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Related Concept Videos

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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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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Heritability01:06

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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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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.
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In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
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mtPGS: Leverage multiple correlated traits for accurate polygenic score construction.

Chang Xu1, Santhi K Ganesh2, Xiang Zhou1

  • 1Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.

American Journal of Human Genetics
|September 16, 2023
PubMed
Summary

We developed multi-trait assisted PGS (mtPGS), a novel method for accurate polygenic scores (PGSs). mtPGS leverages related traits to enhance genetic prediction accuracy for complex traits, advancing personalized medicine.

Keywords:
GWASPGSPRSgenetic correlationgenetic predictiongenome-wide association studiespolygenic risk scorespolygenic scores

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Accurate polygenic scores (PGSs) are crucial for genetic prediction of complex traits.
  • Current PGS methods have limitations in accuracy and scalability.
  • Personalized medicine requires robust genetic prediction tools.

Purpose of the Study:

  • To develop a novel statistical method, multi-trait assisted PGS (mtPGS), for constructing accurate PGSs.
  • To leverage information from multiple related traits to improve PGS accuracy for a target trait.
  • To provide a scalable and robust PGS construction method for large biobank-scale datasets.

Main Methods:

  • Developed mtPGS, a statistical method that borrows SNP effect size similarity between a target trait and relevant traits.
  • mtPGS models shared genetic architecture and accounts for environmental covariance.
  • The method uses summary statistics and a deterministic algorithm for scalable computation.

Main Results:

  • mtPGS demonstrated robust performance in simulations.
  • In UK Biobank data across 25 traits, mtPGS achieved an average accuracy gain of 0.90%-52.91% compared to state-of-the-art methods.
  • The method shows significant improvements in PGS accuracy.

Conclusions:

  • mtPGS offers an accurate, fast, and robust solution for PGS construction.
  • This method enhances genetic prediction for complex traits.
  • mtPGS has the potential to advance personalized medicine applications.