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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.
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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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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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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Genomics02:02

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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...
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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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Related Experiment Video

Updated: Dec 6, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.

Dan Zhou1,2, Yi Jiang3,4,5, Xue Zhong6,3

  • 1Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. zdangm@gmail.com.

Nature Genetics
|October 6, 2020
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Summary

We developed joint-tissue imputation (JTI) to improve gene expression prediction by leveraging genetic regulation across tissues. This method, MR-JTI, enhances Mendelian randomization for causal inference, offering greater statistical power and accuracy in genetic studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Predicting gene expression from genetic data is crucial for understanding complex traits.
  • Existing single-tissue imputation methods have limitations in capturing cross-tissue genetic regulation.
  • Interpreting transcriptome-wide association studies is challenging due to variant-level heterogeneity, such as horizontal pleiotropy.

Purpose of the Study:

  • To introduce a joint-tissue imputation (JTI) approach for enhanced gene expression prediction.
  • To develop a Mendelian randomization framework (MR-JTI) for robust causal inference using gene expression data.
  • To provide a comprehensive resource of imputation models and demonstrate improved performance over existing methods.

Main Methods:

  • Joint-tissue imputation (JTI) borrows information across multiple tissue transcriptomes, leveraging shared genetic regulation.
  • The MR-JTI framework incorporates variant-level heterogeneity to improve causal inference and control type I error rates.
  • Imputation models were generated using GTEx and PsychENCODE data, with performance validated through simulations and analysis of biobank data.

Main Results:

  • JTI outperforms single-tissue imputation methods like PrediXcan, Bayesian sparse linear mixed model, and Dirichlet process regression.
  • MR-JTI effectively models variant-level heterogeneity, addressing a key challenge in transcriptome-wide association studies.
  • Analyses and simulations demonstrate substantially improved statistical power, replication rates, and causal mapping accuracy for JTI and MR-JTI.

Conclusions:

  • JTI provides a powerful approach for improving gene expression prediction by integrating cross-tissue genetic information.
  • MR-JTI offers a robust framework for causal inference in genetic studies, enhancing the interpretation of transcriptome-wide association studies.
  • The developed resource and methods significantly advance the ability to map complex traits to underlying gene expression and genetic regulation.