Enhancing animal breeding through quality control in genomic data - a review

Area of Science:

• Animal Genetics
• Genomic Selection
• Quantitative Genetics

Background:

• High-throughput genotyping and sequencing generate vast genomic data for animal breeding.
• Genomic selection utilizes dense marker information for precise trait selection, improving genetic gains.
• Data accuracy is paramount for the success of genomic selection and breeding programs.

Purpose of the Study:

• To review quality control (QC) strategies for genomic data in animal breeding.
• To emphasize the application of QC in genomic selection, genome-wide association studies (GWAS), and population genetics.
• To highlight the critical role of data integrity in advancing animal breeding practices.

Main Methods:

• Review of established QC protocols for genomic data.
• Examination of methods for managing single nucleotide polymorphism (SNP) quality, call rates, minor allele frequency (MAF), and Hardy-Weinberg equilibrium (HWE).
• Discussion of genotype imputation for low-density platforms.

Main Results:

• Inaccurate genomic data leads to flawed predictions and compromises breeding programs.
• Stringent QC is vital for reliable estimates of breeding values and successful genomic selection.
• Effective QC ensures data accuracy and reliability for GWAS and population genetics.

Conclusions:

• Robust QC measures are indispensable for maximizing the benefits of genomic data in animal breeding.
• Data integrity through QC enhances the reliability of genomic predictions.
• Adherence to QC strategies is crucial for advancing animal breeding practices and achieving desired genetic outcomes.