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Related Concept Videos

Heritability01:06

Heritability

421
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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Multiple Allele Traits01:49

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The Concept of Multiple Allelism
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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.
Genes exist in different versions called alleles,...
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Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Related Experiment Video

Updated: Nov 11, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting.

The Tien Mai1, Paul Turner2,3, Jukka Corander4,5

  • 1Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway. t.t.mai@medisin.uio.no.

BMC Bioinformatics
|March 28, 2021
PubMed
Summary

Boosting heritability offers a reliable method for estimating genetic contributions to traits. This approach uses a multiple sample splitting strategy for stable and accurate heritability inference.

Keywords:
Antimicrobial resistanceBoostingHeritabilityLinear model

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Area of Science:

  • Genetics
  • Statistical Genetics

Background:

  • Heritability quantifies genetic contributions to trait variability.
  • Current methods often use random-effect models for computational efficiency.
  • Fixed-effect models for heritability estimation are less explored.

Purpose of the Study:

  • To propose a novel strategy for heritability inference.
  • To develop a method that combines advantageous features of recent techniques.
  • To estimate heritability using high-dimensional linear models.

Main Methods:

  • Introduced "boosting heritability" strategy.
  • Employed a multiple sample splitting technique.
  • Utilized high-dimensional linear models for inference.

Main Results:

  • Demonstrated a stable and accurate heritability estimate.
  • Validated the method with simulated data.
  • Applied the strategy to antibiotic resistance data in *Streptococcus pneumoniae*.

Conclusions:

  • Boosting heritability provides a reliable inference tool.
  • The method is practically useful for heritability estimation.