P3 faculty are busy this summer publishing their research findings. Video created by Johns Hopkins University for the course "Statistics for Genomic Data Science".

Writing – review & editing, * E-mail: nikola.mueller@helmholtz-muenchen.de. To test the validity of the resulting regulatory predictions for the credible set variants, we tested whether there was an enrichment of variants predicted to affect any of the six feature groups among pancreatic islet expression QTLs (eQTLs) from a recent study in 420 individuals (Viñuela et al., 2019). Random forests. 2013;18: 340–346. No, Is the Subject Area "Multiple sclerosis" applicable to this article? Competing interests: The authors declare that no competing interests exist. MS-specific dSNPs were significantly enriched in the regions active in different immune cell types, height-specific dSNPs in skeletal muscle cells, pericytes, and also a pancreatic carcinoma cell line, and MDD-specific dSNPs in neurocytoma as well as in different brain regions and immune cells (p-value ≤0.05, see Fig 3D). For example, MS is an immune-mediated disorder affecting the central nervous system (CNS). Pathogenic annotation was performed using the CADD webserver. The problem with this will be with getting a relevant GWAS data set that aligns with a very densely-profiled cell line and other orthogonal validation data.

At 25 signals, integration of CNN regulatory predictions downstream of the functional fine-mapping highlighted a single most likely causal variant, with either just a single islet regulatory variant predicted among the credible variants, or the top regulatory variant having much lower q-value (difference in -log10(q) > 100) than other predicted regulatory variants at the signal (a few such signals are highlighted in Figure 4 and Figure 5), narrowing down the list of candidates for further functional follow-up studies. However, our understanding of the determinants of TF binding to known sequence motifs is still incomplete, limiting the success of such methods. (A) Distribution of CNN-predicted regulatory variants (q < 0.05) in the six broader CNN feature groups. German Competence Network Multiple Sclerosis (KKNMS), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, The eQTLs obtained from GTEx were filtered on gene p<0.05 and eQTLs obtained from BIOS and CMC were filtered on FDR <0.05. In all the three DeepWAS, this approach identified regulatory SNPs that overlapped not only with SNPs associated with the same traits in the cohort-matched GWAS, but also with genome-wide significant associations from the larger consortia GWAS for these traits.

For example, 23% of the 53 MS-specific dSNPs were previously identified in the ISMGC MS GWAS including more than 135,000 individuals (n>47,000 MS cases). R.G., B.H., and H.W. (2019) were one of the only studies to focus on comparing feature selection methods as well as models. Methodology,

Model. Sci. Doing so could then lead to more accessible, reusable models – for example with source code or web-interfaces that are useable by a wider range of GWAS researchers – and create more globally implemented ML applications for GWAS prioritization, thus accelerating researchers towards the post-GWAS endgame of understanding disease. 11:350. doi: 10.3389/fgene.2020.00350. The data were then adjusted using “ComBat” function of the Bioconductor R package sva (https://bioconductor.org/packages/release/bioc/html/sva.html). By applying logistic regression with elastic net they could then select the best data for their models (309 features selected which are predominantly from biological ontologies). We next grouped SNPs that are predicted to moderate the same FU, resulting in 919 SNP sets.

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