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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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A flexible bayesian model for testing for transmission ratio distortion.

Joaquim Casellas1, Arianna Manunza2, Anna Mercader3

  • 1Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain joaquim.casellas@uab.cat.

Genetics
|October 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible Bayesian method to detect transmission ratio distortion (TRD) in any population structure. The new approach identified 84 SNPs with significant TRD in pigs, linking to embryonic viability genes.

Keywords:
Bayes factorDurocgenome scanmultinomial distributiontransmission ratio distortion

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Area of Science:

  • Population Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Existing statistical methods for transmission ratio distortion (TRD) are limited to specific experimental designs like F2 populations and backcrosses.
  • Investigating TRD is crucial for understanding genetic inheritance and potential biases in offspring allele frequencies.

Purpose of the Study:

  • To develop and validate a novel Bayesian statistical framework for detecting TRD in diploid species.
  • To accommodate diverse population structures, overcoming limitations of current TRD analysis methods.
  • To apply the method to identify TRD in a porcine population and explore associated biological implications.

Main Methods:

  • A new Bayesian approach utilizing offspring genotypes, parent genotypes, or population-specific allele frequencies.
  • Integration of Bayes factors (BF) for corroborating TRD estimates.
  • Application to simulated datasets for performance evaluation and a real-world porcine dataset (5 half-sib families, 352 offspring) using SNP genotyping.

Main Results:

  • The Bayesian method demonstrated strong statistical performance on simulated data.
  • In the porcine study, 84 single nucleotide polymorphisms (SNPs) showed significant evidence of TRD (BF > 100) after multiple testing correction.
  • Genes in TRD regions were associated with critical biological processes like nucleosome assembly and DNA complex assembly, relevant to embryonic viability.

Conclusions:

  • The developed Bayesian approach offers a flexible and powerful tool for TRD analysis across various population structures.
  • The findings in the porcine population highlight the potential of TRD analysis in identifying genomic regions affecting embryonic viability.
  • This method is expected to advance TRD research in both model and non-model organisms.