Univariate Analysis in Mx Boulder, 2004. Group Structure Title Type: Data/ Calculation/ Constraint Reading Data Matrices Declaration Assigning Specifications/

Slides:



Advertisements
Similar presentations
Multivariate Twin Analysis
Advertisements

Bivariate analysis HGEN619 class 2007.
Using MX for SEM analysis. Using Lisrel Analysis of Reader Reliability in Essay Scoring Votaw's Data Tau-Equivalent Model DA NI=4 NO=126 LA ORIGPRT1 WRITCOPY.
The use of Cholesky decomposition in multivariate models of sex-limited genetic and environmental effects Michael C. Neale Virginia Institute for Psychiatric.
Factor analysis Caroline van Baal March 3 rd 2004, Boulder.
(Re)introduction to OpenMx Sarah Medland. Starting at the beginning  Opening R Gui – double click Unix/Terminal – type R  Closing R Gui – click on the.
Path Analysis Danielle Dick Boulder Path Analysis Allows us to represent linear models for the relationships between variables in diagrammatic form.
(Re)introduction to Mx Sarah Medland. KiwiChinese Gooseberry.
Multivariate Analysis Nick Martin, Hermine Maes TC21 March 2008 HGEN619 10/20/03.
Multivariate Genetic Analysis: Introduction(II) Frühling Rijsdijk & Shaun Purcell Wednesday March 6, 2002.
Summarizing Data Nick Martin, Hermine Maes TC21 March 2008.
Developmental models. Multivariate analysis choleski models factor models –y =  f + u genetic factor models –P j = h G j + c C j + e E j –common pathway.
ACDE model and estimability Why can’t we estimate (co)variances due to A, C, D and E simultaneously in a standard twin design?
Heterogeneity Hermine Maes TC19 March Files to Copy to your Computer Faculty/hmaes/tc19/maes/heterogeneity  ozbmi.rec  ozbmi.dat  ozbmiysat(4)(5).mx.
(Re)introduction to Mx. Starting at the beginning Data preparation Mx expects 1 line per case/family Almost limitless number of families and variables.
Introduction to Linkage
Continuous heterogeneity Shaun Purcell Boulder Twin Workshop March 2004.
Multivariate Analysis Hermine Maes TC19 March 2006 HGEN619 10/20/03.
Genetic Dominance in Extended Pedigrees: Boulder, March 2008 Irene Rebollo Biological Psychology Department, Vrije Universiteit Netherlands Twin Register.
Univariate Analysis Hermine Maes TC19 March 2006.
Missing Data Michael C. Neale International Workshop on Methodology for Genetic Studies of Twins and Families Boulder CO 2006 Virginia Institute for Psychiatric.
Path Analysis Frühling Rijsdijk SGDP Centre Institute of Psychiatry King’s College London, UK.
Mx Practical TC18, 2005 Dorret Boomsma, Nick Martin, Hermine H. Maes.
Introduction to Multivariate Genetic Analysis Kate Morley and Frühling Rijsdijk 21st Twin and Family Methodology Workshop, March 2008.
Path Analysis Frühling Rijsdijk. Biometrical Genetic Theory Aims of session:  Derivation of Predicted Var/Cov matrices Using: (1)Path Tracing Rules (2)Covariance.
Raw data analysis S. Purcell & M. C. Neale Twin Workshop, IBG Colorado, March 2002.
Multivariate Threshold Models Specification in Mx.
Social interaction March 7 th, 2002 Boulder, Colorado John Hewitt.
Intro to Mx Scripts. Groups Data Calculation Constraint.
Power and Sample Size Adapted from: Boulder 2004 Benjamin Neale Shaun Purcell I HAVE THE POWER!!!
Path Analysis HGEN619 class Method of Path Analysis allows us to represent linear models for the relationship between variables in diagrammatic.
MathematicalMarketing Slide 2.1 Descriptive Statistics Chapter 2: Descriptive Statistics We will be comparing the univariate and matrix formulae for common.
Karri Silventoinen University of Helsinki Osaka University.
Institute of Psychiatry King’s College London, UK
Cholesky decomposition May 27th 2015 Helsinki, Finland E. Vuoksimaa.
Heterogeneity Hermine Maes TC21 March Files to Copy to your Computer Faculty/hmaes/tc19/maes/heterogeneity  ozbmi.rec  ozbmi.dat  ozbmiysat(4)(5).mx.
Univariate modeling Sarah Medland. Starting at the beginning… Data preparation – The algebra style used in Mx expects 1 line per case/family – (Almost)
Power and Sample Size Boulder 2004 Benjamin Neale Shaun Purcell.
Univariate Analysis Hermine Maes TC21 March 2008.
Mx modeling of methylation data: twin correlations [means, SD, correlation] ACE / ADE latent factor model regression [sex and age] genetic association.
Means, Thresholds and Moderation Sarah Medland – Boulder 2008 Corrected Version Thanks to Hongyan Du for pointing out the error on the regression examples.
Mx Practical TC20, 2007 Hermine H. Maes Nick Martin, Dorret Boomsma.
Continuous heterogeneity Danielle Dick & Sarah Medland Boulder Twin Workshop March 2006.
Frühling Rijsdijk & Kate Morley
Categorical Data Frühling Rijsdijk 1 & Caroline van Baal 2 1 IoP, London 2 Vrije Universiteit, A’dam Twin Workshop, Boulder Tuesday March 2, 2004.
Welcome  Log on using the username and password you received at registration  Copy the folder: F:/sarah/mon-morning To your H drive.
Linkage in Mx & Merlin Meike Bartels Kate Morley Hermine Maes Based on Posthuma et al., Boulder & Egmond.
QTL Mapping Using Mx Michael C Neale Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University.
March 7, 2012M. de Moor, Twin Workshop Boulder1 Copy files Go to Faculty\marleen\Boulder2012\Multivariate Copy all files to your own directory Go to Faculty\kees\Boulder2012\Multivariate.
Multivariate Genetic Analysis (Introduction) Frühling Rijsdijk Wednesday March 8, 2006.
Categorical Data HGEN
Intro to Mx HGEN619 class 2005.
Multivariate Analysis
Bivariate analysis HGEN619 class 2006.
Introduction to Multivariate Genetic Analysis
Linkage and Association in Mx
BINARY and CATEGORICAL TRAITS
Heterogeneity HGEN619 class 2007.
Longitudinal Analysis
Univariate Analysis HGEN619 class 2006.
MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience
Path Analysis Danielle Dick Boulder 2008
Univariate modeling Sarah Medland.
Liability Threshold Models
Heterogeneity Danielle Dick, Hermine Maes,
(Re)introduction to Mx Sarah Medland
Sarah Medland faculty/sarah/2018/Tuesday
Multivariate Genetic Analysis
Multivariate Genetic Analysis: Introduction
Rater Bias & Sibling Interaction Meike Bartels Boulder 2004
Presentation transcript:

Univariate Analysis in Mx Boulder, 2004

Group Structure Title Type: Data/ Calculation/ Constraint Reading Data Matrices Declaration Assigning Specifications/ Values Matrix Algebra and/or Means/ Covariances Options End

Additional Commands ! Comments #NGroups #define e.g. #define nvar 1 #define #include filename

Reading Data Data NInputvars= [NObs= ] Rectangular File= Missing= Labels Select if Select if zyg =1; Select Summarized in filename.dat

Matrices Declaration Begin Matrices; … End Matrices; Matrix Types: Mx manual p. 56 Begin Matrices = Group

Matrix Algebra Begin Algebra; = ; … End Algebra; Matrix Operations: Mx Manual p. 59 Matrix Functions: Mx Manual p. 64

Means/Covariances Means ; e.g. Means M; dimensions of expected matrix must equal dimensions of observed means Covariances ; dimensions of expected covariance matrix must equal the square of the number of variables

Mx Script I #NGroups 2 #define nvar 1 #define nsib 2 G1: male MZ twin pairs Data NInput_vars=5 Missing=-1.00 Rectangular File=Agg10.rec Labels ZYG RB10A AGG10A RB10B AGG10B Select if zyg =1 ; ! select MZM twins Select AGG10A AGG10B ; May be put in agg10.dat and included with #Include filename

Mx Script II Begin Matrices; X Symm nsib nsib Free! covariances I Iden nsib nsib M Full nvar nsib Free! means End Matrices; Start 2 X 1 1 X 2 2! starting values for variances Start 0.5 M 1 1 M 1 2! starting values for means Begin Algebra; O= \sqrt(I.X)~&X;! MZM correlation End Algebra; Means M;! model for MZM means Covariances X;! model for MZM (co)variances ! O 2 1 Option RSiduals End

Mx Script III Begin Matrices; Y Symm nsib nsib Free! covariances I Iden nsib nsib N Full nvar nsib Free! means End Matrices; Start 2 Y 1 1 X 2 2! starting values for variances Start 0.5 N 1 1 N 1 2! starting values for means Begin Algebra; P= \sqrt(I.Y)~&Y;! DZM correlation End Algebra; Means N;! model for DZM means Covariances Y;! model for DZM (co)variances ! P 2 1 Option RSiduals End

Mx Script IV ! equate means Equate M M N N End ! equate means and variances Equate X X Y Y End

Path Diagram for MZ and DZ twins P1 A1C1E1 P2 A2C2E2D1D aceace 1.00 / d d 1.00 / 0.25

MZ Twins Observed Covariance Variance Twin 1 Covariance Variance Twin 2 Expected Covariance a 2+ c 2+ e 2+ d 2 a 2+ c 2+ d 2 a 2+ c 2+ e 2+ d 2

DZ Twins Observed Covariance Variance Twin 1 Covariance Variance Twin 2 Expected Covariance a 2+ c 2+ e 2+ d 2.5a 2+ c 2+.25d 2 a 2+ c 2+ e 2+ d 2

Univariate Mx Script I #NGroups 3 #define nvar 1 ! define nvar as number of variables #define nsib 2 Title G1: Model Parameters Calculation Begin Matrices; X Lower nvar nvar Free! additive genetic structure Y Lower nvar nvar Free! common environmental structure Z Lower nvar nvar Free! unique environmental path struct. W Lower nvar nvar Free! dominance structure H Full 1 1! scalar for DZ cov of A Q Full 1 1! scalar for DZ cov of D End Matrices;

Declared Matrices P1 A1C1E1 P2 A2C2E2D1D a [X]c [Y]e [Z]a [X]c [Y]e [Z] / 0.50 [H]1.00 d [W] 1.00 d [W] 1.00 / 0.25 [Q]

Univariate Mx Script II Matrix H.5 Matrix Q.25 Start.5 all! starting values for free parameters Begin Algebra; A= X*X' ;! additive genetic variance C= Y*Y' ; ! common environmental variance E= Z*Z' ;! unique environmental variance D= W*W’;! dominance variance V= A+C+E+D;! total variance P= A|C|E|D;! put parameters in one matrix S= standardized variance components End Algebra; S 1 1 – S 1 3! confidence intervals End

Univariate Mx Script III G2: male MZ twins, datagroup Data NInput_vars=5 Missing=-1.00 Rectangular File= Agg10.rec Labels ZYG RB10A AGG10A RB10B AGG10B Select if zyg =1;! select MZM twins Select AGG10A AGG10B ; Begin Matrices = Group 1; M Full nsib nvar Free! means End Matrices; Start 0.5 M 1 1 M 1 2! starting values for means Means M;! model for means Covariances! model for MZ variance/covariances A+C+E+D| A+C+D _ A+C+D| A+C+E+D; Options RSiduals End

Univariate Mx Script IV G3: male DZ twins, datagroup Data NInput_vars= Missing=-1.00 Rectangular File= Agg10.rec Labels ZYG RB10A AGG10A RB10B AGG10B Select if zyg =2;! select DZM twins Select AGG10A AGG10B ; Begin Matrices = Group 1; M Full nsib nvar Free! means End Matrices; Start 0.5 M 1 1 M 1 2! starting values for means Means M;! model for means Covariances! model for DZ variance/covariances A+C+E+D| _ A+C+E+D; Option RSiduals End

Path Diagram to Matrices Path Coefficient aced Matrix NameXYZW Variance Component a2a2 c2c2 e2e2 d2d2 Matrix NameACED

Mx Script V Save satm.mxs ! equate means Equate M M N N End ! equate means and variances Equate X X Y Y End Get satm.mxs ! equate variances only Equate X X Y Y End