David Evans University of Queensland

Slides:



Advertisements
Παρόμοιες παρουσιάσεις
Προβλέψεις με τη χρήση προτύπων γραμμικής παλινδρόμησης και συσχέτισης
Advertisements

Click here to start Important !: You have to enable macros for this game (Tools ->Macros -> Security -> «medium»).
“ Ἡ ἀ γάπη ἀ νυπόκριτος. ἀ ποστυγο ῦ ντες τ ὸ πονηρόν, κολλώμενοι τ ῷ ἀ γαθ ῷ, τ ῇ φιλαδελφί ᾳ ε ἰ ς ἀ λλήλους φιλόστοργοι, τ ῇ τιμ ῇ ἀ λλήλους προηγούμενοι.
WRITING TEACHER ELENI ROSSIDOU ©Υπουργείο Παιδείας και Πολιτισμού.
Further Pure 1 Roots of Equations. Properties of the roots of cubic equations Cubic equations have roots α, β, γ (gamma) az 3 + bz 2 + cz + d = 0 a(z.
ΗΥ Παπαευσταθίου Γιάννης1 Clock generation.
Week 11 Quiz Sentence #2. The sentence. λαλο ῦ μεν ε ἰ δότες ὅ τι ὁ ἐ γείρας τ ὸ ν κύριον Ἰ ησο ῦ ν κα ὶ ἡ μ ᾶ ς σ ὺ ν Ἰ ησο ῦ ἐ γερε ῖ κα ὶ παραστήσει.
WRITING B LYCEUM Teacher Eleni Rossidou ©Υπουργείο Παιδείας και Πολιτισμού.
Πολυώνυμα και Σειρές Taylor 1. Motivation Why do we use approximations? –They are made up of the simplest functions – polynomials. –We can differentiate.
Διοίκηση Απόδοσης Επιχειρηματικών Διαδικασιών Ενότητα #5: Key result indicators (KRIs), Performance Indicators (PIs), Key Performance Indicators (KPIs)
Προσομοίωση Δικτύων 4η Άσκηση Σύνθετες τοπολογίες, διακοπή συνδέσεων, δυναμική δρομολόγηση.
 Το παρόν εκπαιδευτικό υλικό υπόκειται σε άδειες χρήσης Creative Commons.  Για εκπαιδευτικό υλικό, όπως εικόνες, που υπόκειται σε άλλου τύπου άδειας.
Time Management Matrix Assignment Submitted By Safwan Zubair October 21, 2013 BUS Contemporary Business Practice Professor Nankin.
Αριθμητική Επίλυση Διαφορικών Εξισώσεων 1. Συνήθης Δ.Ε. 1 ανεξάρτητη μεταβλητή x 1 εξαρτημένη μεταβλητή y Καθώς και παράγωγοι της y μέχρι n τάξης, στη.
1 ΔΗΜΟΠΑΘΟΛΟΓΙΑ ΤΗΣ ΔΙΑΤΡΟΦΗΣ ΠΑΡΑΔΟΣΗ 1Οη (Θ) Στοιχεία Επαγωγικής Στατιστικής.
Διαχείριση Διαδικτυακής Φήμης! Do the Online Reputation Check! «Ημέρα Ασφαλούς Διαδικτύου 2015» Ε. Κοντοπίδη, ΠΕ19.
Introduction to Latent Variable Models. A comparison of models X1X1 X2X2 X3X3 Y1Y1 δ1δ1 δ2δ2 δ3δ3 Model AModel B ξ1ξ1 X1X1 X2X2 X3X3 δ1δ1 δ2δ2 δ3δ3.
From Applying Theory to Theorising Practice Achilleas Kostoulas Epirus Institute of Technology.
Guide to Business Planning The Value Chain © Guide to Business Planning A principal use of value chain analysis is to identify a strategy mismatch between.
Guide to Business Planning The Value System © Guide to Business Planning The “value system” is also referred to as the “industry value chain”. In contrast.
Διασύνδεση LAN Γιατί όχι μόνο ένα μεγάλο LAN
Ερωτήσεις –απαντήσεις Ομάδων Εργασίας
Αντικειμενοστραφής Προγραμματισμός ΙΙ
Φάσμα παιδαγωγικής ανάπτυξης
Jane Austen Pride and Prejudice (περηφάνια και προκατάληψη)
JSIS E 111: Elementary Modern Greek
Η Ύλη του Μαθήματος Επανάληψη της πολλαπλή παλινδρόμησης και Ασυμπτωτική κατανομή της εκτιμήτριας ελαχίστων τετραγώνων. Βοηθητικές μεταβλητές και παλινδρόμηση.
Matrix Analytic Techniques
JSIS E 111: Elementary Modern Greek
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ
Class X: Athematic verbs II
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ
ΟΡΓΑΝΩΣΗ ΑΘΛΗΤΙΚΗΣ ΕΓΚΑΤΑΣΤΑΣΗΣ
Το χάρτινο θέατρο εμφανίζεται στη Ευρώπη στα τέλη του 18ου αιώνα
Εξόρυξη δεδομένων και διαχείριση δεδομένων μεγάλης κλίμακας
(ALPHA BANK – EUROBANK – PIRAEUS BANK)
Example Rotary Motion Problems
Γεώργιος Σ. Γκουμάς MD,PhD, FESC
Choosing between Competing Experimental Designs
Μία πρακτική εισαγωγή στην χρήση του R
ΥΠΟΥΡΓΕΙΟ ΠΑΙΔΕΙΑΣ ΚΑΙ ΠΟΛΙΤΙΣΜΟΥ
Find: φ σ3 = 400 [lb/ft2] CD test Δσ = 1,000 [lb/ft2] Sand 34˚ 36˚ 38˚
JSIS E 111: Elementary Modern Greek
Ενημέρωση για eTwinning
JSIS E 111: Elementary Modern Greek
aka Mathematical Models and Applications
GLY 326 Structural Geology
ΕΝΣΤΑΣΕΙΣ ΠΟΙΟΣ? Όμως ναι.... Ένα σκάφος
Find: minimum B [ft] γcon=150 [lb/ft3] γT=120 [lb/ft3] Q φ=36˚
Choosing between Competing Experimental Designs
ΤΙ ΕΙΝΑΙ ΤΑ ΜΟΆΙ;.
Find: ρc [in] from load γT=110 [lb/ft3] γT=100 [lb/ft3]
Find: ρc [in] from load γT=106 [lb/ft3] γT=112 [lb/ft3]
Find: σ1 [kPa] for CD test at failure
τ [lb/ft2] σ [lb/ft2] Find: c in [lb/ft2] σ1 = 2,000 [lb/ft2]
Financial Market Theory
ΜΕΤΑΦΡΑΣΗ ‘ABC of Selling’. ΤΟ ΑΛΦΑΒΗΤΑΡΙ ΤΩΝ ΠΩΛΗΣΕΩΝ
Find: Force on culvert in [lb/ft]
We can manipulate simple equations:
3Ω 17 V A3 V3.
Deriving the equations of
Variable-wise and Term-wise Recentering
Find: ρc [in] from load (4 layers)
Εθνικό Μουσείο Σύγχρονης Τέχνης Faceforward … into my home!
CPSC-608 Database Systems
Erasmus + An experience with and for refugees Fay Pliagou.
Class X: Athematic verbs II © Dr. Esa Autero
Μεταγράφημα παρουσίασης:

David Evans University of Queensland Using SEM to partition genetic effects of individual SNPs into maternal and fetal components David Evans University of Queensland

Post-doctoral Position in Statistical Genetics/Genomics

Objectives Illustrate the flexibility of SEM Show how SEM can be used to investigate molecular mechanisms Illustrate model building Illustrate the concept of identification Revise key concepts from the week

Birthweight GWAS (EGG and UKBB) Nicole Warrington UKBB and EGG consortium Birthweight GWAS reflects a mixture of maternal and fetal genetic effects Unrelated individuals*

Conditional Analysis of Genotyped Mother-Offspring Duos Conditional Regression: BWi = βmSNPmi + βcSNPci + εi ϵSNP1 1 SNPm βm Structural Equation Model: 0.5 βc 1 ϵSNP2 SNPc BW 1 ϵ BUT- not many cohorts in the world with these data! Are twins suitable for these analyses?

Disentangling Mother and Child Effects on Birth Weight in UKBB UKBB contains self-reported birthweight and reported birthweight of first offspring Φ GM m 0.5 SNP = ½GM + εSNP BW 1 ϵ c 0.75Φ 1 ϵSNP SNP BW = mGM + cSNP + ε ρ m BWO 1 BWO = cGO + mSNP + εO ϵO 0.5 c 0.75Φ GO

Tracing Rules of Path Analysis Find All Distinct Chains between Variables: Go backwards along zero or more single-headed arrows Change direction at one and only one Double-headed arrow Trace forwards along zero or more Single-headed arrows Multiply path coefficients in a chain Sum the results of step 2 For covariance of a variable with itself (Variance), chains are distinct if they have different paths or a different order

Building The Model: Path Tracing Rules SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ 1 ϵSNP SNP ρ m BWO 1 ϵO 0.5 Note that variances for birthweight are going to be the same (assuming the same measurement error) Variance of SNPs (observed and latent) all constrained to be equal c 0.75Φ GO

Building the Model: Covariance Algebra SNP = ½GG + εSNP BW = mGG + cSNP + ε BWO = cGO + mSNP + εO Φ GG m 0.5 BW 1 ϵ c 0.75Φ 1 ϵSNP SNP ρ (1) cov(cX, Y) = c x cov(X, Y) (2) cov(X + Y, Z) = cov(X, Z) + cov(Y, Z) (3) cov(X, X) = var(X) m BWO 1 ϵO 0.5 c 0.75Φ GO var(SNP)= cov (SNP, SNP) = cov(½GG + εSNP , ½GG + εSNP ) = cov(½GG , ½GG) + cov(½GG , εSNP ) + cov(εSNP ,½GG) + cov( εSNP , εSNP ) = ¼cov(GG , GG) + 0 + 0 + cov( εSNP , εSNP ) = ¼var(GG) + var( εSNP) = Φ

Building the Model: Covariance Algebra SNP = ½GG + εSNP BW = mGG + cSNP + ε BWO = cGO + mSNP + εO Φ GG m 0.5 BW 1 ϵ c 0.75Φ 1 ϵSNP SNP ρ (1) cov(cX, Y) = c x cov(X, Y) (2) cov(X + Y, Z) = cov(X, Z) + cov(Y, Z) (3) cov(X, X) = var(X) m BWO 1 ϵO 0.5 c 0.75Φ GO cov(BW, SNP)= cov(mGG + cSNP + ε , ½GG + εSNP ) = cov(mGG,½GG) + cov(mGG,εSNP) + cov(cSNP,½GG) + cov(cSNP,εSNP) + cov(ε,½GG) + cov(ε,εSNP) = ½mcov(GG,GG) + mcov(GG,εSNP) + ½ccov(SNP,GG) + ccov(SNP,εSNP) + ½cov(ε,GG) + cov(ε,εSNP) = ½mvar(GG) + 0 + ½c ½Φ + c¾Φ + 0 + 0 = ½mΦ + cΦ

Σ = Σ(θ) Understanding SEM Σ = Σ(θ) Observed Sample Covariance Matrix Expected Covariance Matrix Expected covariance matrix a function of model parameters Parameters chosen to minimize the difference between observed and expected covariance matrices

Identification Means that all parameters in a model can be estimated uniquely given the data A necessary (but not sufficient condition) for identifiability is that you have the same (or more) observed statistics than parameters you want to estimate If all parameters in a model are identified, then the model as a whole is identified Even though the model as a whole may be unidentified some parameters may be identified

Identified or Not? (1) θ1 + θ2 = 10 (2) θ1 + θ2 = 10 θ1 - θ2 = 0 (3) θ1 + θ2 = 10 2θ1 +2θ2 = 20

Identification in Twin Models OBSERVED EXPECTED VARMZ-T1 VA + VC + VE ΣMZ = Σ(θ)MZ = COVMZ VARMZ-T2 VA + VC VA + VC + VE VARDZ-T1 VA + VC + VE Σ DZ = Σ(θ)DZ = COVDZ VARDZ-T2 ½VA + VC VA + VC + VE How many observed statistics? Why can’t we model VA, VC, VD, VE How many parameters?

Identified or Not? How many observed statistics? How many parameters? SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO How many observed statistics? How many parameters?

Identified or Not? Φ Φ = var(SNP) SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO Φ = var(SNP)

Identified or Not? - c and m SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO cΦ + ½mΦ = cov(BW, SNP) mΦ + ½cΦ = cov(BWO,SNP)

Identified or Not? var(ε) SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO m2Φ + c2Φ + mcΦ + var(ε) = var(BW)

Identified or Not? var(εO) SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO m2Φ + c2Φ + mcΦ + var(εO) = var(BWO)

½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ = cov(BW, BWO) Identified or Not? ρ SNP BW BWO Φ cΦ + ½mΦ mΦ + ½cΦ m2Φ + c2Φ + mcΦ + var(ε) ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εO) Φ GG m 0.5 BW 1 ϵ c 0.75Φ ϵSNP 1 SNP ρ m BWO 1 ϵO 0.5 c 0.75Φ GO ½ m2Φ + ½c2Φ + ¼mcΦ + mcΦ + ρ = cov(BW, BWO)

Can we go further?

Disentangling Mother and Child Effects on Birth Weight SNP SBP SBPm Φ GG m 0.5 SBPm ϵm c 0.75Φ Gm ρ m SBP ϵ 0.5 c 0.75Φ SNP

Disentangling Mother and Child Effects on Birth Weight SNP SBP SBPm Φ cΦ + ½mΦ ½cΦ + ¼ mΦ m2Φ + c2Φ + mcΦ + var(ε) ½ c2Φ + ½m2Φ + mcΦ + ¼ mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εm) Φ GG m 0.5 SBPm ϵm c 0.75Φ Gm ρ m SBP ϵ 0.5 c 0.75Φ SNP

Identified or Not? How many observed statistics? How many parameters? SNP SBP SBPm Φ cΦ + ½mΦ ½cΦ + ¼ mΦ m2Φ + c2Φ + mcΦ + var(ε) ½ c2Φ + ½m2Φ + mcΦ + ¼ mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εm) Φ GG m 0.5 SBPm ϵm c 0.75Φ Gm ρ m SBP ϵ 0.5 c 0.75Φ SNP How many observed statistics? How many parameters?

Identified or Not? Φ SNP SBP SBPm Φ cΦ + ½mΦ ½cΦ + ¼ mΦ m2Φ + c2Φ + mcΦ + var(ε) ½ c2Φ + ½m2Φ + mcΦ + ¼ mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εm) Φ GG m 0.5 SBPm ϵm c 0.75Φ Gm ρ m SBP ϵ 0.5 c 0.75Φ SNP

Identified or Not? c,m? SNP SBP SBPm Φ cΦ + ½mΦ ½cΦ + ¼ mΦ m2Φ + c2Φ + mcΦ + var(ε) ½ c2Φ + ½m2Φ + mcΦ + ¼ mcΦ + ρ m2Φ + c2Φ + mcΦ + var(εm) Φ GG m 0.5 SBPm ϵm c 0.75Φ Gm ρ m SBP ϵ 0.5 c 0.75Φ SNP

Intuition To estimate maternal effects, we need individuals with observed genotypes who have reported their offspring’s phenotype In the first situation where we examine birthweight we have this In the second situation where we examine blood pressure we do not

Questions?