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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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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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Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step

Eduardo P Cappa1,2, Blaise Ratcliffe3, Charles Chen4

  • 1Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina. cappa.eduardo@inta.gob.ar.

Heredity
|February 19, 2022
PubMed
Summary
This summary is machine-generated.

Reducing the number of genetic markers and accounting for spatial variation in forest trees improves genomic evaluation efficiency. Informative markers and spatial adjustments enhance predictive ability for traits like diameter, height, and disease resistance.

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Area of Science:

  • Forestry
  • Quantitative Genetics
  • Plant Breeding

Background:

  • Genomic evaluation in forest trees can be improved by modeling environmental spatial heterogeneity.
  • Reducing the number of genetic markers can lower genotyping costs while maintaining evaluation efficiency.

Purpose of the Study:

  • To investigate the impact of phenotypic data adjustments and reduced marker sets on variance components, breeding value accuracy, and bias in forest tree genomic evaluation.
  • To assess the effectiveness of spatial models and ancestry-informative markers in single-step Genomic Best Linear Unbiased Prediction (ssGBLUP).

Main Methods:

  • Applied a multiple-trait multiple-site ssGBLUP approach to lodgepole pine progeny trials, analyzing diameter at breast height (DBH), height (HT), and western gall rust (WGR) resistance.
  • Compared four scenarios combining two phenotypic data adjustments (experimental design and autoregressive spatial models) with two marker sets (full and reduced subset of 8000 SNPs).
  • Fitted conventional ABLUP model using pedigree information as a control and evaluated prediction bias across all models.

Main Results:

  • Highest heritability estimates were achieved with ABLUP and ssGBLUP using the full marker set and spatial phenotype adjustments.
  • Highest predictive ability was obtained using a reduced marker subset (8000 SNPs) with spatial (DBH, WGR) or design (HT) phenotype corrections.
  • No significant difference in prediction bias was detected among models, with values close to 1.

Conclusions:

  • Selecting informative markers that capture ancestry can enhance predictive ability in forest tree genomic evaluations.
  • The use of spatial correlation structures in phenotypic data increases heritability and reduces prediction bias, with trait-dependent effects on predictive ability.
  • Reduced marker sets, when combined with appropriate spatial adjustments, offer an efficient approach for genomic evaluation in forest trees.