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Asim Ali

Asim Ali

· Senior Lecturer and Director of Undergraduate StudiesVerified

University of Maryland, College Park · American Studies

Active 2001–2024

h-index17
Citations848
Papers8861 last 5y
Funding
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Research topics

  • Biology
  • Fishery
  • Genetics
  • Computational biology
  • Artificial Intelligence
  • Computer Science
  • Evolutionary biology
  • Structural engineering
  • Ecology
  • Engineering

Selected publications

  • Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

    BMC Genomics · 2021 · 39 citations

    • Computer Science
    • Artificial Intelligence
    • Biology

    BACKGROUND: One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). RESULTS: The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19-0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500-800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. CONCLUSION: These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

  • PacBio Iso-Seq Improves the Rainbow Trout Genome Annotation and Identifies Alternative Splicing Associated With Economically Important Phenotypes

    Frontiers in Genetics · 2021 · 38 citations

    1st authorCorresponding
    • Biology
    • Genetics
    • Computational biology

    ) gene. The reconstructed gene models and their posttranscriptional processing in rainbow trout provide invaluable resources that could be further used for future genetics and genomics studies. Additionally, the study identified characteristic transcription events associated with economically important phenotypes, which could be applied in selective breeding.

  • Genome-wide identification of loci associated with growth in rainbow trout

    BMC Genomics · 2020 · 56 citations

    1st authorCorresponding
    • Biology
    • Genetics
    • Computational biology

    BACKGROUND: Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout. RESULTS: = 0.09). CONCLUSION: The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

Frequent coauthors

Education

  • Ph.D., Biology

    Middle Tennessee State University

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