Curvas de lactancia e identificación QTLs asociados a características productivas en ganado Holando y cruza HolandoxJersey - Beribe, María José (2020)
The general objective of this thesis was the modeling of lactation curves to estimate milk production traits applied to the identification of chromosomal regions and genes associated with these traits, in Holstein and crossbred HolsteinxJersey cows from the central dairy region of the province of Santa Fe, Argentina.\nIn Chapter 2, descriptive statistics for fertility and survival were generated to ensure that the estimation of productive parameters were reliable, consistent and adequate with the production system under study and avoid distortions in the results of subsequent analyzes. The average age at the first service was 20+3 months (average + standard deviation), the age at the first conception was 21+4 months and the age at the first calving was 30+4 months. The interval from calving to conception interval had an average duration of 139+92 days and the interval between services was 44+32 days. The gestation period had an average duration of 265+49 days and the calving interval was 398+108 days. The average lactation length was 301+129 days and the average longevity of the animals under study was 5.6+2.x years.\nIn Chapter 3, a series of mathematical models to describe the lactation curve for the five productive variables: milk production (PL), protein percentage (PP), protein production (ProdP), fat percentage (PG) and daily fat production (ProdG), were compared. The results showed that the model of random regression using a Legendre polynomial of sixth grade was the best method to model the lactation curves for the five variables evaluated. In Chapter 4, estimates for the production of milk, fat and protein accumulated at 305 days and fat and protein content were obtained from the random regression model using a sixth-grade Legendre polynomial. In general, milk production traits were affected by proportion of Holstein, lactation number, and year and season of calving.\nWith these phenotypes, in Chapter 5, a complete genome association study was conducted using 50,000 SNPs distributed in the bovine genome using mixed linear models, considering the factors that affect the traits studied, population structure and genetic relationships. In this strict context of correction of models and using the adjustment by multiple comparisons of Bonferroni at genome level, no statistically significant SNPs were found associated with any of the productive characteristics considered. However, using a less conservative level of significance and inspecting the Quantil-Quantil graphs, 15 SNPs associated with the productive characters evaluated were identified. The analysis made possible to calculate the proportion of phenotypic variance captured by the SNPs, being 0,16 for PL305 and ProdP305, 0,11 for ProdG305, 0,03 for PGm and 0.09 for PPm. The search for nearby genes was performed according to the gene annotation of the bovine genome corresponding to the UMD3.1 assembly and taking into account the calculated linkage disequilibrium (r2 = 0.22 ± 0.27 at an inter-SNP distance of 25-50Kb). It was found that 11 of the genes identified were associated in previous studies with dairy productive traits (IRS2, VEGFA, TCF7L2, RF00100, DCDC2 and OCA2) and with different aspects of the mammary gland such as metabolism (IRS2, LIN28A), the development (VEGFA, TCF7L2), tissue (LOC525599) and the central ligament (PKHD1). Other genes were associated with\nsomatic cell count (DCDC2), mastitis (RF00100), reproductive parameters such as resumption of ovulation after calving (OCA2), interval between calving (MAPT), age at first insemination (PKHD1) and bovine diseases such as paratuberculosis (ZDHHC14) and bovine leukosis virus (PKHD1).\nThe estimation of parameters related wtih fertility, longevity and production and composition of the milk of animals of different breeds and crosses of dairy cattle under commercial herds of Argentina provide important information for future studies aiming the incorporating fertility and survival characteristics in a future national dairy breeding program of Argentina. The knowledge of genomic regions and genes related to the production and composition of milk in cattle in Argentina is an important and relevant initial contribution to animal genetic improvement in which the genetic merit of cows and bulls can be predicted through genomic selection. At the same time, it make a contribution towards t a better understanding of the molecular mechanisms underlying the characteristics evaluated.