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Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around Quantitative Trait Nucleotides (QTN) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle, and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.
Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around Quantitative Trait Nucleotides (QTN) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle, and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.
The current investigation was undertaken to elucidate the population-stratifying and ancestry-informative markers in Indian, Chinese, and wild yak populations using whole genome resequencing (WGS) analysis while employing various selection strategies ( Delta , Pairwise Wright’s Fixation Index - F ST , and Informativeness of Assignment ) and marker densities (5–25 thousand). The study used WGS data on 105 individuals from three separate yak cohorts i.e., Indian yak ( n = 29), Chinese yak ( n = 61), and wild yak ( n = 15). Variant calling in the GATK program with strict quality control resulted in 1,002,970 high-quality and independent (LD-pruned) SNP markers across the yak autosomes. Analysis was undertaken in toolbox for ranking and evaluation of SNPs (TRES) program wherein three different criteria i.e., Delta , Pairwise Wright’s Fixation Index-F ST , and Informativeness of Assignment were employed to identify population-stratifying and ancestry-informative markers across various datasets. The top-ranked 5,000 (5K), 10,000 (10K), 15,000 (15K), 20,000 (20K), and 25,000 (25K) SNPs were identified from each dataset while their composition and performance was assessed using different criteria. The average genomic breed clustering of Indian, Chinese, and wild yak cohorts with full density dataset (105 individuals with 1,002,970 markers) was 81.74%, 80.02%, and 83.62%, respectively. Informativeness of Assignment criterion with 10K density emerged as the best combination for three yak cohorts with 86.94%, 96.46%, and 98.20% clustering for Indian, Chinese, and wild yak, respectively. There was an average increase of 7.56%, 22.72%, and 30.35% in genomic breed clustering scores of Indian, Chinese, and wild yak cohorts over the estimates of the original dataset. The selected markers showed overlap multiple protein-coding genes within a 10 kb window including ADGRB3 , ANK1 , CACNG7 , CALN1 , CHCHD2 , CREBBP , GLI3 , KHDRBS2 , and OSBPL10 . This is the first report ever on elucidating low-density SNP marker sets with population-stratifying and ancestry-informative properties in three yak groups using WGS data. The results gain significance for application of genomic selection using cost-effective low-density SNP panels in global yak species. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-024-10924-9.
With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops’ audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research.
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