Genetic parameters for semen quality and quantity traits in five pig lines1

Abstract

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Estimation of genetic parameters and season effects for semen traits in three pig breeds of South China

“…For ABN, our estimates of heritability ranged from 0.15 ± 0.01 to 0.21 ± 0.02, which are well consistent with those (0.15 ± 0.05 to 0.25 ± 0.06) obtained by Marques et al. (). Instead of using individual ejaculate records, Smital et al.…”

“…For MOT, our estimates of heritability (0.11 ± 0.02 to 0.42 ± 0.02) are slightly higher than those obtained by Marques et al. (), who reported estimated heritabilities of 0.08 ± 0.03 to 0.25 ± 0.05 in five pig lines (a synthetic line derived from the cross Large White and Pietrain, a Pietrain based line, a Duroc‐based line, a Large White‐based line and a Landrace‐based line), and are much higher than that (0.05) from Wolf (). For ABN, our estimates of heritability ranged from 0.15 ± 0.01 to 0.21 ± 0.02, which are well consistent with those (0.15 ± 0.05 to 0.25 ± 0.06) obtained by Marques et al.…”

“…According to previous studies of Marques et al. () and Wang et al. (), we chose to use the following criteria for cleaning raw data: (a) ages of boars should be within the range of 8–50 months; (b) records for first semen collection were excluded, and the interval between two subsequent semen collections was within the range of 2–14 days; (c) records on the 4 traits should be within the following ranges: 50–600 ml for VOL, 0.1 × 10 9 to 10 × 10 9 /ml for DEN, 10–99 for MOT and 0–99 for ABN.…”

“…For elaborating an efficient selection programme for semen traits, the estimation of genetic parameters for these traits is essential. There have been a few studies on estimating genetic parameters for semen traits in foreign pig populations (Marques et al., ; Smital, Wolf, & De Sousa, ; Wolf, ). However, these genetic parameter estimates showed a high degree of variability and reflected population‐specific features of genetic parameters due to population‐specific genetic structures (Falconer & Mackay, ).…”

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs

“…All the recommended steps for the GWAS method used were performed in each study and reliable candidate genes with biological meaning were found. In addition, Marques et al [ 42 ] reported high genetic correlations between MOT, PROMOT and ABN, which corroborate our findings regarding the overlapping QTL regions that explained more than 1% of the genetic variance for these traits. Therefore, we believe that the QTL results of the present and previous studies should not be considered as false positives and we conclude that the single-SNP method with pseudo-phenotypes and the amount of data in the previous study were not able to identify some of the relevant QTL related to MOT.…”