Additional file 1: Table S1. of Fine mapping of QTL and genomic prediction using allele-specific expression SNPs demonstrates that the complex trait of genetic resistance to Marek’s disease is predominantly determined by transcriptional regulation

Content of custom SNP array. (XLSX 424 kb

Procedure Used to Obtain Overrepresented Oligomers Starting from the Overrepresented 12-Mers

<p>The overrepresented 12-mers were defined with the initial criteria: at least ten occurrences and at least 5-fold enrichment. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#s2" target="_blank">Results</a> and Methods for a detailed description.</p

The Distributions over the Length of L1 Element of Overrepresented Oligomers Found in <i>I</i> Subgenome (Yellow Bars) and of All L1 Sequences within Xp22 (Red Bars)

<div><p>Only overrepresented oligomers mapping frequently to L1s (>50% of their genomic occurrences in the <i>I</i> subgenome) are shown. Although the full-length L1 is approximately 7 kb long, the alignment of L1 subfamilies was approximately 9 kb long.</p><p>ORF, open reading frame.</p></div

The Distribution of Correctly and Incorrectly Classified Genes along the X Chromosome

<p>Dark green indicates correctly classified genes; light green indicates misclassified genes. X inactivation expression patterns [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#pgen-0020151-b006" target="_blank">6</a>] for genes included in this study: yellow indicates inactivated genes, and blue indicates escape genes. Not all genes were analyzed at all distances because sequences that included adjacent genes with <i>different</i> inactivation patterns were excluded from analysis (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#s4" target="_blank">Methods</a>). These gene distances remain uncolored.</p

LDA Classification Success Rates for Different Values of the Tuning Parameter τ

<div><p>(A) Training set derived largely, but not exclusively, from Xp22 (See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#pgen-0020151-st003" target="_blank">Table S3</a>).</p><p>(B) Test set of Xp22 genes, with training performed on genes in (A).</p><p>(C) Test set of X genes outside of Xp22, with training performed on genes in (A).</p><p>(D) Training set of all X genes, including genes in Xp22. Dots indicate optimal values of τ (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#pgen-0020151-t004" target="_blank">Table 4</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020151#s4" target="_blank">Methods</a>).</p></div

On multiplicative structure in Quasi-Newton methods for nonlinear equations

We address the problem how additive and multiplicative structure in the derivatives can be exploited for the construction of Quasi-Newton approximations in smooth nonlinear equations. We derive a model algorithm and show its convergence properties based on a Broyden-like update rule. As a consequence of the use of exact multiplicative parts the convergence factor of the q-linear convergence rate is monotonically decreasing with the norm of the multiplicative part at the solution. Moreover, q-superlinear convergence can be shown, if certain compactness properties are valid, and q-quadratic convergence is obtained, if the multiplicative part vanishes at the solutionAvailable from TIB Hannover: RR 1843(92-22) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman