The development of next-generation sequencing technologies (NGS) has made the use of whole-genome sequence data for routine genetic evaluations possible, which has triggered a considerable interest in animal and plant breeding fields. Here, we investigated whether complete or partial sequence data can improve upon existing SNP (single nucleotide polymorphism) array-based selection strategies by simulation using a mixed coalescence - gene-dropping approach. We simulated 20 or 100 causal mutations (quantitative trait nucleotides, QTN) within 65 predefined ‘gene’ regions, each 10 kb long, within a genome composed of ten 3-Mb chromosomes. We compared prediction accuracy by cross-validation using a medium-density chip (7.5 k SNPs), a high-density (HD, 17 k) and sequence data (335 k). Genetic evaluation was based on a GBLUP method. The simulations showed: (1) a law of diminishing returns with increasing number of SNPs; (2) a modest effect of SNP ascertainment bias in arrays; (3) a small advantage of using whole-genome sequence data vs. HD arrays i.e. ~4%; (4) a minor effect of NGS errors except when imputation error rates are high (≥20%); and (5) if QTN were known, prediction accuracy approached 1. Since this is obviously unrealistic, we explored milder assumptions. We showed that, if all SNPs within causal genes were included in the prediction model, accuracy could also dramatically increase by ~40%. However, this criterion was highly sensitive to either misspecification (including wrong genes) or to the use of an incomplete gene list; in these cases, accuracy fell rapidly towards that reached when all SNPs from sequence data were blindly included in the model. Our study shows that, unless an accurate prior estimate on the functionality of SNPs can be included in the predictor, there is a law of diminishing returns with increasing SNP density. As a result, use of whole-genome sequence data may not result in a highly increased selection response over high-density genotyping.

La zona del Cauca posee resguardos indígenas que han presentado diferentes problemáticas asociadas a la lucha armada en Colombia, lo que ha limitado que las entidades puedan realizar programas de control de enfermedades bovinas. Por lo anterior, el objetivo de este trabajo fue determinar la prevalencia de algunas enfermedades infecciosas de carácter reproductivo en el ganado bovino de tres resguardos indígenas del Cauca, 2017. Se recolectaron muestras sanguíneas de 30 vacas para determinar la prevalencia de Neospora caninum, Brucella abortus, diarrea viral bovina (DVB), rinotraqueitis infecciosa bovina (IBR) y Leptospira sp. serovares Hardjo prajitno, bovis y Pomona. Las pruebas se realizaron por ELISA, rosa de bengala y microaglutinación. Mediante el software R se realizó un análisis descriptivo y se determinaron las prevalencias para cada enfermedad, se estimaron las co-infecciones y se realizó un modelo de regresión logística para determinar el efecto del hato, resguardo, componente racial, grupo etario, número de animales y tamaño del hato sobre la positividad a las enfermedades. Se encontró una prevalencia del 36% para N. caninum, 60% DVB, 30% IBR, 0% B. abortus, Leptospira spp. serovares Harjo prajitno 26%, Hardjo bovis 12% y Pomona 10%. De acuerdo al análisis de riesgo, la seropositividad para DVB e IBR estuvo asociada con el resguardo donde habitaban los animales, en este caso Tacueyó tuvo más riesgo y estuvo directamente relacionado con el área de los hatos, entre mayor área más animales fueron positivos. En general se encontró que la situación de la zona es similar a la de otros sitios de Colombia.

Traditional selection programs for dairy cattle, based on quantitative principles, have worked well and allowed strong selection processes in the world over many decades. The objectives of this work were to estimate linkage disequilibrium (LD) levels at varying SNPs densities, to evaluate the effective population size of Holstein cattle, to characterize runs of homozygosity (ROH) distribution through Holstein cattle from Nariño and, to estimate and compare inbreeding coefficient (F) based on genomic markers information, runs of homozygosity (FROH), genomic relationship matrix (FGRM), and excess of homozygous (FSNP). After quality control, the dataset used was composed of 606 Holstein animals and 22200 SNP markers. PLINK program was used to identify LD, Ne, ROH segment and FROH and FSNP, FGRM was calculated with BLUPF90 family of programs. The average of r2 in all chromosomes was 0.011, the highest r2 was found in BTA3 (0.0323), and the lowest in BTA12 (0.0039). 533 ROH segments were identified in 319 animals; findings obtained in this study suggest that on average 0,28% of Holstein genome is autozygous. Total length of ROH was composed mostly of small segments (ROH1-4Mb and ROH4-8Mb). These segments accounted for approximately 96%, while larger ROH (ROH>8Mb) were 3.37% of all ROH detected. Inbreeding averages FROH, FSNP and FGRM methodologies were 0.28%, 3.11% and 3.36% respectively. The Pearson’s correlation among these different F values was: 0.49 (FROH-FSNP), 0.25 (FROH-FGRM), 0.22 (FSNP-FGRM). The distribution of ROH shared regions identified on 19 autosome chromosomes, cover a relevant number of genes inside these ROH. Our result evidenced lowest LD extension levels compared with other Holstein populations; inbreeding results suggest that FGRM and FSNP may be useful estimators of individual autozygosity in Holstein from Colombia. Genes related with production and reproduction were found, but the most important are the two that may be related to adaptation to Colombian high tropics. This work is a pioneer and be the starting point for programs of genetic improvement and genomic population studies in the country and mainly in high tropic areas where the dairy breeds have an important production.