Batch effects and their COMBAT adjustment on merging Nelson and Rothman datasets.

<p>Batch effects and their COMBAT adjustment on merging Nelson and Rothman datasets.</p

The classification statistics, including SR, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were recorded after the classification of each dataset.

<p>The classification statistics, including SR, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were recorded after the classification of each dataset.</p

The SR of the optimised feature lists on the NelsonB and Rothman datasets.

<p>The SR of the optimised feature lists on the NelsonB and Rothman datasets.</p

Upper limit of 95% confidence interval (CI Upper) and lower limit of 95% confidence interval (CI Lower) for SR, sensitivity, specificity, PPV and NPV were recorded after the classification of each dataset.

<p>Upper limit of 95% confidence interval (CI Upper) and lower limit of 95% confidence interval (CI Lower) for SR, sensitivity, specificity, PPV and NPV were recorded after the classification of each dataset.</p

The ROC graph is plotted to show the performance of the binary classifiers.

<p>The ROC graph is plotted to show the performance of the binary classifiers.</p

A MDS plot created for Beata dataset to show the distribution of its cases and controls.

<p>A MDS plot created for Beata dataset to show the distribution of its cases and controls.</p

Incorporating Non-Coding Annotations into Rare Variant Analysis

<div><p>Background</p><p>The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.</p><p>Methods & Results</p><p>We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between <i>ANGPTL4</i> and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.</p><p>Conclusion</p><p>Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.</p></div

Results of gene-level rare variant association tests using various variant filters (MAF ≤ 1%).

<p>Results of gene-level rare variant association tests using various variant filters (MAF ≤ 1%).</p

Results of gene-level low frequency variant association tests using various variant filters (MAF ≤ 5%).

<p>Results of gene-level low frequency variant association tests using various variant filters (MAF ≤ 5%).</p

Hexbin plots representing gene-based SKAT analyses for all genes across the genome using a MAF cutoff of 1% with 4 lipid traits.

<p>The x-axis represents the–log10 transformed p-value from the analysis after filtering according to CADD annotations. The y-axis represents the–log10 transformed p-value from the analysis after filtering according to ‘nonsynonymous’ annotations. Only gene regions which had at least 2 variants in them after filtering by both methods were plotted.</p