Genomic growth curves of an outbred pig population

Using quantile regression methodology to evaluate changes in the shape of growth curves in pigs selected for increased feed efficiency based on residual feed intake

Growth rate is a major component of feed efficiency when estimating residual feed intake (RFI). Quantile regression (QR) methodology can be used to identify animals with different growth trajectories. The objective of this study was to evaluate the use of QR to identify phenotypic and genetic differences in pigs selected for low RFI. Using performance data on 750 Yorkshire pigs selected for low RFI, individual average daily gain (ADG), average daily feed intake (ADFI), RFI and Gompertz growth curve parameters (asymptotic weight (a), inflection point (b) and decay parameter (c)) were estimated for each pig. Using QR methodology, three Gompertz growth curves were estimated for the whole population for three quantiles (0.1, 0.5 and 0.9) of the BW data. Each animal was classified into one of the quantile regression groups (QRG) based on their overall Euclidian distance between each observed and estimated BW from the quantile growth curves. These three curves were also estimated using only part of the data (generations -1 to 3, and -1 to 4) in order to evaluate the agreement classification rate of animals from later generations into QRGs. We evaluated the effect of QRG on growth parameters and performance traits. Genetic parameters were estimated for these traits, as well as for QRG. In addition, genetic trends for each QRG were estimated. Three distinct growth curves were observed for animals classified into either quantiles 0.1 (QRG0.1), 0.5 (QRG0.5) or 0.9 (QRG0.9). When only part of the data was used to estimate quantile growth curves, all animals from QRG0.1 were correctly classified in their group. Animals in QRG0.1 had significantly lower ADFI, ADG and RFI, and greater a, b and c than animals in the other groups. Quantile regression groups analysed as a trait was highly heritable (0.41) and had high (0.8) and moderate (0.46) genetic correlations with ADG and RFI, respectively. Selection for reduced RFI increased the number of animals classified as QRG0.1 in the population. Overall, downward genetic trends were observed for all traits as a function of selection for reduced RFI. However, QRG0.1 was the only group that had a positive genetic trend for ADG. Altogether, these results indicate that selection for reduced RFI changes the shape of growth curves in Yorkshire in pigs, and that QR methodology was able to identify animals having different genetic potential for feed efficiency, bringing a new opportunity to improve selection for reduced RFI.

Regularized quantile regression for SNP marker estimation of pig growth curves

Background

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels).

Results

The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others.

Conclusions

RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei

Background

Due to the great advantages in selection accuracy and efficiency, genomic selection (GS) has been widely studied in livestock, crop and aquatic animals. Our previous study based on one full-sib family of Litopenaeus vannamei (L. vannamei) showed that GS was feasible in penaeid shrimp. However, the applicability of GS might be influenced by many factors including heritability, marker density and population structure etc. Therefore it is necessary to evaluate the major factors affecting the prediction ability of GS in shrimp. The aim of this study was to evaluate the factors influencing the GS accuracy for growth traits in L. vannamei. Genotype and phenotype data of 200 individuals from 13 full-sib families were used for this analysis.

Results

In the present study, the heritability of growth traits in L. vannamei was estimated firstly based on the full set of markers (23 K). It was 0.321 for body weight and 0.452 for body length. The estimated heritability increased rapidly with the increase of the marker density from 0.05 K to 3.2 K, and then it tended to be stable for both traits. For genomic prediction on the growth traits in L. vannamei, three statistic models (RR-BLUP, BayesA and Bayesian LASSO) showed similar performance for the prediction accuracy of genomic estimated breeding value (GEBV). The prediction accuracy was improved with the increasing of marker density. However, the marker density would bring a weak effect on the prediction accuracy after the marker number reached 3.2 K. In addition, the genetic relationship between reference and validation population could influence the GS accuracy significantly. A distant genetic relationship between reference and validation population resulted in a poor performance of genomic prediction for growth traits in L. vannamei.

Conclusions

For the growth traits with moderate or high heritability, such as body weight and body length, the number of about 3.2 K SNPs distributed evenly along the genome was able to satisfy the need for accurate GS prediction in the investigated L.vannamei population. The genetic relationship between the reference population and the validation population showed significant effects on the accuracy for genomic prediction. Therefore it is very important to optimize the design of the reference population when applying GS to shrimp breeding.

Heterogeneity of T-Tubules in Pig Hearts

Background

T-tubules are invaginations of the sarcolemma that play a key role in excitation-contraction coupling in mammalian cardiac myocytes. Although t-tubules were generally considered to be effectively absent in atrial myocytes, recent studies on atrial cells from larger mammals suggest that t-tubules may be more numerous than previously supposed. However, the degree of heterogeneity between cardiomyocytes in the extent of the t-tubule network remains unclear. The aim of the present study was to investigate the t-tubule network of pig atrial myocytes in comparison with ventricular tissue.

Methods

Cardiac tissue was obtained from young female Landrace White pigs (45–75 kg, 5–6 months old). Cardiomyocytes were isolated by arterial perfusion with a collagenase-containing solution. Ca²⁺ transients were examined in field-stimulated isolated cells loaded with fluo-4-AM. Membranes of isolated cells were visualized using di-8-ANEPPS. T-tubules were visualized in fixed-frozen tissue sections stained with Alexa-Fluor 488-conjugated WGA. Binary images were obtained by application of a threshold and t-tubule density (TTD) calculated. A distance mapping approach was used to calculate half-distance to nearest t-tubule (HD_TT).

Results & Conclusion

The spatio-temporal properties of the Ca²⁺ transient appeared to be consistent with the absence of functional t-tubules in isolated atrial myocytes. However, t-tubules could be identified in a sub-population of atrial cells in frozen sections. While all ventricular myocytes had TTD >3% (mean TTD = 6.94±0.395%, n = 24), this was true of just 5/22 atrial cells. Mean atrial TTD (2.35±0.457%, n = 22) was lower than ventricular TTD (P<0.0001). TTD correlated with cell-width (r = 0.7756, n = 46, P<0.0001). HD_TT was significantly greater in the atrial cells with TTD ≤3% (2.29±0.16 μm, n = 17) than in either ventricular cells (1.33±0.05 μm, n = 24, P<0.0001) or in atrial cells with TTD >3% (1.65±0.06 μm, n = 5, P<0.05). These data demonstrate considerable heterogeneity between pig cardiomyocytes in the extent of t-tubule network, which correlated with cell size.

Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars

Background

Feed intake and growth are economically important traits in swine production. Previous genome wide association studies (GWAS) have utilized average daily gain or daily feed intake to identify regions that impact growth and feed intake across time. The use of longitudinal models in GWAS studies, such as random regression, allows for SNPs having a heterogeneous effect across the trajectory to be characterized. The objective of this study is therefore to conduct a single step GWAS (ssGWAS) on the animal polynomial coefficients for feed intake and growth.

Results

Corrected daily feed intake (DFI_Adj) and average daily weight measurements (DBW_Avg) on 8981 (n = 525,240 observations) and 5643 (n = 283,607 observations) animals were utilized in a random regression model using Legendre polynomials (order = 2) and a relationship matrix that included genotyped and un-genotyped animals. A ssGWAS was conducted on the animal polynomials coefficients (intercept, linear and quadratic) for animals with genotypes (DFI_Adj: n = 855; DBW_Avg: n = 590). Regions were characterized based on the variance of 10-SNP sliding windows GEBV (WGEBV). A bootstrap analysis (n =1000) was conducted to declare significance. Heritability estimates for the traits trajectory ranged from 0.34-0.52 to 0.07-0.23 for DBW_Avg and DFI_Adj, respectively. Genetic correlations across age classes were large and positive for both DBW_Avg and DFI_Adj, albeit age classes at the beginning had a small to moderate genetic correlation with age classes towards the end of the trajectory for both traits. The WGEBV variance explained by significant regions (P < 0.001) for each polynomial coefficient ranged from 0.2-0.9 to 0.3-1.01 % for DBW_Avg and DFI_Adj, respectively. The WGEBV variance explained by significant regions for the trajectory was 1.54 and 1.95 % for DBW_Avg and DFI_Adj. Both traits identified candidate genes with functions related to metabolite and energy homeostasis, glucose and insulin signaling and behavior.

Conclusions

We have identified regions of the genome that have an impact on the intercept, linear and quadratic terms for DBW_Avg and DFI_Adj. These results provide preliminary evidence that individual growth and feed intake trajectories are impacted by different regions of the genome at different times.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0218-8) contains supplementary material, which is available to authorized users.