+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Discovery of exercise-related genes and pathway analysis based on comparative genomes of Mongolian originated Abaga and Wushen horse


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          The Mongolian horses have excellent endurance and stress resistance to adapt to the cold and harsh plateau conditions. Intraspecific genetic diversity is mainly embodied in various genetic advantages of different branches of the Mongolian horse. Since people pay progressive attention to the athletic performance of horse, we expect to guide the exercise-oriented breeding of horses through genomics research. We obtained the clean data of 630,535,376,400 bp through the entire genome second-generation sequencing for the whole blood of four Abaga horses and ten Wushen horses. Based on the data analysis of single nucleotide polymorphism, we severally detected that 479 and 943 positively selected genes, particularly exercise related, were mainly enriched on equine chromosome 4 in Abaga horses and Wushen horses, which implied that chromosome 4 may be associated with the evolution of the Mongolian horse and athletic performance. Four hundred and forty genes of positive selection were enriched in 12 exercise-related pathways and narrowed in 21 exercise-related genes in Abaga horse, which were distinguished from Wushen horse. So, we speculated that the Abaga horse may have oriented genes for the motorial mechanism and 21 exercise-related genes also provided a molecular genetic basis for exercise-directed breeding of the Mongolian horse.

          Related collections

          Most cited references72

          • Record: found
          • Abstract: found
          • Article: not found

          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

            High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
              • Record: found
              • Abstract: found
              • Article: not found

              A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

              Heng Li (2011)
              Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.

                Author and article information

                Open Life Sci
                Open Life Sci
                Open Life Sciences
                De Gruyter
                26 September 2022
                : 17
                : 1
                : 1269-1281
                Faculty of Life Sciences, Inner Mongolia Agricultural University , Hohhot, Inner Mongolia Autonomous Region, People’s Republic of China
                Department of Reproductive Medicine, Inner Mongolia Maternal and Child Health Care Hospitaly , Hohhot, Inner Mongolia Autonomous Region, People’s Republic of China
                Mongolia-China Joint Laboratory of Applied Molecular Biology, “Administration of the Science Park” CSTI , Ulaanbaatar, Mongolia
                Beijing 8omics Gene Technology Co. Ltd , Beijing, People’s Republic of China
                Faculty of Life Sciences, Nankai University , Tianjin, People’s Republic of China
                Animal Husbandry Workstation of Ewenki Autonomous County , Hulun Buir, Inner Mongolia Autonomous Region, People’s Republic of China
                Bayanta Village of Animal Husbandry and Veterinary Station of Ewenki Autonomous County , Hulun Buir, Inner Mongolia Autonomous Region, People’s Republic of China
                Sheep Collaboration and Innovation Center, Inner Mongolia Universityy , Hohhot, Inner Mongolia Autonomous Region, People’s Republic of China
                © 2022 Jing Pan et al., published by De Gruyter

                This work is licensed under the Creative Commons Attribution 4.0 International License.

                Page count
                Pages: 13
                Research Article

                genome,mongolian horse,athletic performance,exercise
                genome, mongolian horse, athletic performance, exercise


                Comment on this article