Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
Heritability01:06

Heritability

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" a trait is,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

THE COMPARATIVE EFFECTIVENESS OF INPATIENT VERSUS SKILLED NURSING FACILITY REHABILITATION USING LINKED ACUTE STROKE REGISTRY DATA.

Archives of physical medicine and rehabilitation·2026
Same author

Infant subcortical brain volumes associated with maternal obesity and diabetes: a large multicohort human study.

BMC medicine·2026
Same author

Structural Equation Modeling of Genetic and Residual Covariance Matrices for Multiple-Trait Evaluation in Beef Cattle.

Animals : an open access journal from MDPI·2026
Same author

Genomes to fields 2024 maize genotype by environment prediction competition.

BMC research notes·2026
Same author

Improving polygenic score prediction for underrepresented groups through transfer learning.

Nature communications·2026
Same author

G2P datasets: a hub for genomic datasets for predictive modeling in plants and animals.

G3 (Bethesda, Md.)·2026
Same journal

Comprehensive Analysis of Macrophage Dynamics, CCBE1, and Their Implications in Colorectal Cancer Microenvironment: Insights Into Tumor Progression and Therapeutic Opportunities.

Genetics research·2026
Same journal

Compound Heterozygous ATM Variants Cause Adolescent-Onset Cerebellar and Extrapyramidal Disease Without Telangiectasia in a Consanguineous Pakistani Family.

Genetics research·2026
Same journal

Biological Context-Informed and Population-Stratified Strategies Improve Genetic Diagnosis of CCDC22-Related Disorder.

Genetics research·2026
Same journal

Predicting the Impact of Deleterious Single-Nucleotide Polymorphisms in the p47ING1a Isoform of Human ING1 Gene.

Genetics research·2026
Same journal

Two Novel FBN2 Variants Causing Congenital Contractural Arachnodactyly.

Genetics research·2026
Same journal

Identification of Genetic Diagnostic Markers for Systemic Lupus Erythematosus.

Genetics research·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Genotyping and Quantification of In Situ Hybridization Staining in Zebrafish
05:41

Genotyping and Quantification of In Situ Hybridization Staining in Zebrafish

Published on: January 28, 2020

Inferring genetic values for quantitative traits non-parametrically.

Daniel Gianola1, Gustavo de los Campos

  • 1Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA. gianola@ansci.wisc.edu

Genetics Research
|January 7, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces novel non-parametric methods for inferring genetic values and predicting phenotypes, particularly addressing complex gene interactions in quantitative genetics. The approach enhances prediction accuracy for animal and plant breeding.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Jun 26, 2026

Genotyping and Quantification of In Situ Hybridization Staining in Zebrafish
05:41

Genotyping and Quantification of In Situ Hybridization Staining in Zebrafish

Published on: January 28, 2020

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Genomics

Background:

  • Predicting phenotypes from genotypes is crucial for breeding and evolutionary studies.
  • Complex gene interactions, or epistasis, pose significant challenges for traditional genetic models.
  • Existing variance component models struggle with non-linear and cryptic forms of epistasis.

Purpose of the Study:

  • To develop and evaluate advanced statistical methods for inferring genetic values and predicting phenotypes.
  • To address limitations in current approaches for handling epistatic variability in quantitative genetics.
  • To propose non-parametric definitions of additive effects (breeding values) using molecular information.

Main Methods:

  • Review of current variance component models for epistatic variability.
  • Development of non-parametric definitions for additive effects (breeding values).
  • Application of reproducing kernel Hilbert spaces regression for inference.
  • Numerical evaluation using two stylized examples, including a two-locus non-linear genetic system.

Main Results:

  • Demonstration of non-parametric inference for additive and dominance effects in a simple model.
  • Evaluation of predictive abilities of various models in a complex, non-linear genetic system.
  • Successful application of reproducing kernel Hilbert spaces regression for genetic value prediction.

Conclusions:

  • The proposed non-parametric methods offer a powerful alternative for analyzing complex genetic architectures.
  • These methods improve the prediction of phenotypes in the presence of non-linear gene action.
  • The findings have implications for enhancing accuracy in animal and plant breeding programs and evolutionary genetics.