School of Mathematics and Statistics - Research Publications

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    Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation
    Traynelis, J ; Silk, M ; Wang, Q ; Berkovic, SF ; Liu, L ; Ascher, DB ; Balding, DJ ; Petrovski, S (COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT, 2017-10)
    Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10-16). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.
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    Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies
    Abou-Khalil, B ; Auce, P ; Avbersek, A ; Bahlo, M ; Balding, DJ ; Bast, T ; Baum, L ; Becker, AJ ; Becker, F ; Berghuis, B ; Berkovic, SF ; Boysen, KE ; Bradfield, JP ; Brody, LC ; Buono, RJ ; Campbell, E ; Cascino, GD ; Catarino, CB ; Cavalleri, GL ; Cherny, SS ; Chinthapalli, K ; Coffey, AJ ; Compston, A ; Coppola, A ; Cossette, P ; Craig, JJ ; de Haan, G-J ; De Jonghe, P ; de Kovel, CGF ; Delanty, N ; Depondt, C ; Devinsky, O ; Dlugos, DJ ; Doherty, CP ; Elger, CE ; Eriksson, JG ; Ferraro, TN ; Feucht, M ; Francis, B ; Franke, A ; French, JA ; Freytag, S ; Gaus, V ; Geller, EB ; Gieger, C ; Glauser, T ; Glynn, S ; Goldstein, DB ; Gui, H ; Guo, Y ; Haas, KF ; Hakonarson, H ; Hallmann, K ; Haut, S ; Heinzen, EL ; Helbig, I ; Hengsbach, C ; Hjalgrim, H ; Iacomino, M ; Ingason, A ; Jamnadas-Khoda, J ; Johnson, MR ; Kalviainen, R ; Kantanen, A-M ; Kasperaviciute, D ; Trenite, DK-N ; Kirsch, HE ; Knowlton, RC ; Koeleman, BPC ; Krause, R ; Krenn, M ; Kunz, WS ; Kuzniecky, R ; Kwan, P ; Lal, D ; Lau, Y-L ; Lehesjoki, A-E ; Lerche, H ; Leu, C ; Lieb, W ; Lindhout, D ; Lo, WD ; Lopes-Cendes, I ; Lowenstein, DH ; Malovini, A ; Marson, AG ; Mayer, T ; McCormack, M ; Mills, JL ; Mirza, N ; Moerzinger, M ; Moller, RS ; Molloy, AM ; Muhle, H ; Newton, M ; Ng, P-W ; Noethen, MM ; Nuernberg, P ; O'Brien, TJ ; Oliver, KL ; Palotie, A ; Pangilinan, F ; Peter, S ; Petrovski, S ; Poduri, A ; Privitera, M ; Radtke, R ; Rau, S ; Reif, PS ; Reinthaler, EM ; Rosenow, F ; Sander, JW ; Sander, T ; Scattergood, T ; Schachter, SC ; Schankin, CJ ; Scheffer, IE ; Schmitz, B ; Schoch, S ; Sham, PC ; Shih, JJ ; Sills, GJ ; Sisodiya, SM ; Slattery, L ; Smith, A ; Smith, DF ; Smith, MC ; Smith, PE ; Sonsma, ACM ; Speed, D ; Sperling, MR ; Steinhoff, BJ ; Stephani, U ; Stevelink, R ; Strauch, K ; Striano, P ; Stroink, H ; Surges, R ; Tan, KM ; Thio, LL ; Thomas, GN ; Todaro, M ; Tozzi, R ; Vari, MS ; Vining, EPG ; Visscher, F ; von Spiczak, S ; Walley, NM ; Weber, YG ; Wei, Z ; Weisenberg, J ; Whelan, CD ; Widdess-Walsh, P ; Wolff, M ; Wolking, S ; Yang, W ; Zara, F ; Zimprich, F (NATURE PUBLISHING GROUP, 2018-12-10)
    The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.