Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies

<div><p>Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.</p> </div

The numbers and proportion of nsSNVs removed by hard-filtering and functional prediction by the logit model in 3 Mendelian-disease patients with in-house exome sequencing data.

a<p>Related cases with autosomal dominant spinocerebellar ataxia.</p>b<p>Case with neonatal-onset Crohn's disease.</p>c<p>nsSNVs in which prediction is unavailable due to missing scores.</p

Theoretical inbreeding coefficient (<i>F</i>) and corresponding number of homozygous pathogenic variants in the children of various relationships, given that on average each individual carries 22 pathogenic derived alleles.

<p>Theoretical inbreeding coefficient (<i>F</i>) and corresponding number of homozygous pathogenic variants in the children of various relationships, given that on average each individual carries 22 pathogenic derived alleles.</p

Measurement of inter-orbital distance in axial and coronal view.

<p>Top panel shows coronal view and lower panel shows the axial view. Measurements were made on the coronal view between A and B, with the axial view used for additional reference.</p

Mann–Whitney <i>U</i> test <i>p</i> values for the difference in prediction scores between autosomal dominant and autosomal recessive disease-causing mutations.

<p>Mann–Whitney <i>U</i> test <i>p</i> values for the difference in prediction scores between autosomal dominant and autosomal recessive disease-causing mutations.</p

ROC and PR curves of prediction methods evaluated on the ExoVar dataset using a 10-fold cross-validation.

<p>(a) ROC and (b) PR. AUC is shown next to the name of each method.</p

ROC and PR curves of prediction methods evaluated on the DomRec dataset using a 3-fold cross-validation.

<p>(a) ROC and (b) PR. AUC is shown next to the name of each method.</p

ROC and PR curves of prediction methods evaluated on the HumVar dataset using a 10-fold cross-validation.

<p>(a) ROC and (b) PR. AUC is shown next to the name of each method.</p

The relationship between prior and posterior probabilities of a rare nsSNV being pathogenic, given the prediction scores from SIFT, PolyPhen2, and MutationTaster.

<p>The white dashed lines indicate the estimated range of the prior (5%). We assume that there is no difference in prediction scores from the three methods for the same variant. The <i>α</i>, <i>β</i>_SIFT, <i>β</i>_Polyphen2 and <i>β</i>_{MutationTaster} in a selected sample evaluated in the ExoVar dataset are used in the calculation of posteriors (See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003143#pgen.1003143.e002" target="_blank">Eq. 2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003143#pgen.1003143.e003" target="_blank">3</a> in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003143#s4" target="_blank">Materials and Methods</a>) and take the values of −3.53, 1.64, 1.48, and 2.47 respectively. The prior and posterior are equivalent to the quantity <i>P</i>_disease in an individual genome in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003143#pgen.1003143.e003" target="_blank">Eq. 3</a> and P(<i>Y</i> = 1|<i>X</i>) in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003143#pgen.1003143.e002" target="_blank">Eq. 2</a> respectively.</p

Scatter plot of grey matter volume against inter-orbital distance of a cluster in left and right amygdala region.

<p>Inter-orbital distance in mm. Grey matter volume in ml. Autism group represented by triangles and controls by circles. Left amygdala cluster volume correlation with inter-orbital distance is shown in the left panel. Right amygdala cluster correlation is shown in the right panel.</p