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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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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...
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...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Related Experiment Video

Updated: Jun 18, 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

Software engineering the mixed model for genome-wide association studies on large samples.

Zhiwu Zhang1, Edward S Buckler, Terry M Casstevens

  • 1Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu

Briefings in Bioinformatics
|November 26, 2009
PubMed
Summary

Mixed models enhance genome-wide association studies (GWAS) by accounting for relatedness. However, current software struggles with large datasets, necessitating improved computational efficiency and usability for future genetic research.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Mixed models are crucial for detecting phenotype-genotype associations in genome-wide association studies (GWAS), especially with population stratification and relatedness.
  • Increasing sample sizes and marker numbers in GWAS enhance statistical power but also increase computational demands for mixed models.
  • Existing software for mixed models in GWAS lacks an optimal balance of speed, scalability, flexibility, and user-friendliness for large datasets.

Purpose of the Study:

  • To review key elements of mixed model association analysis in GWAS.
  • To evaluate current software packages for mixed model analysis in large-scale genetic studies.
  • To provide recommendations for future software development to address computational challenges.

Main Methods:

  • Review of mixed model methodology for GWAS, including population stratification, kinship estimation, and variance component estimation.
  • Evaluation of existing mixed model software based on computational speed, scalability, modeling flexibility, and ease of use.
  • Analysis of techniques to improve efficiency, such as using best linear unbiased predictors (BLUPs) or residuals.

Main Results:

  • Mixed models are effective but computationally intensive for large GWAS datasets.
  • No single available software package optimally combines speed, scalability, flexibility, and usability.
  • Key methodological aspects include accurate kinship estimation and efficient variance component estimation.

Conclusions:

  • There is a critical need for more efficient and user-friendly software for mixed model analysis in large-scale GWAS.
  • Future software development should focus on optimizing computational performance and scalability.
  • Improving software-user interaction is essential for broader adoption and application in genetic research.