model 2 (combined body conformation and ovary traits dataset) when assigning traits to their highest loading latent variable (ML). Abbreviations: BWT, body weight; MG, mid girth; HG, heart girth; HH, hip height; BL, body length; HW, hip width; DENS, body density; AFC, antral follicular count; ROD, right ovary diameter; LOD, left ovary diameter; and UHD, uterine horn diameter.

FIGURE 6

Heatmap of factor loading scores for Model 2 carcass only dataset identifying latent variables (ML 1–2) from exploratory factor analysis. Abbreviations: IMF, intramuscular fat; YG, yield grade; RIB, rib fat; REA, ribeye area; and RMP, rump fat.

FIGURE 7

Factor diagram for Model 2 (carcass only dataset) when assigning traits to their highest loading latent variable (ML). Abbreviations: IMF, intramuscular fat; YG, yield grade; RIB, rib fat; REA, ribeye area; and RMP, rump fat.

FIGURE 8

Bayesian networks learned from (A) Tabu search algorithm and (B) Max-Min Hill-Climbing algorithm to explain interrelationships among composite phenotypes from Models 1 and 2.

FIGURE 9

Genotypically adjusted Bayesian networks learned from (A) Tabu search algorithm and (B) Max-Min Hill-Climbing algorithm to explain corrected interrelationships among composite phenotypes from Models 1 and 2.