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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

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Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Trihybrid Crosses02:27

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal chance to...
Pedigree Analysis01:35

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Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials.

Sudipto Banerjee1, Andrew O Finley, Patrik Waldmann

  • 1School of Public Health, University of Minnesota, Minneapolis, MN 55455.

Journal of the American Statistical Association
|August 3, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces efficient Bayesian spatial process models for quantitative genetics. These models accurately estimate genetic variance in large datasets, preventing biased heritability estimates by accounting for spatial effects.

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Spatial statistics

Background:

  • Bayesian hierarchical models are increasingly used in quantitative genetics.
  • Analyzing large, spatially referenced trial datasets with multiple traits presents computational challenges.
  • Existing multivariate models struggle with large datasets due to computationally intensive matrix operations, especially within Markov chain Monte Carlo (MCMC) methods.

Purpose of the Study:

  • To develop computationally feasible spatial process models for inferring additive and dominance genetic variance in large, spatially referenced datasets.
  • To address the computational infeasibility of direct application of multivariate models to large spatial data.
  • To improve the accuracy of heritability estimates by incorporating spatial effects.

Main Methods:

  • Employed spectral decomposition of relationship matrices to avoid expensive matrix inversions in MCMC methods.
  • Utilized a multivariate predictive process for spatial effects, projecting the original process onto a subspace to reduce computational burden.
  • Applied proposed methods to both synthetic datasets and a large-scale Scots pine progeny study.

Main Results:

  • Demonstrated that spectral decomposition significantly reduces computational cost for genetic effects.
  • Showcased the efficiency of the multivariate predictive process for handling spatial effects in large datasets.
  • Validated the methods on complex datasets with multivariate genetic effects and anisotropic spatial residuals.

Conclusions:

  • The proposed spatial process models offer a computationally efficient approach for analyzing large, spatially referenced genetic trial data.
  • Ignoring spatial effects in genetic analyses can lead to significantly downwardly biased heritability estimates.
  • These methods provide a comprehensive analysis, enhancing the reliability of genetic variance inference.