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Statistical Human Genetics

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Cover of 'Statistical Human Genetics'

Table of Contents

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    Book Overview
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    Chapter 1 Statistical Genetic Terminology
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    Chapter 2 Identification of Genotype Errors
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    Chapter 3 Detecting Pedigree Relationship Errors
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    Chapter 4 Identifying Cryptic Relationships
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    Chapter 5 Estimating Allele Frequencies
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    Chapter 6 Testing Departure from Hardy-Weinberg Proportions
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    Chapter 7 Estimating Disequilibrium Coefficients
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    Chapter 8 Detecting Familial Aggregation
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    Chapter 9 Estimating Heritability from Twin Studies
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    Chapter 10 Estimating Heritability from Nuclear Family and Pedigree Data
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    Chapter 11 Correcting for Ascertainment
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    Chapter 12 Segregation Analysis Using the Unified Model
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    Chapter 13 Design Considerations for Genetic Linkage and Association Studies
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    Chapter 14 Model-Based Linkage Analysis of a Quantitative Trait
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    Chapter 15 Model-Based Linkage Analysis of a Binary Trait
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    Chapter 16 Model-Free Linkage Analysis of a Quantitative Trait
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    Chapter 17 Model-Free Linkage Analysis of a Binary Trait
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    Chapter 18 Single Marker Association Analysis for Unrelated Samples
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    Chapter 19 Single Marker Family-Based Association Analysis Conditional on Parental Information
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    Chapter 20 Single Marker Family-Based Association Analysis Not Conditional on Parental Information
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    Chapter 21 Calibrating Population Stratification in Association Analysis
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    Chapter 22 Cross-Phenotype Association Analysis Using Summary Statistics from GWAS
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    Chapter 23 Haplotype Inference
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    Chapter 24 Multi-SNP Haplotype Analysis Methods for Association Analysis
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    Chapter 25 The Analysis of Ethnic Mixtures
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    Chapter 26 Detecting Multiethnic Rare Variants
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    Chapter 27 Identifying Gene Interaction Networks
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    Chapter 28 Structural Equation Modeling
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    Chapter 29 Mendelian Randomization
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    Chapter 30 Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform
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    Chapter 31 Processing and Analyzing Human Microbiome Data
Attention for Chapter 10: Estimating Heritability from Nuclear Family and Pedigree Data
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Chapter title
Estimating Heritability from Nuclear Family and Pedigree Data
Chapter number 10
Book title
Statistical Human Genetics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7274-6_10
Pubmed ID
Book ISBNs
978-1-4939-7273-9, 978-1-4939-7274-6
Authors

Murielle Bochud

Abstract

Heritability is a measure of familial resemblance. Estimating the heritability of a trait could be one of the first steps in the gene mapping process. This chapter describes how to estimate heritability for quantitative traits from nuclear and pedigree data using the ASSOC program in the Statistical Analysis in Genetic Epidemiology (S.A.G.E.) software package. Estimating heritability rests on the assumption that the total phenotypic variance of a quantitative trait can be partitioned into independent genetic and environmental components. In turn, the genetic variance can be divided into an additive (polygenic) genetic variance, a dominance variance (nonlinear interaction effects between alleles at the same locus) and an epistatic variance (interaction effects between alleles at different loci). The last two are often assumed to be zero. The additive genetic variance represents the average effects of individual alleles on the phenotype and reflects transmissible resemblance between relatives. Heritability in the narrow sense (h (2) ) refers to the ratio of the additive genetic variance to the total phenotypic variance. Heritability is a dimensionless population-specific parameter. ASSOC estimates association parameters (regression coefficients) and variance components from family data. ASSOC uses a linear regression model in which the total residual variance is partitioned, after regressing on covariates, into the sum of random components such as an additive polygenic component, a random sibship component, random nuclear family components, a random marital component, and an individual-specific random component. Assortative mating, nonrandom ascertainment of families, and failure to account for key confounding factors may bias heritability estimates.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 31%
Student > Ph. D. Student 3 23%
Student > Master 2 15%
Student > Bachelor 1 8%
Other 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 15%
Computer Science 2 15%
Neuroscience 2 15%
Agricultural and Biological Sciences 1 8%
Economics, Econometrics and Finance 1 8%
Other 1 8%
Unknown 4 31%