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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Quantification of Circulating Pig-Specific DNA in the Blood of a Xenotransplantation Model
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Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs.

Jungjae Lee1, Yongmin Kim2, Eunseok Cho2

  • 1Jung P & C Institute, Inc., 1504 U-TOWER, Yongin-si, Gyeonggi-do 16950, Korea.

Animals : an Open Access Journal From MDPI
|April 30, 2020
PubMed
Summary

Genomic prediction in Korean Duroc pigs improved with higher density SNP panels. Including parental information in deregressed estimated breeding values (DEBVs) enhanced genomic prediction accuracy, regardless of the Bayesian method or genotyping platform used.

Keywords:
Durocgenome-wide association studygenomic breeding valuesingle nucleotide polymorphism

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Genomic Prediction

Background:

  • Genomic evaluation is crucial for livestock breeding, utilizing single nucleotide polymorphism (SNP) genotyping.
  • Understanding informative genomic regions and prediction efficiency is key for optimizing breeding programs.

Purpose of the Study:

  • To investigate informative genomic regions and genomic prediction efficiency in Korean Duroc pigs.
  • To compare two Bayesian approaches (BayesB, BayesC) using moderate-density SNP panels.
  • To evaluate the impact of different SNP panels on genomic prediction accuracy.

Main Methods:

  • Genotyped 1026 Korean Duroc pigs using Illumina60K (61,565 SNPs) and GeneSeek80K (68,528 SNPs) panels.
  • Employed two Bayesian methods (BayesB, BayesC) for genome-wide association study (GWAS) and genomic prediction.
  • Utilized deregressed estimated breeding values (DEBVs) as response variables.

Main Results:

  • Identified significant genomic regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL).
  • The GeneSeek80K panel showed higher prediction accuracy for DAYS (Δ2%) and LMA (Δ2-3%).
  • No accuracy gains were observed with Bayesian approaches across four growth and production traits.

Conclusions:

  • Genomic prediction accuracy is influenced by SNP panel density, with higher density panels being more effective.
  • Incorporating parental information into DEBVs is the most effective strategy for enhancing genomic prediction accuracy in Korean Duroc pigs.
  • The choice of Bayesian method or genotyping platform had minimal impact on prediction accuracy when parental information was included.