University of Michigan

About: University of Michigan is a education organization based out in Ann Arbor, Michigan, United States. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 138538 authors who have published 342338 publications receiving 17638979 citations. The organization is also known as: UMich & UM.

Evolution and functional impact of rare coding variation from deep sequencing of human exomes

Abstract: As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Linear Mixed Models: A Practical Guide Using Statistical Software

Abstract: INTRODUCTION What Are Linear Mixed Models (LMMs)? A Brief History of Linear Mixed Models LINEAR MIXED MODELS: AN OVERVIEW Introduction Specification of LMMs The Marginal Linear Model Estimation in LMMs Computational Issues Tools for Model Selection Model-Building Strategies Checking Model Assumptions (Diagnostics) Other Aspects of LMMs Power Analysis for Linear Mixed Models Chapter Summary TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLE Introduction The Rat Pup Study Overview of the Rat Pup Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes and Recommendations THREE-LEVEL MODELS FOR CLUSTERED DATA THE CLASSROOM EXAMPLE Introduction The Classroom Study Overview of the Classroom Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes Recommendations MODELS FOR REPEATED-MEASURES DATA: THE RAT BRAIN EXAMPLE Introduction The Rat Brain Study Overview of the Rat Brain Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes Other Analytic Approaches Recommendations RANDOM COEFFICIENT MODELS FOR LONGITUDINAL DATA: THE AUTISM EXAMPLE Introduction The Autism Study Overview of the Autism Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Calculating Predicted Values Diagnostics for the Final Model Software Note: Computational Problems with the D Matrix An Alternative Approach: Fitting the Marginal Model with an Unstructured Covariance Matrix MODELS FOR CLUSTERED LONGITUDINAL DATA: THE DENTAL VENEER EXAMPLE Introduction The Dental Veneer Study Overview of the Dental Veneer Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes and Recommendations Other Analytic Approaches MODELS FOR DATA WITH CROSSED RANDOM FACTORS: THE SAT SCORE EXAMPLE Introduction The SAT Score Study Overview of the SAT Score Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Recommended Diagnostics for the Final Model Software Notes and Additional Recommendations APPENDIX A: STATISTICAL SOFTWARE RESOURCES APPENDIX B: CALCULATION OF THE MARGINAL VARIANCE-COVARIANCE MATRIX APPENDIX C: ACRONYMS/ABBREVIATIONS BIBLIOGRAPHY INDEX

The geometries of 3-manifolds

Abstract: The theory of 3-manifolds has been revolutionised in the last few years by work of Thurston [66-70]. He has shown that geometry has an important role to play in the theory in addition to the use of purely topological methods. The basic aim of this article is to discuss the various geometries which arise and explain their significance for the theory of 3-manifolds. The idea is that many 3-manifolds admit 'nice' metrics which give one new insight into properties of the manifolds. For the purposes of this article, the nicest metrics are those of constant curvature. An observer in a manifold with a constant curvature metric will see the same picture wherever he stands and in whichever direction he looks. Such manifolds have special topological properties. However, we will also need to consider nice metrics which are not of constant curvature. In this article, I will explain what is meant by a 'nice' metric and describe their classification in dimension three which is due to Thurston. Then I will discuss some of the 3-manifolds which admit these nice metrics and the relationship between their geometric and topological properties. In this introduction all manifolds and metrics will be assumed to be smooth so that the objects of interest are all Riemannian manifolds. It has been known since the nineteenth century that in dimension two there is a very close relationship between geometry and topology. I will start by describing some basic facts about closed surfaces. I will discuss these in more detail in §1. Each

Exome sequencing as a tool for Mendelian disease gene discovery

Abstract: Exome sequencing — the targeted sequencing of the subset of the human genome that is protein coding — is a powerful and cost-effective new tool for dissecting the genetic basis of diseases and traits that have proved to be intractable to conventional gene-discovery strategies. Over the past 2 years, experimental and analytical approaches relating to exome sequencing have established a rich framework for discovering the genes underlying unsolved Mendelian disorders. Additionally, exome sequencing is being adapted to explore the extent to which rare alleles explain the heritability of complex diseases and health- related traits. These advances also set the stage for applying exome and whole-genome sequencing to facilitate clinical diagnosis and personalized disease-risk profiling.