Supplementary Materials: Meta-analysis of Quantitative Pleiotropic Traits at Gene Level with Multivariate Functional Linear Models
|
|
- Chrystal Annabel Todd
- 5 years ago
- Views:
Transcription
1 Supplementary Materials: Meta-analysis of Quantitative Pleiotropic Traits at Gene Level with Multivariate Functional Linear Models Appendix A. Type I Error and Power Simulations and Results A.. Type I Error Simulations and Results The scenarios of simulations are given in Table S.. The results of type I error rates are given in Tables S. and S.4. A.. Power Simulation Parameter Settings The simulation parameters of power calculations are given in Table S.. Appendix B. Information and Extra Results of the Eight European Cohorts For each of the eight European cohorts, we performed analysis for four lipid traits and genes. The information of the genes is given in Table S.5. The sample sizes of each trait are presented in Table S.6. The results of one-trait meta-analysis by likelihood ratio tests (LRT) from Tables and of Fan et al. (05) are presented in Tables S.7 and S.. The results of study-based pleiotropy analysis from Table of Wang et al. (05) are presented in Table S.. Tables S.8, S.9, and S.0 present the results of two-trait meta-analysis of lipid traits in European studies using F -approximations based on Pillai-Bartlett trace. Table S.: Simulation Study Settings. Sample sizes are total sample sizes in each study. Covariates represent covariates in each study. EUR refers to the scenario where all three studies had EUR samples. EUR + AA refers to the scenario where studies and had EUR samples and study had AA samples. z is a binary covariate taking values 0 and each with probability 0.5, and z and z are continuous covariates and distributed as standard normal. Scenario Population Sample Sizes Covariates Study Study Study Study Study Study EUR,600,00,00 (z, z ) (z, z ) (z, z ) EUR,600,00,00 z (z, z ) (z, z, z ) EUR+AA,600,00,00 z (z, z ) (z, z, z )
2 Table S.: Simulation Parameter Settings. The constants c l = (c l, c l, c l ) in β ljk = c lj log 0 (MAF k ) of power simulations, l, j =,,, are given in this table for two cases: () homogeneous genetic effect and () heterogeneous genetic effect. Genetic Effect Study (c l ) Percentage of Causal Variants (c ) Homogeneous (0.475, , ) (0.75,.5, ).50 ) (0.5,.5, ) (c ) (c ) (0.475, , ) (0.475,.5, ) (0.5,.5, ) (0.475, (c Heterogeneous ).5, ) (0.475,.5, ) (0.5,.5, ) +(0.5, 0.5, 0.5) +(0.5, 0.5, 0.5) +(0.5, 0.5, 0.5) (c ) (0.475, , ) (0.475,.5, ) (0.5,.5, ) (0.5, 0.5, 0.5) (0.5, 0.5, 0.5) (0.5, 0.5, 0.5)
3 Table S.: Empirical Type I Error Rates of Approximate F -distributed Test Statistics at Different α Levels Based on. 0 6 Simulated Datasets, When All Variants are Rare. Type of Tests is explained in the main text, i.e., Het-F is approximate F -distributed test statistic if genetic effects are heterogeneous and Hom-F is approximate F -distributed test statistic if genetic effects are homogenous, and Scenario is given in Table S.. The results of Basis of Both GVF and β l (t) were based on smoothing both GVF and genetic effect functions β l (t) of model (4), and the results of Basis of beta-smooth Only were based on smoothing β l (t) only approach of model (7). Abbreviation: GVF = Genetic Variant Function. Traits (y, y ) (y, y, y ) Type Approximate F -distributed Test Statistics Additive of Scenario Level α Basis of both GVF & β l (t) Basis of beta-smooth only Model Tests B-spline Fourier B-spline Fourier (9) Het-F Hom-F Het-F Hom-F X X X X X X X X X X X X X X X X X X X X X X X X
4 Table S.4: Empirical Type I Error Rates of Approximate F -distributed Test Statistics at Different α Levels Based on. 0 6 Simulated Datasets, When Some Variants are Rare and Some are Common. Type of Tests is explained in the main text, i.e., Het-F is approximate F - distributed test statistic if genetic effects are heterogeneous and Hom-F is approximate F -distributed test statistic if genetic effects are homogenous, and Scenario is given in Table S.. The results of Basis of Both GVF and β l (t) were based on smoothing both GVF and genetic effect functions β l (t) of model (4), and the results of Basis of beta-smooth Only were based on smoothing β l (t) only approach of model (7). Abbreviation: GVF = Genetic Variant Function. Traits (y, y ) (y, y, y ) Type Approximate F -distributed Test Statistics Additive of Scenario Level α Basis of both GVF & β l (t) Basis of beta-smooth only Model Tests B-spline Fourier B-spline Fourier (9) Het-F Hom-F Het-F Hom-F X X X X X X X x X X X X X X X X X X X X X X X x 4
5 Table S.5: Summary of Genes and the Number of Genetic Variants in Each Gene Region by Mar. 006 (NCBI6/hg8). The number of variants is the number of genetic variants in a region of Start (-5Kb) End (+5Kb) Positions. The gene region of PCSK9 is (557777, 5504), and (55757, ) is the region in the database. # The length is the length of the region in bp. Gene Chromosome Gene Start (-5Kb) - End (+5Kb) Number of Region Positions (bp) Positions (Length # ) Variants PCSK (457) 74 APOB (5644) IGFBP (900) CDKAL (707946) 560 JAZF (6044) 84 LPL (888) CDKNB (640) 64 CDC (646) 65 IDE (4) 7 KIF (77) 6 HHEX (577) 0 TCF7L (747) 58 KCNQ (449) 660 MTNRB (59) 06 HMGA (58) 4 TSPAN (490) 54 HNFA (765) 7 OASL (8950) 08 FTO (40506) 9 LDLR (54467) 4 APOE (6) 5 GIPR (45) 7 Table S.6: Sample Sizes of the Four Lipid Traits for Each of the Seven Studies. Study HDL LDL TG CHOL Dd DIAGEN Dps DRs EXTRA FUSION Stage METSIM Norway Total
6 Table S.7: One-trait Meta-analysis of Lipid Traits in Eight European Cohorts by Homogeneous Likelihood Ratio Tests (Hom- LRT), Hom-MetaSKAT-O, and Hom-MetaSKAT. The results are from Table of Fan et al. (05) and associations that attain a threshold significance of P <. 0 6 are highlighted by red (Liu et al. 04). 5 The results of Basis of Both GVF and βl(t) were based on smoothing both GVF and genetic effect functions βl(t) of model (4), and the results of Basis of beta-smooth Only were based on smoothing βl(t) only approach of model (7), the results of Additive Model (0) were based on the additive effect model (0), and the p-values of Hom-MetaSKAT and Hom-MetaSKAT-O were based of R package MetaSKAT. Abbreviation: GVF = Genetic Variant Function. Traits Gene P -values of the F -approximation Based on Pillai-Bartlett Trace P -values of Basis of Both GVF and βl(t) Basis of beta-smooth Only Additive Hom-Meta- B-spline Basis Fourier Basis B-spline Basis Fourier Basis Model (0) SKAT SKAT-O HDL LPL LDL APOB APOE LDLR PCSK TG APOE LPL CHOL APOB APOE HNFA LDLR
7 Table S.8: Two-trait Meta-analysis of Lipid Traits in European Studies Using Homogeneous F -approximation (Hom-F) Based on Pillai-Bartlett Trace. The associations that attain a threshold significance of P <. 0 6 are highlighted in red [Liu et al. 04]. 5 The results of Basis of Both GVF and βl(t) were based on smoothing both GVF and genetic effect functions βl(t) of model (4), and the results of Basis of beta-smooth Only were based on smoothing βl(t) only approach of model (7). Abbreviations: GVF = Genetic Variant Function. Traits Gene (HDL, TG) P -values of the Homogeneous F -approximation (Hom-F) Basis of Both GVF and βl(t) Basis of beta-smooth Only B-spline Basis Fourier Basis B-spline Basis Fourier Basis APOE LPL (HDL, LDL) APOB APOE LDLR PCSK (HDL, CHOL) APOB APOE LDLR LPL (TG, CHOL) APOB APOE LDLR LPL (LDL, TG) APOB APOE LDLR LPL PCSK (LDL, CHOL) APOB APOE LDLR PCSK
8 Table S.9: Two-trait Meta-analysis of Lipid Traits in European Studies Using Heterogeneous F -approximation (Het-F) Based on Pillai-Bartlett Trace. The associations that attain a threshold significance of P <. 0 6 are highlighted in red [Liu et al. 04]. 5 The results of Basis of Both GVF and βl(t) were based on smoothing both GVF and genetic effect functions βl(t) of model (4), the results of Basis of beta-smooth Only were based on smoothing βl(t) only approach of model (7), and the results of Additive Model (9) were based on the additive effect model (9). Abbreviations: GVF = Genetic Variant Function. Traits Gene (HDL, TG) P -values of the Heterogeneous F -approximation (Het-F) Basis of Both GVF and βl(t) Basis of beta-smooth Only Additive B-spline Basis Fourier Basis B-spline Basis Fourier Basis Model (9) APOB APOE CDKAL JAZF LPL TSPAN (HDL, LDL) APOB APOE CDKAL CDKNB FTO HNFA JAZF LDLR LPL OASL PCSK TSPAN (HDL, CHOL) APOB APOE CDKAL CDKNB FTO HNFA IDE JAZF LDLR LPL MTNRB OASL PCSK TSPAN
9 Table S.0: Two-trait Meta-analysis of Lipid Traits in European Studies Using Heterogeneous F -approximation (Het-F) Based on Pillai-Bartlett Trace. The associations that attain a threshold significance of P <. 0 6 are highlighted in red [Liu et al. 04]. 5 The results of Basis of Both GVF and βl(t) were based on smoothing both GVF and genetic effect functions βl(t) of model (4), the results of Basis of beta-smooth Only were based on smoothing βl(t) only approach of model (7), and the results of Additive Model (9) were based on the additive effect model (9). Abbreviations: GVF = Genetic Variant Function. Traits Gene (TG, CHOL) P -values of the Heterogeneous F -approximation (Het-F) Basis of Both GVF and βl(t) Basis of beta-smooth Only Additive B-spline Basis Fourier Basis B-spline Basis Fourier Basis Model (9) APOB APOE HNFA LPL PCSK TSPAN (LDL, TG) APOB APOE HNFA LPL OASL PCSK TSPAN (LDL, CHOL) APOB APOE CDKAL FTO HNFA KCNQ LDLR OASL PCSK TSPAN
10 Table S.: One-trait Meta-analysis of Lipid Traits in Eight European Cohorts by Heterogeneous Likelihood Ratio Tests (Het- LRT), Het-MetaSKAT-O, and Het-MetaSKAT. The results are from Table of Fan et al. (05) and associations that attain a threshold significance of P <. 0 6 are highlighted by red (Liu et al. 04). 5 The results of Basis of Both GVF and βl(t) were based on smoothing both GVF and genetic effect functions βl(t) of model (4), and the results of Basis of beta-smooth Only were based on smoothing βl(t) only approach of model (7), the results of Additive Model (9) were based on the additive effect model (9), and the p-values of Het-MetaSKAT and Het-MetaSKAT-O were based of R package MetaSKAT. Traits Gene LDL P -values of the Het-LRT P -values of Basis of Both GVF and βl(t) Basis of beta-smooth Only Additive Het-Meta- B-spline Basis Fourier Basis B-spline Basis Fourier Basis Model (9) SKAT SKAT-O APOB APOE CDC CDKAL CDKNB FTO HNFA LDLR OASL PCSK TSPAN TG LPL CHOL APOB APOE CDC CDKAL CDKNB FTO HNFA IDE JAZF KIF LDLR MTNRB OASL PCSK TSPAN
11 Table S.: Study-based Pleiotropy Analysis of Lipid Traits in 5 European Studies in the Regions of APOE and LDLR Genes Using the F -approximation Based on Pillai-Bartlett Trace. The results are from Table of Wang et al. (05) and associations that attain a threshold significance of P <. 0 6 are highlighted in red [Liu et al. 04]. 5 Abbreviations: GVF = Genetic Variant Function, and FPCA = functional principal component analysis. Study Gene Traits Dd-007 APOE P -values of the F -approximation Based on Pillai-Bartlett Trace P -values Basis of both GVF and βl(t) Basis of beta-smooth Only Additive of FPCA B-sp Basis Fourier Basis B-sp Basis Fourier Basis Model SKAT-O LDL CHOL LDL, CHOL X FUSION LDL Stage APOE CHOL LDL,CHOL X Norway APOE DIAGEN APOE METSIM APOE LDLR LDL TG CHOL LDL,TG X LDL,CHOL X TG,CHOL X LDL,TG,CHOL X LDL TG CHOL LDL,TG X LDL,CHOL X TG,CHOL X LDL,TG,CHOL X LDL TG CHOL LDL,TG X LDL,CHOL X LDL,TG,CHOL X LDL CHOL LDL,CHOL X
Genotype Imputation. Biostatistics 666
Genotype Imputation Biostatistics 666 Previously Hidden Markov Models for Relative Pairs Linkage analysis using affected sibling pairs Estimation of pairwise relationships Identity-by-Descent Relatives
More informationExtending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy
Received: 20 October 2016 Revised: 15 August 2017 Accepted: 23 August 2017 DOI: 10.1002/sim.7492 RESEARCH ARTICLE Extending the MR-Egger method for multivariable Mendelian randomization to correct for
More informationGenotype Imputation. Class Discussion for January 19, 2016
Genotype Imputation Class Discussion for January 19, 2016 Intuition Patterns of genetic variation in one individual guide our interpretation of the genomes of other individuals Imputation uses previously
More informationPower and sample size calculations for designing rare variant sequencing association studies.
Power and sample size calculations for designing rare variant sequencing association studies. Seunggeun Lee 1, Michael C. Wu 2, Tianxi Cai 1, Yun Li 2,3, Michael Boehnke 4 and Xihong Lin 1 1 Department
More informationA Robust Test for Two-Stage Design in Genome-Wide Association Studies
Biometrics Supplementary Materials A Robust Test for Two-Stage Design in Genome-Wide Association Studies Minjung Kwak, Jungnam Joo and Gang Zheng Appendix A: Calculations of the thresholds D 1 and D The
More informationLecture 9: Kernel (Variance Component) Tests and Omnibus Tests for Rare Variants. Summer Institute in Statistical Genetics 2017
Lecture 9: Kernel (Variance Component) Tests and Omnibus Tests for Rare Variants Timothy Thornton and Michael Wu Summer Institute in Statistical Genetics 2017 1 / 46 Lecture Overview 1. Variance Component
More informationDepartment of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University, Xi'an, China;
Title: Evaluation of genetic susceptibility of common variants in CACNA1D with schizophrenia in Han Chinese Author names and affiliations: Fanglin Guan a,e, Lu Li b, Chuchu Qiao b, Gang Chen b, Tinglin
More informationGENETIC VARIANT SELECTION: LEARNING ACROSS TRAITS AND SITES. Laurel Stell Chiara Sabatti
GENETIC VARIANT SELECTION: LEARNING ACROSS TRAITS AND SITES By Laurel Stell Chiara Sabatti Technical Report 269 April 2015 Division of Biostatistics STANFORD UNIVERSITY Stanford, California GENETIC VARIANT
More informationPackage KMgene. November 22, 2017
Type Package Package KMgene November 22, 2017 Title Gene-Based Association Analysis for Complex Traits Version 1.2 Author Qi Yan Maintainer Qi Yan Gene based association test between a
More informationNIH Public Access Author Manuscript Genet Epidemiol. Author manuscript; available in PMC 2014 December 01.
NIH Public Access Author Manuscript Published in final edited form as: Genet Epidemiol. 2013 December ; 37(8): 759 767. doi:10.1002/gepi.21759. A General Framework for Association Tests With Multivariate
More informationHeterozygous BMN lines
Optical density at 80 hours 0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2 a YPD b YPD + 1µM nystatin c YPD + 2µM nystatin d YPD + 4µM nystatin 1 3 5 6 9 13 16 20 21 22 23 25 28 29 30
More informationBootstrap Procedures for Testing Homogeneity Hypotheses
Journal of Statistical Theory and Applications Volume 11, Number 2, 2012, pp. 183-195 ISSN 1538-7887 Bootstrap Procedures for Testing Homogeneity Hypotheses Bimal Sinha 1, Arvind Shah 2, Dihua Xu 1, Jianxin
More informationHow to analyze many contingency tables simultaneously?
How to analyze many contingency tables simultaneously? Thorsten Dickhaus Humboldt-Universität zu Berlin Beuth Hochschule für Technik Berlin, 31.10.2012 Outline Motivation: Genetic association studies Statistical
More informationStatistical Methods in Mapping Complex Diseases
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Summer 8-12-2011 Statistical Methods in Mapping Complex Diseases Jing He University of Pennsylvania, jinghe@mail.med.upenn.edu
More informationTest for interactions between a genetic marker set and environment in generalized linear models Supplementary Materials
Biostatistics (2013), pp. 1 31 doi:10.1093/biostatistics/kxt006 Test for interactions between a genetic marker set and environment in generalized linear models Supplementary Materials XINYI LIN, SEUNGGUEN
More informationFigure S2. The distribution of the sizes (in bp) of syntenic regions of humans and chimpanzees on human chromosome 21.
Frequency 0 1000 2000 3000 4000 5000 0 2 4 6 8 10 Distance Figure S1. The distribution of human-chimpanzee sequence divergence for syntenic regions of humans and chimpanzees on human chromosome 21. Distance
More informationCausal inference in biomedical sciences: causal models involving genotypes. Mendelian randomization genes as Instrumental Variables
Causal inference in biomedical sciences: causal models involving genotypes Causal models for observational data Instrumental variables estimation and Mendelian randomization Krista Fischer Estonian Genome
More informationEfficient Bayesian mixed model analysis increases association power in large cohorts
Linear regression Existing mixed model methods New method: BOLT-LMM Time O(MM) O(MN 2 ) O MN 1.5 Corrects for confounding? Power Efficient Bayesian mixed model analysis increases association power in large
More informationTheoretical and computational aspects of association tests: application in case-control genome-wide association studies.
Theoretical and computational aspects of association tests: application in case-control genome-wide association studies Mathieu Emily November 18, 2014 Caen mathieu.emily@agrocampus-ouest.fr - Agrocampus
More informationSUPPLEMENTARY INFORMATION
doi:10.1038/nature25973 Power Simulations We performed extensive power simulations to demonstrate that the analyses carried out in our study are well powered. Our simulations indicate very high power for
More informationcontents: BreedeR: a R-package implementing statistical models specifically suited for forest genetic resources analysts
contents: definitions components of phenotypic correlations causal components of genetic correlations pleiotropy versus LD scenarios of correlation computing genetic correlations why genetic correlations
More informationEfficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data SUPPLEMENTARY MATERIAL A FACTORED DELETION FOR MAR Table 5: alarm network with MCAR data We now give a more detailed derivation
More informationAssociation Testing with Quantitative Traits: Common and Rare Variants. Summer Institute in Statistical Genetics 2014 Module 10 Lecture 5
Association Testing with Quantitative Traits: Common and Rare Variants Timothy Thornton and Katie Kerr Summer Institute in Statistical Genetics 2014 Module 10 Lecture 5 1 / 41 Introduction to Quantitative
More informationSUPPLEMENTARY MATERIALS
SUPPLEMENTARY MATERIALS Enhanced Recognition of Transmembrane Protein Domains with Prediction-based Structural Profiles Baoqiang Cao, Aleksey Porollo, Rafal Adamczak, Mark Jarrell and Jaroslaw Meller Contact:
More informationGroup exponential penalties for bi-level variable selection
for bi-level variable selection Department of Biostatistics Department of Statistics University of Kentucky July 31, 2011 Introduction In regression, variables can often be thought of as grouped: Indicator
More informationBinomial Mixture Model-based Association Tests under Genetic Heterogeneity
Binomial Mixture Model-based Association Tests under Genetic Heterogeneity Hui Zhou, Wei Pan Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 April 30,
More informationStatistical Methods for Modeling Heterogeneous Effects in Genetic Association Studies
Statistical Methods for Modeling Heterogeneous Effects in Genetic Association Studies by Jingchunzi Shi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy
More informationSupplementary Information
Supplementary Information 1 Supplementary Figures (a) Statistical power (p = 2.6 10 8 ) (b) Statistical power (p = 4.0 10 6 ) Supplementary Figure 1: Statistical power comparison between GEMMA (red) and
More informationMissing Data Issues in the Studies of Neurodegenerative Disorders: the Methodology
Missing Data Issues in the Studies of Neurodegenerative Disorders: the Methodology Sheng Luo, PhD Associate Professor Department of Biostatistics & Bioinformatics Duke University Medical Center sheng.luo@duke.edu
More informationExtending Process Logs with Events from Supplementary Sources. Felix Mannhardt Massimiliano de Leoni, Hajo A.
Extending Process Logs with Events from Supplementary Sources Felix Mannhardt (@fmannhardt), Massimiliano de Leoni, Hajo A. Reijers Heterogeneous Information Systems Systems Databases Join Event Logs?
More informationSupplementary Information for Discovery and characterization of indel and point mutations
Supplementary Information for Discovery and characterization of indel and point mutations using DeNovoGear Avinash Ramu 1 Michiel J. Noordam 1 Rachel S. Schwartz 2 Arthur Wuster 3 Matthew E. Hurles 3 Reed
More informationLinear Regression (1/1/17)
STA613/CBB540: Statistical methods in computational biology Linear Regression (1/1/17) Lecturer: Barbara Engelhardt Scribe: Ethan Hada 1. Linear regression 1.1. Linear regression basics. Linear regression
More informationDNA polymorphisms such as SNP and familial effects (additive genetic, common environment) to
1 1 1 1 1 1 1 1 0 SUPPLEMENTARY MATERIALS, B. BIVARIATE PEDIGREE-BASED ASSOCIATION ANALYSIS Introduction We propose here a statistical method of bivariate genetic analysis, designed to evaluate contribution
More informationA Comparison of Robust Methods for Mendelian Randomization Using Multiple Genetic Variants
8 A Comparison of Robust Methods for Mendelian Randomization Using Multiple Genetic Variants Yanchun Bao ISER, University of Essex Paul Clarke ISER, University of Essex Melissa C Smart ISER, University
More informationThe Generalized Higher Criticism for Testing SNP-sets in Genetic Association Studies
The Generalized Higher Criticism for Testing SNP-sets in Genetic Association Studies Ian Barnett, Rajarshi Mukherjee & Xihong Lin Harvard University ibarnett@hsph.harvard.edu August 5, 2014 Ian Barnett
More informationPackage MACAU2. R topics documented: April 8, Type Package. Title MACAU 2.0: Efficient Mixed Model Analysis of Count Data. Version 1.
Package MACAU2 April 8, 2017 Type Package Title MACAU 2.0: Efficient Mixed Model Analysis of Count Data Version 1.10 Date 2017-03-31 Author Shiquan Sun, Jiaqiang Zhu, Xiang Zhou Maintainer Shiquan Sun
More informationp(d g A,g B )p(g B ), g B
Supplementary Note Marginal effects for two-locus models Here we derive the marginal effect size of the three models given in Figure 1 of the main text. For each model we assume the two loci (A and B)
More informationAn Introduction to Multivariate Statistical Analysis
An Introduction to Multivariate Statistical Analysis Third Edition T. W. ANDERSON Stanford University Department of Statistics Stanford, CA WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Contents
More informationPackage NarrowPeaks. September 24, 2012
Package NarrowPeaks September 24, 2012 Version 1.0.1 Date 2012-03-15 Type Package Title Functional Principal Component Analysis to Narrow Down Transcription Factor Binding Site Candidates Author Pedro
More informationMODEL-FREE LINKAGE AND ASSOCIATION MAPPING OF COMPLEX TRAITS USING QUANTITATIVE ENDOPHENOTYPES
MODEL-FREE LINKAGE AND ASSOCIATION MAPPING OF COMPLEX TRAITS USING QUANTITATIVE ENDOPHENOTYPES Saurabh Ghosh Human Genetics Unit Indian Statistical Institute, Kolkata Most common diseases are caused by
More informationThe Generalized Higher Criticism for Testing SNP-sets in Genetic Association Studies
The Generalized Higher Criticism for Testing SNP-sets in Genetic Association Studies Ian Barnett, Rajarshi Mukherjee & Xihong Lin Harvard University ibarnett@hsph.harvard.edu June 24, 2014 Ian Barnett
More informationGENOME-WIDE association studies (GWAS) have allowed
INVESTIGATION Genetic Variant Selection: Learning Across Traits and Sites Laurel Stell*,1 and Chiara Sabatti*, *Department of Health Research and Policy and Department of Statistics, Stanford University,
More information. Also, in this case, p i = N1 ) T, (2) where. I γ C N(N 2 2 F + N1 2 Q)
Supplementary information S7 Testing for association at imputed SPs puted SPs Score tests A Score Test needs calculations of the observed data score and information matrix only under the null hypothesis,
More informationQuantitative Genomics and Genetics BTRY 4830/6830; PBSB
Quantitative Genomics and Genetics BTRY 4830/6830; PBSB.5201.01 Lecture 18: Introduction to covariates, the QQ plot, and population structure II + minimal GWAS steps Jason Mezey jgm45@cornell.edu April
More informationTime Series: Theory and Methods
Peter J. Brockwell Richard A. Davis Time Series: Theory and Methods Second Edition With 124 Illustrations Springer Contents Preface to the Second Edition Preface to the First Edition vn ix CHAPTER 1 Stationary
More informationBayesian Inference of Interactions and Associations
Bayesian Inference of Interactions and Associations Jun Liu Department of Statistics Harvard University http://www.fas.harvard.edu/~junliu Based on collaborations with Yu Zhang, Jing Zhang, Yuan Yuan,
More informationA General Framework for Variable Selection in Linear Mixed Models with Applications to Genetic Studies with Structured Populations
A General Framework for Variable Selection in Linear Mixed Models with Applications to Genetic Studies with Structured Populations Joint work with Karim Oualkacha (UQÀM), Yi Yang (McGill), Celia Greenwood
More informationFinite Population Sampling and Inference
Finite Population Sampling and Inference A Prediction Approach RICHARD VALLIANT ALAN H. DORFMAN RICHARD M. ROYALL A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim Brisbane
More informationTutorial Session 2. MCMC for the analysis of genetic data on pedigrees:
MCMC for the analysis of genetic data on pedigrees: Tutorial Session 2 Elizabeth Thompson University of Washington Genetic mapping and linkage lod scores Monte Carlo likelihood and likelihood ratio estimation
More informationPackage LBLGXE. R topics documented: July 20, Type Package
Type Package Package LBLGXE July 20, 2015 Title Bayesian Lasso for detecting Rare (or Common) Haplotype Association and their interactions with Environmental Covariates Version 1.2 Date 2015-07-09 Author
More informationRemote sensing of biodiversity: measuring ecological complexity from space
Remote sensing of biodiversity: measuring ecological complexity from space Duccio Rocchini Fondazione Edmund Mach Research and Innovation Centre Department of Biodiversity and Molecular Ecology San Michele
More informationReview We have covered so far: Single variant association analysis and effect size estimation GxE interaction and higher order >2 interaction Measurement error in dietary variables (nutritional epidemiology)
More informationOnline Supplementary Material. MetaLP: A Nonparametric Distributed Learning Framework for Small and Big Data
Online Supplementary Material MetaLP: A Nonparametric Distributed Learning Framework for Small and Big Data PI : Subhadeep Mukhopadhyay Department of Statistics, Temple University Philadelphia, Pennsylvania,
More informationGenetic association methods for multiple types of traits in family samples
Boston University OpenBU Theses & Dissertations http://open.bu.edu Boston University Theses & Dissertations 2015 Genetic association methods for multiple types of traits in family samples Wang, Shuai https://hdl.handle.net/2144/16359
More informationRobust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization
Robust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization arxiv:1606.03729v1 [stat.me] 12 Jun 2016 Stephen Burgess 1, Jack Bowden 2 Frank Dudbridge
More informationSet-based Tests for Genetic Association and Gene-Environment Interaction
Set-based Tests for Genetic Association and Gene-Environment Interaction by Zihuai He A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biostatistics)
More informationPackage ESPRESSO. August 29, 2013
Package ESPRESSO August 29, 2013 Type Package Title Power Analysis and Sample Size Calculation Version 1.1 Date 2011-04-01 Author Amadou Gaye, Paul Burton Maintainer Amadou Gaye The package
More informationExperimental Design and Data Analysis for Biologists
Experimental Design and Data Analysis for Biologists Gerry P. Quinn Monash University Michael J. Keough University of Melbourne CAMBRIDGE UNIVERSITY PRESS Contents Preface page xv I I Introduction 1 1.1
More information27: Case study with popular GM III. 1 Introduction: Gene association mapping for complex diseases 1
10-708: Probabilistic Graphical Models, Spring 2015 27: Case study with popular GM III Lecturer: Eric P. Xing Scribes: Hyun Ah Song & Elizabeth Silver 1 Introduction: Gene association mapping for complex
More informationLecture 9 Multi-Trait Models, Binary and Count Traits
Lecture 9 Multi-Trait Models, Binary and Count Traits Guilherme J. M. Rosa University of Wisconsin-Madison Mixed Models in Quantitative Genetics SISG, Seattle 18 0 September 018 OUTLINE Multiple-trait
More informationOn the limiting distribution of the likelihood ratio test in nucleotide mapping of complex disease
On the limiting distribution of the likelihood ratio test in nucleotide mapping of complex disease Yuehua Cui 1 and Dong-Yun Kim 2 1 Department of Statistics and Probability, Michigan State University,
More informationFlexible phenotype simulation with PhenotypeSimulator Hannah Meyer
Flexible phenotype simulation with PhenotypeSimulator Hannah Meyer 2018-03-01 Contents Introduction 1 Work-flow 2 Examples 2 Example 1: Creating a phenotype composed of population structure and observational
More informationA novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer
A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer Binghui Liu, Chong Wu, Xiaotong Shen, Wei Pan University of Minnesota, Minneapolis, MN 55455 Nov 2017 Introduction
More informationHANDBOOK OF APPLICABLE MATHEMATICS
HANDBOOK OF APPLICABLE MATHEMATICS Chief Editor: Walter Ledermann Volume VI: Statistics PART A Edited by Emlyn Lloyd University of Lancaster A Wiley-Interscience Publication JOHN WILEY & SONS Chichester
More informationLecture 2: Genetic Association Testing with Quantitative Traits. Summer Institute in Statistical Genetics 2017
Lecture 2: Genetic Association Testing with Quantitative Traits Instructors: Timothy Thornton and Michael Wu Summer Institute in Statistical Genetics 2017 1 / 29 Introduction to Quantitative Trait Mapping
More informationMultilevel Statistical Models: 3 rd edition, 2003 Contents
Multilevel Statistical Models: 3 rd edition, 2003 Contents Preface Acknowledgements Notation Two and three level models. A general classification notation and diagram Glossary Chapter 1 An introduction
More informationSupplementary Information
Supplementary Information 1 List of Figures 1 Models of circular chromosomes. 2 Distribution of distances between core genes in Escherichia coli K12, arc based model. 3 Distribution of distances between
More informationAssociation studies and regression
Association studies and regression CM226: Machine Learning for Bioinformatics. Fall 2016 Sriram Sankararaman Acknowledgments: Fei Sha, Ameet Talwalkar Association studies and regression 1 / 104 Administration
More informationSupplementary File 3: Tutorial for ASReml-R. Tutorial 1 (ASReml-R) - Estimating the heritability of birth weight
Supplementary File 3: Tutorial for ASReml-R Tutorial 1 (ASReml-R) - Estimating the heritability of birth weight This tutorial will demonstrate how to run a univariate animal model using the software ASReml
More informationConditions under which genome-wide association studies will be positively misleading
Genetics: Published Articles Ahead of Print, published on September 2, 2010 as 10.1534/genetics.110.121665 Conditions under which genome-wide association studies will be positively misleading Alexander
More informationModule 17: Bayesian Statistics for Genetics Lecture 4: Linear regression
1/37 The linear regression model Module 17: Bayesian Statistics for Genetics Lecture 4: Linear regression Ken Rice Department of Biostatistics University of Washington 2/37 The linear regression model
More informationDEGseq: an R package for identifying differentially expressed genes from RNA-seq data
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data Likun Wang Zhixing Feng i Wang iaowo Wang * and uegong Zhang * MOE Key Laboratory of Bioinformatics and Bioinformatics
More informationClassification Ensemble That Maximizes the Area Under Receiver Operating Characteristic Curve (AUC)
Classification Ensemble That Maximizes the Area Under Receiver Operating Characteristic Curve (AUC) Eunsik Park 1 and Y-c Ivan Chang 2 1 Chonnam National University, Gwangju, Korea 2 Academia Sinica, Taipei,
More informationMendelian randomization: From genetic association to epidemiological causation
Mendelian randomization: From genetic association to epidemiological causation Qingyuan Zhao Department of Statistics, The Wharton School, University of Pennsylvania April 24, 2018 2 C (Confounder) 1 Z
More informationReliability of Acceptance Criteria in Nonlinear Response History Analysis of Tall Buildings
Reliability of Acceptance Criteria in Nonlinear Response History Analysis of Tall Buildings M.M. Talaat, PhD, PE Senior Staff - Simpson Gumpertz & Heger Inc Adjunct Assistant Professor - Cairo University
More informationClassical Selection, Balancing Selection, and Neutral Mutations
Classical Selection, Balancing Selection, and Neutral Mutations Classical Selection Perspective of the Fate of Mutations All mutations are EITHER beneficial or deleterious o Beneficial mutations are selected
More informationPackage pfa. July 4, 2016
Type Package Package pfa July 4, 2016 Title Estimates False Discovery Proportion Under Arbitrary Covariance Dependence Version 1.1 Date 2016-06-24 Author Jianqing Fan, Tracy Ke, Sydney Li and Lucy Xia
More informationUnivariate Linkage in Mx. Boulder, TC 18, March 2005 Posthuma, Maes, Neale
Univariate Linkage in Mx Boulder, TC 18, March 2005 Posthuma, Maes, Neale VC analysis of Linkage Incorporating IBD Coefficients Covariance might differ according to sharing at a particular locus. Sharing
More informationLecture 11: Multiple trait models for QTL analysis
Lecture 11: Multiple trait models for QTL analysis Julius van der Werf Multiple trait mapping of QTL...99 Increased power of QTL detection...99 Testing for linked QTL vs pleiotropic QTL...100 Multiple
More informationBayesian Variable Selection Regression Of Multivariate Responses For Group Data
Bayesian Variable Selection Regression Of Multivariate Responses For Group Data B. Liquet 1,2 and K. Mengersen 2 and A. N. Pettitt 2 and M. Sutton 2 1 LMAP, Université de Pau et des Pays de L Adour 2 ACEMS,
More informationSupplementary Materials for Molecular QTL Discovery Incorporating Genomic Annotations using Bayesian False Discovery Rate Control
Supplementary Materials for Molecular QTL Discovery Incorporating Genomic Annotations using Bayesian False Discovery Rate Control Xiaoquan Wen Department of Biostatistics, University of Michigan A Model
More informationBIAS OF MAXIMUM-LIKELIHOOD ESTIMATES IN LOGISTIC AND COX REGRESSION MODELS: A COMPARATIVE SIMULATION STUDY
BIAS OF MAXIMUM-LIKELIHOOD ESTIMATES IN LOGISTIC AND COX REGRESSION MODELS: A COMPARATIVE SIMULATION STUDY Ingo Langner 1, Ralf Bender 2, Rebecca Lenz-Tönjes 1, Helmut Küchenhoff 2, Maria Blettner 2 1
More informationSignificant Pattern Mining
Department Biosystems Significant Pattern Mining Karsten Borgwardt ETH Zürich Uni Basel, April 21, 2016 Biomarker Discovery Department Biosystems Karsten Borgwardt Seminar Basel April 21, 2016 2 / 41 Department
More informationSUPPLEMENTARY SIMULATIONS & FIGURES
Supplementary Material: Supplementary Material for Mixed Effects Models for Resampled Network Statistics Improve Statistical Power to Find Differences in Multi-Subject Functional Connectivity Manjari Narayan,
More informationNature Methods: doi: /nmeth.3439
Supplementary Figure 1 Computational run time of alternative implementations of mtset as a function of the number of traits. Shown is the extrapolated CPU time (h to test associations on chromosome 20,
More informationtigating global association between DNA copy number profiles from multiple myeloma (MM) patients and two relevant biomarkers. Chapter 4 deals with
ABSTRACT ZHAO, JING. Kernel Machine Regression in Presence of Multivariate Response with Application to Genetic Data. (Under the direction of Arnab Maity and Jung-Ying Tzeng.) Genomic association studies
More informationGenetics and Natural Selection
Genetics and Natural Selection Darwin did not have an understanding of the mechanisms of inheritance and thus did not understand how natural selection would alter the patterns of inheritance in a population.
More informationA novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction
A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction Sangseob Leem, Hye-Young Jung, Sungyoung Lee and Taesung Park Bioinformatics and Biostatistics lab
More informationTHE STATISTICAL ANALYSIS OF GENETIC SEQUENCING AND RARE VARIANT ASSOCIATION STUDIES. Eugene Urrutia
THE STATISTICAL ANALYSIS OF GENETIC SEQUENCING AND RARE VARIANT ASSOCIATION STUDIES Eugene Urrutia A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial
More informationPREDICTION OF SOIL ORGANIC CARBON CONTENT BY SPECTROSCOPY AT EUROPEAN SCALE USING A LOCAL PARTIAL LEAST SQUARE REGRESSION APPROACH
Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 PREDICTION OF SOIL ORGANIC CARBON CONTENT BY SPECTROSCOPY AT EUROPEAN SCALE
More informationEffects of Causal Networks on the Structure and Stability of Resource Allocation Trait Correlations
Effects of Causal Networks on the Structure and Stability of Resource Allocation Trait Correlations By: Robert P. Gove, William Chen, Nicholas B. Zweber, Rebecca Erwin, Jan Rychtář, David L. Remington
More information25 : Graphical induced structured input/output models
10-708: Probabilistic Graphical Models 10-708, Spring 2013 25 : Graphical induced structured input/output models Lecturer: Eric P. Xing Scribes: Meghana Kshirsagar (mkshirsa), Yiwen Chen (yiwenche) 1 Graph
More information6.867 Machine Learning
6.867 Machine Learning Problem Set 2 Due date: Wednesday October 6 Please address all questions and comments about this problem set to 6867-staff@csail.mit.edu. You will need to use MATLAB for some of
More informationAbstract. Maximizing the Power of Principal Components Analysis of Correlated Phenotypes in Genome-wide Association Studies.
Maximizing the Power of Principal Components Analysis of Correlated Phenotypes in Genome-wide Association Studies. Hugues Aschard 1, Bjarni J. Vilhjálmsson 1, Nicolas Greliche 2, Pierre-Emmanuel Morange
More informationBig & Quic: Sparse Inverse Covariance Estimation for a Million Variables
for a Million Variables Cho-Jui Hsieh The University of Texas at Austin NIPS Lake Tahoe, Nevada Dec 8, 2013 Joint work with M. Sustik, I. Dhillon, P. Ravikumar and R. Poldrack FMRI Brain Analysis Goal:
More informationPhylogenomics Resolves The Timing And Pattern Of Insect Evolution. - Supplementary File Archives -
Phylogenomics Resolves The Timing And Pattern Of Insect Evolution. - Supplementary File Archives - This README was written in June 2014 For any questions regarding the nature of our data, please contact
More informationEFFICIENT COMPUTATION WITH A LINEAR MIXED MODEL ON LARGE-SCALE DATA SETS WITH APPLICATIONS TO GENETIC STUDIES
Submitted to the Annals of Applied Statistics EFFICIENT COMPUTATION WITH A LINEAR MIXED MODEL ON LARGE-SCALE DATA SETS WITH APPLICATIONS TO GENETIC STUDIES By Matti Pirinen, Peter Donnelly and Chris C.A.
More informationz = β βσβ Statistical Analysis of MV Data Example : µ=0 (Σ known) consider Y = β X~ N 1 (β µ, β Σβ) test statistic for H 0β is
Example X~N p (µ,σ); H 0 : µ=0 (Σ known) consider Y = β X~ N 1 (β µ, β Σβ) H 0β : β µ = 0 test statistic for H 0β is y z = β βσβ /n And reject H 0β if z β > c [suitable critical value] 301 Reject H 0 if
More informationResearch Projects. Hanxiang Peng. March 4, Department of Mathematical Sciences Indiana University-Purdue University at Indianapolis
Hanxiang Department of Mathematical Sciences Indiana University-Purdue University at Indianapolis March 4, 2009 Outline Project I: Free Knot Spline Cox Model Project I: Free Knot Spline Cox Model Consider
More informationPower and Sample Size + Principles of Simulation. Benjamin Neale March 4 th, 2010 International Twin Workshop, Boulder, CO
Power and Sample Size + Principles of Simulation Benjamin Neale March 4 th, 2010 International Twin Workshop, Boulder, CO What is power? What affects power? How do we calculate power? What is simulation?
More information