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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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 Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by
Same author

Airway wall thickness and PRISm are associated with cognitive impairment in individuals with cigarette smoking exposure.

Respiratory research·2026
Same author

Polygenic risk scores associate with asthma phenotypes and proteomic analyses implicate IL1R1 in two family-based studies.

medRxiv : the preprint server for health sciences·2026
Same author

GRN rs5848 variant associates with TDP-43 pathology and cancer in opposite directions.

Journal of neuropathology and experimental neurology·2026
Same author

Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.

bioRxiv : the preprint server for biology·2026
Same author

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes.

bioRxiv : the preprint server for biology·2026
Same author

Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.

Brain pathology (Zurich, Switzerland)·2026

Related Experiment Video

Updated: May 8, 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

Statistical Approaches to Combine Genetic Association Data.

Sharon M Lutz1, Tasha Fingerlin, David W Fardo

  • 1Department of Biostatistics, University of Colorado, 13001 E. 17 St, Aurora CO 80045, USA.

Journal of Biometrics & Biostatistics
|September 7, 2013
PubMed
Summary
This summary is machine-generated.

New statistical methods combine diverse data sources to uncover genetic predispositions for complex traits. Careful design and analysis are crucial for accurate genetic epidemiology and statistical genetics insights.

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

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Related Experiment Videos

Last Updated: May 8, 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

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

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

  • Statistical genetics
  • Genetic epidemiology
  • Genomics

Background:

  • Complex traits are influenced by genetic and environmental factors.
  • Understanding genetic predisposition requires analyzing multiple data types.
  • Existing methods may lack the power to detect associations for complex traits.

Purpose of the Study:

  • To explore methodologies for combining diverse genetic data sources.
  • To discuss advancements in statistical genetics and genetic epidemiology.
  • To highlight the importance of data aggregation in genetic research.

Main Methods:

  • Review of statistical methods for data aggregation.
  • Analysis of combining data across genetic variants, biological measures, and studies.
  • Exploration of methodologies in statistical genetics and genetic epidemiology.

Main Results:

  • Data aggregation can increase statistical power for detecting genetic associations.
  • Combining diverse data sources presents challenges in study design and analysis.
  • Novel advances are emerging in the field through integrated data approaches.

Conclusions:

  • Combining multiple data sources is essential for unraveling genetic predisposition to complex traits.
  • Methodological rigor in design, analysis, and interpretation is critical for successful data integration.
  • Future research in statistical genetics and genetic epidemiology will benefit from advanced data aggregation techniques.