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Statistical Human Genetics

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Cover of 'Statistical Human Genetics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Statistical Genetic Terminology
  3. Altmetric Badge
    Chapter 2 Identification of Genotype Errors
  4. Altmetric Badge
    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
  31. Altmetric Badge
    Chapter 30 Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform
  32. Altmetric Badge
    Chapter 31 Processing and Analyzing Human Microbiome Data
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Chapter title
Detecting Familial Aggregation
Chapter number 8
Book title
Statistical Human Genetics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7274-6_8
Pubmed ID
Book ISBNs
978-1-4939-7273-9, 978-1-4939-7274-6
Authors

Adam C. Naj, Terri H. Beaty

Abstract

In addition to characterizing the distribution of genetic features of populations (mutation and allele frequencies; measures of Hardy-Weinberg equilibrium), genetic epidemiology and statistical genetics aim to explore and define the role of genomic variation in risk of disease or variation in traits of interest. To facilitate this kind of exploration, genetic epidemiology and statistical genetics address a series of questions: 1. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Does variation at a particular genomic position tend to cosegregate with disease in families? 4. Do specific genetic variants tend to be carried more frequently by those with disease than by those without these variants in a given population (or across families)? The first question can be examined using studies of familial aggregation or correlation. An ancillary question: "how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?" is typically answered by estimating heritability, the proportion of variance in a trait or in risk to a disease attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on genomic markers in pedigrees with affected members informative for linkage, where meiotic cross-over events are estimated or assessed. The fourth question is answerable using genotype data on genomic markers on unrelated affected and unaffected individuals and/or families with affected members and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 8 and 9 ; segregation in Chapter 12 ; linkage in Chapters 13 - 17 ; and association in Chapters 18 - 20 ), this chapter focuses on statistical methods to address questions of familial aggregation of qualitative phenotypes (e.g., disease status) or quantitative phenotypes.While studies exploring genotype-phenotype correlations are arguably the most important and common type of statistical genetic study performed, these studies are performed under the assumption that genetic contributors at least partially explain risk of a disease or a trait of interest. This may not always be the case, especially with diseases or traits known to be strongly influenced by environmental factors. For this reason, before any of the last three questions described above can be answered, it is important to ask first whether the disease clusters among family members more than unrelated persons, as this constitutes evidence of a possible heritable contribution to disease, justifying the pursuit of studies answering the other questions. In this chapter, the underlying principles of familial aggregation studies are addressed to provide an understanding and set of analytical tools to help answer the question if diseases or traits of interest are likely to be heritable and therefore justify subsequent statistical genetic studies to identify specific genetic causes.

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Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 17%
Student > Ph. D. Student 1 17%
Student > Postgraduate 1 17%
Unknown 3 50%
Readers by discipline Count As %
Unspecified 1 17%
Agricultural and Biological Sciences 1 17%
Medicine and Dentistry 1 17%
Unknown 3 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 October 2017.
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#20,449,496
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