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- 1. Application of Bayesian and Sparse Network Models for Assessing Linkage Disequilibrium in Animals and Plants C-36-6 Gota Morota Department of Animal Sciences University of Wisconsin-Madison Aug 30, 2012 1 / 16
- 2. Systems Genetics Figure 1: Multi-dimensional gene network Purpose of this study • take the view that loci associate and interact together as a network • evaluate LD reﬂecting the biological nature that loci interact as a complex system 2 / 16
- 3. IAMB algorithm Incremental Association Markov Blanket (Tsamardinos et al. 2003) 1. Compute Markov Blankets (MB) 2. Compute Graph Structure 3. Orient Edges Figure 2: The Markov Blanket of a node xi 3 / 16
- 4. Identifying the MB of a node • Growing phase • heuristic function: f (X ; T |CMB ) = MI(X ; T |CMB ) = cmb ∈CMB P (CMB ) P (X , T |CMB ) P (X , T |CMB ) log P (X |CMB )P (T |CMB ) x ∈X t ∈T • conditional independence tests (Pearson’s χ2 test): H0 : P (X , T |CMB ) = P (X |CMB ) · P (T |CMB ) (do not add X ) HA : P (X , T |CMB ) P (X |CMB ) · P (T |CMB ) (add X to the CMB) • Shrinking phase • conditional independence tests (Pearson’s χ2 test): H0 : P (X , T |CMB − X ) P (X |CMB − X ) · P (T |CMB − X ) (keep X ) HA : P (X , T |CMB − X ) = P (X |CMB − X ) · P (T |CMB − X ) (remove X 4 / 16
- 5. Network Structure Algorithm Suppose Y ∈ MB (T ). Then T and Y are connected if they are conditionally dependent given all subsets of the smaller of MB (T ) − (Y ) and MB (Y ) − (T ). Example: • MB (T ) = (A , B , Y ), MB (Y ) = (C , D , E , F , T ) • since MB (T ) < MB (Y ), independence tests are conditional on all subsets of MB (T ) − (Y ) = (A , B ). • if any of the CI(T , Y |{}), CI(T , Y |{A }), CI(T , Y |{B }), andCI(T , Y |{A , B }) imply conditional independence, ↓ • T and Y are considered separate (spouses) • repeat for T ∈ S and Y ∈ MB (T ), 5 / 16
- 6. Materials 1. Data • 4,898 Holstein bulls (USDA-ARS AIPL) • 37,217 SNP markers (MAF > 0.025) • milk protein yield 2. Missing genotypes imputation • fastPHASE (Scheet and Stephens, 2006) 3. Select 15 SNPs • Bayesian LASSO 4. uncover associations among a set of marker loci found to have the strongest effects on milk protein yield 6 / 16
- 7. Results – Top 15 SNPs IAMB algorithm Pairwise LD among SNPs (r2) J d A c b a Z L Y X M N W V U F T S B K G R Q P H O N O E M L K J I I H G F E C D C B R2 Color Key A 0 Figure 3: r 2 0.2 0.4 0.6 0.8 1 D Figure 4: IAMB 7 / 16
- 8. Conclusion and Possible Improvements • LD relationships are of a multivariate nature • r 2 gives an incomplete description of LD ⇓ • undirected networks • sparsity 8 / 16
- 9. Pairwise Binary Markov Networks We estimate the Markov network parameters Θp ×p by maximizing a log-likelihood. f (x1 , ..., xp ) = exp Ψ(Θ) 1 p θj ,j xj + j =1 1≤j <k ≤p θj ,k xj xk (1) where xj ∈ {0, 1} Ψ(Θ) = x ∈0 , 1 (2) exp p θj ,j xj + j =1 1 ≤j <k ≤p θj ,k xj xk (3) • the ﬁrst term is a main effect of binary marker xj (node potential) • the second term corresponds to an“interaction effect” between binary markers xj and xk (link potential) • Ψ(Θ) is the normalization constant (partition function) 9 / 16
- 10. Ravikumar et al. (2010) The pseudo-likelihood based on the local conditional likelihood associated with each binary marker can be represented as n p x φi ,ij,j (1 − φi ,j )1−xi,j l (Θ) = (4) i =1 j =1 where φi ,j is the conditional probability of xi ,j = 1 given all other variables. Using a logistic link function, φi ,j = P(xi ,j = 1|xi ,k , k j ; θj ,k , 1 ≤ k ≤ p ) exp(θj ,j + k j θj ,k xi ,k ) = 1 + exp(θj ,j + k j θj ,k xi ,k ) (5) (6) 10 / 16
- 11. Ravikumar et al. (2010) (cont.) • L1 regularized logistic regressions problem • regressing each marker on the rest of the markers • the network structure is recovered from the sparsity pattern of the regression coefﬁcients 0 ˆ−2 β 1 ˆ . . Θ= . −(p −1) ˆ β 1 −p ˆ β1 ˆ β −1 , 2 0 ··· , ··· , ··· , 0 ˆ−(p −1) · · · , β p −2 ˆ p · · · , β−−2 p ˜ Θ= ˆ ˆ Θ • ΘT ˆ 1 β−−1 p ˆ 2 β−−1 p ˆp β −1 ˆp β −2 . . . ··· , −(p −1) ˆp 0 β −p ˆ β p −1 0 (7) (8) 11 / 16
- 12. Materials 1. Data • 599 inbred wheat lines (CIMMYT) • 1447 Diversity Array Technology (DArT) binary markers • mean grain yields 2. Select 30 SNPs • Bayesian LASSO 3. Benchmark methods • IAMB algorithm • r2 12 / 16
- 13. lambda = CV 9 8 7 Bayesian Network 9 6 10 8 7 6 10 5 11 4 3 12 5 11 13 4 3 12 13 2 2 14 1 14 15 0 15 0 16 29 16 29 28 17 18 27 19 26 20 25 21 22 23 24 Figure 5: L1 regularization 1 28 17 18 27 19 26 20 25 21 22 23 24 Figure 6: IAMB 13 / 16
- 14. Summary Interactions and associations among the cells and genes form a complex biological system ⇓ • r 2 → association(m1, m2)|∅ (empty set) • L1 regularized MN → association(m1, m2) | else A ﬁnal remark • selecting tag SNPs unconditionally, as well as conditionally, on other markers when the dimension of the data is high • data generated from next generation sequence technologies 14 / 16
- 15. Acknowledgments University of Wisconsin-Madison • Daniel Gianola • Guilherme Rosa University College London • Marco Scutari • Kent Weigel • Bruno Valente 15 / 16