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

Multiple Allele Traits01:49

Multiple Allele Traits

38.9K
The Concept of Multiple Allelism
38.9K
Multiple Allele Traits01:49

Multiple Allele Traits

15.1K
15.1K
Polygenic Traits01:18

Polygenic Traits

11.5K
11.5K
Polygenic Traits01:18

Polygenic Traits

70.6K
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...
70.6K
Dihybrid Crosses01:18

Dihybrid Crosses

82.8K
Overview
82.8K
X-linked Traits01:19

X-linked Traits

59.7K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
59.7K

You might also read

Related Articles

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

Sort by
Same author

All-cause mortality and suicide after pediatric traumatic brain injury: a 20-year nationwide study in Finland.

Public health·2024
Same author

A novel genomic region on chromosome 11 associated with fearfulness in dogs.

Translational psychiatry·2020
Same author

Two novel genomic regions associated with fearfulness in dogs overlap human neuropsychiatric loci.

Translational psychiatry·2019
Same author

Genetic heterogeneity underlying variation in a locally adaptive clinal trait in Pinus sylvestris revealed by a Bayesian multipopulation analysis.

Heredity·2016
Same author

Using the unified relationship matrix adjusted by breed-wise allele frequencies in genomic evaluation of a multibreed population.

Journal of dairy science·2013
Same author

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach.

Heredity·2013

Related Experiment Video

Updated: Apr 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

5.0K

A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits.

Z Li1,2,3,4, J Möttönen5, M J Sillanpää1,3,4

  • 1Biocenter Oulu, University of Oulu, Oulu, Finland.

Heredity
|July 16, 2015
PubMed
Summary

This study introduces a robust regression method for genetic analysis, improving quantitative trait loci detection in non-normally distributed data. The approach handles outliers and missing data effectively, enhancing statistical power in plant and animal genetics.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.5K

Related Experiment Videos

Last Updated: Apr 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

5.0K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.5K

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Standard linear regression methods for quantitative trait loci (QTL) mapping assume normally distributed residuals, which is often violated in real genetic data.
  • Deviations from normality, such as outliers or data from multiple sources, can reduce the statistical power of traditional methods to detect QTL.
  • Existing methods may struggle with complex trait distributions and missing data, limiting their application in diverse genetic studies.

Purpose of the Study:

  • To develop a robust statistical approach for quantitative trait loci and association mapping that does not rely on the normality assumption.
  • To enhance the detection of genetic associations for multiple quantitative traits, particularly in the presence of non-normal data distributions and outliers.
  • To provide a flexible method capable of addressing challenges like missing phenotype data in multi-trait analyses.

Main Methods:

  • A robust multiple-locus regression approach utilizing the least absolute deviation (LAD) objective function, assuming multivariate Laplace distributed errors with heavier tails than normal.
  • Integration of a group LASSO (Least Absolute Shrinkage and Selection Operator) penalty for shrinkage estimation of marker effects and modeling genetic correlations among phenotypes.
  • Application of the LAD-LASSO method to handle outlying observations and missing phenotype data by treating them as extreme values.

Main Results:

  • The proposed LAD-LASSO method demonstrates reduced sensitivity to outliers compared to standard least squares regression.
  • The approach effectively analyzes multiple quantitative traits without a normality assumption, improving statistical power for QTL detection.
  • Simulations and real data analyses confirm the efficiency and robustness of the LAD-LASSO method, particularly for skewed phenotypes and missing data imputation.

Conclusions:

  • The LAD-LASSO regression approach offers a powerful and robust alternative for genetic association studies with non-normally distributed quantitative traits.
  • This method enhances the ability to detect quantitative trait loci and understand genetic correlations in complex datasets, including those with missing values.
  • The findings have significant implications for improving the accuracy and power of genetic analyses in plant and animal breeding and research.