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Related Concept Videos

Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Variability: Analysis01:11

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Heritability01:06

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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
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Variance component estimates for growth traits in beef cattle using selected variants from imputed low-pass sequence

Chad A Russell1, Larry A Kuehn2, Warren M Snelling2

  • 1Department of Animal Science, University of Nebraska, Lincoln, NE 68583, USA.

Journal of Animal Science
|August 16, 2023
PubMed
Summary

Functional variants in beef cattle significantly impact genetic parameter estimation for birth weight and post-weaning gain. Specific variant subsets, like untranslated regions, are key for accurate heritability estimates.

Keywords:
beef cattlebirth weightgenetic parameters, low-pass sequencingpost-weaning gain

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Area of Science:

  • Animal Genetics
  • Genomic Selection
  • Quantitative Genetics

Background:

  • Accurate estimation of genetic parameters is crucial for beef cattle breeding programs.
  • Imputed low-pass sequence (LPS) variants offer a denser marker set than traditional array genotypes.
  • Understanding the functional impact of LPS variants is key to optimizing their use in genomic evaluation.

Purpose of the Study:

  • To assess the impact of imputed low-pass sequence (LPS) variants on the estimation of genetic parameters for birth weight (BWT) and post-weaning gain (PWG) in beef cattle.
  • To identify specific variant subsets with high predictive power for heritability estimation.
  • To compare the performance of LPS variants against traditional array genotypes.

Main Methods:

  • Utilized a beef cattle population (n=2,343) with imputed LPS genotypes.
  • Selected and partitioned variants based on predicted functional impact (low, modifier, moderate, high) and mutation consequence (G1-G6).
  • Constructed genomic relationship matrices (GRMs) using different variant subsets for univariate animal models and compared heritability estimates.

Main Results:

  • Heritability estimates for BWT and PWG varied significantly (BWT: 0.10-0.42, PWG: 0.05-0.38) depending on the variant subset used.
  • Variants in the 'modifier' and G1 (untranslated region) subsets yielded the highest heritability estimates, comparable to using all LPS variants.
  • All LPS variants combined provided similar heritability to chip genotypes, with minimal additional information gained when combined with chip data.

Conclusions:

  • Specific functional variants, particularly those in untranslated regions, are highly informative for estimating genetic parameters in beef cattle.
  • Array genotypes and less consequential LPS variants are in high linkage disequilibrium with causal variants, capturing substantial additive genetic variation.
  • Optimizing variant selection from LPS data can improve the accuracy of genomic evaluations without necessarily requiring denser marker panels.