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Genomic relationships computed from either next-generation sequence or array SNP data.

M Pérez-Enciso1

  • 1Centre for Research in Agricultural Genomics (CRAG), Bellaterra, Spain; Veterinary School, Universitat Autònoma de Barcelona, Bellaterra, Spain; Institut Català de Recerca i Estudis Avancats (ICREA), Barcelona, Spain; Animal Breeding and Genomics Group, Wageningen University, Wageningen, The Netherlands.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|January 9, 2014
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) offers accurate genetic relationship (G) estimates for genomic prediction, but requires sufficient depth. High-density SNP arrays provide a reliable alternative for genomic selection.

Keywords:
CoalescenceSNP ascertainmentgenomic selectionmolecular relationship matrixnext-generation sequencing

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Area of Science:

  • Animal Genomics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genomic prediction models increasingly utilize sequence data due to falling costs of next-generation sequencing (NGS) technologies.
  • The direct interrogation of genomes for causal mutations is theoretically possible with advanced sequencing.
  • Accurate estimation of genetic relationships (G) is crucial for effective genomic selection.

Purpose of the Study:

  • To compare the accuracy of estimating genetic relationships (G) using simulated next-generation sequencing (NGS) data versus ascertained SNP arrays.
  • To evaluate the impact of population structure (bottleneck, breed split, introgression) on G estimation.
  • To determine optimal strategies for genomic selection using different data types.

Main Methods:

  • DNA sequences were simulated using coalescence models under two scenarios: a cattle model (bottleneck, breed split) and a pig model (Chinese introgression).
  • Genetic relationships (G) were estimated using both simulated NGS data (varying depths) and ascertained SNP arrays.
  • The accuracy of G estimates was assessed in relation to sequencing depth, allele frequency standardization, and population characteristics.

Main Results:

  • Next-generation sequencing (NGS) data generally yielded more accurate estimates of genetic relationships (G), provided adequate sequencing depth was achieved.
  • Shallow NGS (e.g., 4×) can lead to distorted G estimates, particularly without standardization by allele frequency.
  • High-density genotyping arrays also produced accurate G estimates and are less noisy than NGS data.
  • Introgression significantly increased genomic variability in differentiation and diversity.

Conclusions:

  • Sufficient sequencing depth is essential for accurate G estimation with NGS in genomic prediction.
  • High-density SNP arrays, potentially developed using influential individuals' sequences, offer a robust alternative for genomic selection.
  • Careful consideration of data type, depth, and standardization is necessary for optimal genomic prediction models.