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Related Concept Videos

Epistasis Analysis01:09

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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Inheritance01:25

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Gregor Mendel's pioneering work on the principles of inheritance fundamentally transformed our understanding of how traits are transmitted from generation to generation. His experiments with pea plants laid the groundwork for the discovery of genes, discrete units within organisms that control heredity.
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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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Behavioral Genetics and Its Designs01:23

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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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Epistasis01:39

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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BowSaw: Inferring Higher-Order Trait Interactions Associated With Complex Biological Phenotypes.

Demetrius DiMucci1,2, Mark Kon1,3, Daniel Segrè1,2,4,5,6

  • 1Bioinformatics Graduate Program, Boston University, Boston, MA, United States.

Frontiers in Molecular Biosciences
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

BowSaw, a new machine learning tool, interprets biological complexity by identifying key variables in large datasets. It uncovers complex microbial patterns linked to Crohn's disease, offering new mechanistic insights.

Keywords:
Boolean rulescomplex phenotypesdecision treeepistasishigh-order interactionsmicrobiomerandom forest

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Machine learning aids in interpreting biological complexity from large datasets (genomic, transcriptomic, metagenomic).
  • Understanding how variables contribute to model output offers mechanistic insights beyond black-box approaches.

Purpose of the Study:

  • To develop a novel suite of algorithms, BowSaw, for interpreting machine learning models.
  • To identify combinations of variables (rules) frequently used in classification by random forests.
  • To generate testable biological hypotheses from complex biological data.

Main Methods:

  • Developed BowSaw, a suite of algorithms leveraging random forest structure.
  • Applied BowSaw to simulated data to assess rule recovery accuracy under noise.
  • Utilized BowSaw on Human Microbiome Project data for microbial association analysis.

Main Results:

  • BowSaw accurately recovered complex Boolean rules from simulated data, even with high noise.
  • Identified novel, high-order combinations of microbial taxa associated with Crohn's disease in the Human Microbiome Project dataset.

Conclusions:

  • BowSaw offers a new method for extracting mechanistic insights from machine learning models.
  • The approach facilitates hypothesis generation for complex diseases like Crohn's disease.
  • Leveraging random forest structures enhances the interpretability of biological data analysis.