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

Polygenic Traits01:18

Polygenic Traits

66.7K
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...
66.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.5K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.5K
Human Genetics01:28

Human Genetics

750
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
750
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

562
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
562
Heritability01:06

Heritability

322
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"...
322
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Genome-wide association study of untargeted plasma metabolomic profiles identifies host genetic regulation in people with HIV.

HGG advances·2026
Same author

Genetic and transcriptomic signatures of host control in HIV-1 infection.

Retrovirology·2026
Same author

Genome-wide association study of paediatric bacteraemia and sepsis.

EBioMedicine·2026
Same author

A polygenic risk score modifies the cardiovascular risk associated with obstructive sleep apnea.

Sleep advances : a journal of the Sleep Research Society·2026
Same author

From mutation to degradation: predicting nonsense-mediated mRNA decay with NMDap.

Biochemical and biophysical research communications·2026
Same author

Human genetics of HIV infection.

Current opinion in virology·2026

Related Experiment Video

Updated: Sep 23, 2025

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

13.1K

Improving polygenic prediction with genetically inferred ancestry.

Olivier Naret1,2, Zoltan Kutalik2,3,4, Flavia Hodel1,2

  • 1School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

HGG Advances
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

Incorporating genetic ancestry improves polygenic scores (PGSs) for disease risk prediction. This new ancestry score method enhances genetic risk estimation, especially for traits influenced by ancestry-specific variants.

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

10.2K
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

3.8K

Related Experiment Videos

Last Updated: Sep 23, 2025

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

13.1K
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

10.2K
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

3.8K

Area of Science:

  • Human Genetics
  • Statistical Genetics
  • Population Genetics

Background:

  • Common diseases are influenced by numerous genetic variants, each with a small effect.
  • Polygenic scores (PGSs) are built from GWAS summary statistics to estimate individual disease risk.
  • Current PGS methods do not fully account for genetic ancestry, potentially limiting prediction accuracy.

Purpose of the Study:

  • To develop and evaluate a novel method for improving polygenic score-based risk prediction by incorporating genetic ancestry information.
  • To introduce an "ancestry score" as a predictor derived from genome-wide genotyping data.
  • To assess the impact of the ancestry score on disease risk prediction across diverse phenotypes and population structures.

Main Methods:

  • Utilized a three-cohort approach: base (discovery), target (prediction), and map (ancestry mapping).
  • Generated mapped principal components (PCs) from genome-wide data using the map cohort.
  • Associated phenotypes with mapped PCs in the base cohort to create an ancestry score for the target cohort.

Main Results:

  • Simulations showed ancestry scores significantly impact traits with ancestry-specific variants.
  • UK Biobank data demonstrated that ancestry scores improve genetic prediction for nine phenotypes to varying degrees.
  • Simulations indicated greater benefits of ancestry scores when base and target cohorts are more genetically heterogeneous.
  • Validation confirmed the approach's utility using UK Biobank and Swiss (CoLaus|PsyCoLaus) cohorts.

Conclusions:

  • The proposed ancestry score effectively enhances polygenic score-based genetic risk prediction.
  • The method's benefit is most pronounced for ancestrally diverse populations and traits with ancestry-specific genetic architecture.
  • This approach offers a more refined tool for personalized disease risk assessment in genomics.