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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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...
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Polygenic Traits01:18

Polygenic Traits

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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...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Human Genetics01:28

Human Genetics

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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.
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Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Related Experiment Video

Updated: Dec 13, 2025

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

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Fine-mapping genetic associations.

Anna Hutchinson1, Jennifer Asimit1, Chris Wallace1,2,3

  • 1MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK.

Human Molecular Genetics
|August 4, 2020
PubMed
Summary
This summary is machine-generated.

Identifying causal genetic variants for human traits requires fine-mapping. This review covers methods to pinpoint single or multiple causal variants, incorporating functional data and multiple datasets for improved accuracy.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Thousands of genetic variants are linked to human traits.
  • Distinguishing causal variants from associated ones necessitates a fine-mapping step.

Purpose of the Study:

  • To review current genetic fine-mapping approaches.
  • To discuss advancements accommodating multiple causal variants and functional data.
  • To explore methods for simultaneous fine-mapping across datasets and traits.

Main Methods:

  • Review of computational fine-mapping techniques using summary statistics.
  • Discussion of extensions for multiple causal variants and functional annotations.
  • Exploration of multi-dataset and multi-trait fine-mapping strategies.

Main Results:

  • Basic fine-mapping is fast but assumes a single causal variant.
  • Advanced methods incorporate multiple causal variants and functional data.
  • Simultaneous fine-mapping across ancestries and traits is feasible.

Conclusions:

  • Fine-mapping is crucial for identifying causal genetic variants.
  • Future methods will yield more accurate maps of causal variants for diverse human traits.