Heterogeneity in Statistical Genetics

Heterogeneity in Statistical Genetics
Author: Derek Gordon
Publisher: Springer Nature
Total Pages: 366
Release: 2020-12-16
Genre: Medical
ISBN: 3030611213

Heterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon. In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association. We anticipate that this book will be useful to researchers who want to estimate heterogeneity in their data, develop or apply genetic association statistics where heterogeneity exists, and accurately evaluate statistical power and sample size for genetic association through the application of robust experimental design.

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide
Author: Agency for Health Care Research and Quality (U.S.)
Publisher: Government Printing Office
Total Pages: 236
Release: 2013-02-21
Genre: Medical
ISBN: 1587634236

This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Handbook of Statistical Genomics

Handbook of Statistical Genomics
Author: David J. Balding
Publisher: John Wiley & Sons
Total Pages: 1828
Release: 2019-07-09
Genre: Science
ISBN: 1119429250

A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Meta-analysis and Combining Information in Genetics and Genomics

Meta-analysis and Combining Information in Genetics and Genomics
Author: Rudy Guerra
Publisher: CRC Press
Total Pages: 354
Release: 2016-04-19
Genre: Mathematics
ISBN: 142001062X

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-AnalysisWith the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative infor

Bayesian Analysis of Genetic Association Data, Accounting for Heterogeneity

Bayesian Analysis of Genetic Association Data, Accounting for Heterogeneity
Author: Xiaoquan Wen
Publisher:
Total Pages: 142
Release: 2011
Genre:
ISBN: 9781124869520

In this dissertation research, we tackle the statistical problem of analyzing potentially heterogeneous genetic association data. Most frequently, this type of the data arise from applications of genetic meta-analysis and study of gene-environment interactions. These two types of applications are both critical for us to understand the effects of genetic variants on complex traits.

Comparative Effectiveness Review Methods

Comparative Effectiveness Review Methods
Author: U. S. Department of Health and Human Services
Publisher: Createspace Independent Pub
Total Pages: 226
Release: 2013-05-17
Genre: Medical
ISBN: 9781484997062

The Agency for Healthcare Research and Quality (AHRQ) commissioned the RTI International–University of North Carolina at Chapel Hill (RTI-UNC) Evidence-based Practice Center (EPC) to explore how systematic review groups have dealt with clinical heterogeneity and to seek out best practices for addressing clinical heterogeneity in systematic reviews (SRs) and comparative effectiveness reviews (CERs). Such best practices, to the extent they exist, may enable AHRQ's EPCs to address critiques from patients, clinicians, policymakers, and other proponents of health care about the extent to which “average” estimates of the benefits and harms of health care interventions apply to individual patients or to small groups of patients sharing similar characteristics. Such users of reviews often assert that EPC reviews typically focus on broad populations and, as a result, often lack information relevant to patient subgroups that are of particular concern to them. More important, even when EPCs evaluate literature on homogeneous groups, there may be varying individual treatment for no apparent reason, indicating that average treatment effect does not point to the best treatment for any given individual. Thus, the health care community is looking for better ways to develop information that may foster better medical care at a “personal” or “individual” level. To address our charge for this methods project, the EPC set out to answer six key questions (KQ). Key questions for methods report on clinical heterogeneity include: 1. What is clinical heterogeneity? a. How has it been defined by various groups? b. How is it distinct from statistical heterogeneity? c. How does it fit with other issues that have been addressed by the AHRQ Methods Manual for CERs? 2. How have systematic reviews dealt with clinical heterogeneity in the key questions? a. What questions have been asked? b. How have they pre-identified population subgroups with common clinical characteristics that modify their intervention-outcome association? c. What are best practices in key questions and how these subgroups have been identified? 3. How have systematic reviews dealt with clinical heterogeneity in the review process? a. What do guidance documents of various systematic review groups recommend? b. How have EPCs handled clinical heterogeneity in their reviews? c. What are best practices in searching for and interpreting results for particular subgroups with common clinical characteristics that may modify their intervention-outcome association? 4. What are critiques in how systematic reviews handle clinical heterogeneity? a. What are critiques from specific reviews (peer and public) on how EPCs handled clinical heterogeneity? b. What general critiques (in the literature) have been made against how systematic reviews handle clinical heterogeneity? 5. What evidence is there to support how to best address clinical heterogeneity in a systematic review? 6. What questions should an EPC work group on clinical heterogeneity address? Heterogeneity (of any type) in EPC reviews is important because its appearance suggests that included studies differed on one or more dimensions such as patient demographics, study designs, coexisting conditions, or other factors. EPCs then need to clarify for clinical and other audiences, collectively referred to as stakeholders, what are the potential causes of the heterogeneity in their results. This will allow the stakeholders to understand whether and to what degree they can apply this information to their own patients or constituents. Of greatest importance for this project was clinical heterogeneity, which we define as the variation in study population characteristics, coexisting conditions, cointerventions, and outcomes evaluated across studies included in an SR or CER that may influence or modify the magnitude of the intervention measure of effect (e.g., odds ratio, risk ratio, risk difference).

The Fundamentals of Modern Statistical Genetics

The Fundamentals of Modern Statistical Genetics
Author: Nan M. Laird
Publisher: Springer Science & Business Media
Total Pages: 226
Release: 2010-12-13
Genre: Medical
ISBN: 1441973389

This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.

Meta-analysis and Combining Information in Genetics and Genomics

Meta-analysis and Combining Information in Genetics and Genomics
Author: Rudy Guerra
Publisher: CRC Press
Total Pages: 354
Release: 2016-04-19
Genre: Mathematics
ISBN: 142001062X

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-AnalysisWith the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative infor