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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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Law of Independent Assortment02:03

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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Dihybrid Crosses01:18

Dihybrid Crosses

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Overview
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Pleiotropy01:33

Pleiotropy

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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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Law of Segregation01:49

Law of Segregation

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When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
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Chi-square Analysis02:46

Chi-square Analysis

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The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
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Mendelian randomization and pleiotropy analysis.

Xiaofeng Zhu1

  • 1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.

Quantitative Biology (Beijing, China)
|August 13, 2021
PubMed
Summary
This summary is machine-generated.

Mendelian randomization (MR) analysis uses genetic data to infer causality between exposures and outcomes. This review examines current MR methods, highlighting their strengths, weaknesses, and challenges using a high-density lipoprotein cholesterol and coronary artery disease example.

Keywords:
Mendelian randomizationcausalityconfoundinginstrumental variablesummary statistics

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Mendelian randomization (MR) is a popular method for inferring causal relationships between exposures and outcomes.
  • Its rise is linked to the success of genome-wide association studies (GWAS).
  • Various statistical approaches exist, each with specific assumptions.

Purpose of the Study:

  • To review the advantages and disadvantages of existing Mendelian randomization methods.
  • To illustrate the complexities of MR investigations through a practical example.

Main Methods:

  • Review of statistical methodologies for Mendelian randomization.
  • Application of MR methods to investigate the causal effect of high-density lipoprotein cholesterol on coronary artery disease.

Main Results:

  • The article discusses the pros and cons of various Mendelian randomization techniques.
  • An example using high-density lipoprotein cholesterol and coronary artery disease highlights practical challenges in MR studies.

Conclusions:

  • Current MR approaches enable the study of causality between risk factors and outcomes.
  • Novel methods are needed to address multi-source confounding in Mendelian randomization analysis.