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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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...
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...
Genetic Variation01:25

Genetic Variation

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, which...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

You might also read

Related Articles

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

Sort by
Same author

Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.

Genome research·2026
Same author

CEDR: robust consensus cancer subtyping with multi-omics data via ensemble dimensionality reduction.

Briefings in bioinformatics·2026
Same author

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Improving polygenic score prediction for underrepresented groups through transfer learning.

Nature communications·2026
Same author

Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning.

Briefings in bioinformatics·2025
Same author

Multi-Omics Data Integration for Improved Cancer Subtyping via Denoising Autoencoder-Based Multi-Kernel Learning.

Genes·2025
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
Same journal

CLABP: a contrastive learning framework integrating protein language models and structural information for antibacterial peptide prediction.

Briefings in bioinformatics·2026
Same journal

Toward the regularization of E value from BLAST similarity search into a dissimilarity measure as distance function, and the metrication of protein sequence space.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

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

Nonlinear kernel-based high-dimensional inference for set-based genetic association studies.

Zechen Zhang1,2,3, Hui Yang1,2,3, Meilin Zhu1

  • 1Division of Health Statistics, School of Public Health, Hebei Medical University, 361 East Zhongshan Road, Shijiazhuang, Hebei 050017, P.R. China.

Briefings in Bioinformatics
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonlinear framework for genetic association analysis, improving power for complex diseases. The method enhances the discovery of genetic variants contributing to conditions like Alzheimer's disease.

Keywords:
P-value combinationSNP–set associationkernel methodnonlinear high-dimensional inferenceomnibus test

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 29, 2026

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

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex diseases involve nonlinear genetic effects like epistasis, which current linear models often miss.
  • Existing SNP-set association tests lack power and stability for nonlinear or heterogeneous genetic data.

Purpose of the Study:

  • To develop a robust, scalable framework for nonlinear, high-dimensional genetic association analysis.
  • To improve the power and reliability of detecting complex genetic contributions to disease risk.

Main Methods:

  • Proposed a nonlinear high-dimensional inference framework integrating scalable kernel methods.
  • Utilized distance correlation-based screening, kernel PCA with Nyström approximation, and de-sparsified LASSO.
  • Implemented a two-stage omnibus testing strategy for adaptive evidence aggregation.

Main Results:

  • Simulations show the method maintains Type I error control and higher power than existing tests, especially for nonlinear effects.
  • The framework outperforms Sequence Kernel Association Test and adaptive Sum of Powered Score tests in nonlinear scenarios.
  • Identified gene associations with Alzheimer's disease brain volumes linked to neuronal excitability and calcium signaling.

Conclusions:

  • The developed framework offers a powerful and scalable solution for nonlinear set-based inference in genome-wide studies.
  • This expands the analytical tools for understanding complex genetic architectures in diseases.
  • The approach is particularly valuable for dissecting genetic contributions to neurodegenerative disorders.