QUANT GENETICS & ITS CONNECTION WITH BIG DATA...

by Charles J Mode

Overview

The book gives an overview of developments in Quantitative Genetics and variance component analysis in an era of Big Data and Sequenced Genomes. It provides a detailed description of a direct method of estimation that will be a useful means of extracting information from a large set of data that was inconceivable 10 to 20 years ago.The book is a combination of a history of variance component analysis and a forward looking view as to how direct methods of estimation arise from the availability of big data sets and sequenced genomes of each individual in the sample.Many papers and books on quantitative genetics versions of the general linear model from statistics are useful for analyzing the data, using relatively small sets of data. In this book, new methods of direct estimation are introduced and analyzed that are appropriate for an era of big sets of data and sequences genomes. These direct methods of estimation are based on taking conditional expectations rather the methods of least squares that characterize many applications of the general linear model of statistics.

Product Details

ISBN-13:	9789813140707
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	11/02/2016
Sold by:	Barnes & Noble
Format:	eBook
Pages:	180
File size:	12 MB
Note:	This product may take a few minutes to download.

Table of Contents

Preface vii

Acknowledgments ix

Chapter 1 An Example of an Application of Variance and Covariance Models in Quantitative Genetics in the 1950s 1

Background 1

A Retrospective and Prospective View of the Paper by Mode and Robinson (1959) 4

1 Introduction and Theory 5

2 Properties of Covariance Matrices 9

3 A One-Way Classification for Estimating Variance Components of a Quantitative Trait 13

4 A Multivariate Version of the One-Way Classification for Estimating Variance and Covariance Components of Multiple Traits 16

5 An Experimental Design and Data 20

6 A Prospective View of Mode and Robinson (1959) 29

Appendix 32

References 34

Chapter 2 On Fitting a Genetic Model to Data 35

Background 35

A Distribution-Free Test for Major Gene Differences in Quantitative Inheritance 36

1 Introduction 36

2 A Brief Review of Theory 38

3 An Application of the Smirnov Test 40

4 A Look into the Future on Fitting Models to Data in an Era of Sequenced Genomes 44

References 44

Chapter 3 Estimating Effects and Variance Components in Models of Quantitative Genetics in an Era of Sequenced Genomes 47

Abstract 47

1 Introduction 48

2 The Case of One Locus with Multiple Alleles 51

3 A Partition of the Genetic Variance into the Additive and Intra-allelelic Components for the Case of One Autosomal Locus 56

4 Estimation of Parameters and Effects from Data 62

5 The Case of Two Autosomal Loci 67

6 An Overview of the Case of 11 Autosomal Loci 76

Acknowledgments 82

References 83

Chapter 4 Estimating Statistical Measures of Pleiotropic and Epistatic Effects in the Genomic Era 85

Abstract 85

1 Introduction 86

2 Pleiotropism and the Phenotypic, Genetic and Environmental Covariance Matrices for the Case of One Autosomal Locus 88

3 Partitioning the Genetic Covariance Matrix into Component Covariance Matrices for the Case of One Autosomal Locus 95

4 Estimation of Mean Genetic Vector and Covariance Matrices from Data for the Case of One Locus 98

5 Measures of Pleiotropism and Epistasis for the Case of Two Autosomal Loci 104

6 Searching for Unusual Effects and Interactions Among the Alleles at Two Autosomal Loci 111

7 An Overview of Cases for l > 2 Autosomal Loci 114

Acknowledgments 118

References 119

Chapter 5 Direct Estimation of Effects and Tests of Their Statistical Significance for the Case of One Autosomal Locus with Two Alleles 121

Abstract 121

1 Introduction 122

2 Nonidentifiability of Heterozygotes in the Case of One Autosomal Locus 123

3 Estimating Effects Directly for the Case of One Autosomal Locus 128

4 Permutation Tests for Statistical Significance of Estimated Effects 134

5 Testing the Statistical Significance of Estimated Effects Based on Monte Carlo Simulation Methods 138

6 Monte Carlo Simulation Experiments on Simulating Data and Testing the Null Hypotheses 143

7 Two Monte Carlo Simulation Experiments to Simulate Data and Test Null Hypotheses 148

8 Further Developments and Potential Applications 151

Appendix 154

References 161

Subject Index 163