title: Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes.
abstract: Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information.
  
  
  
  We examined the characteristics of Mendelian disease genes under different inheritance modes. The results suggest that Mendelian disease genes with autosomal dominant (AD) inheritance mode are more haploinsufficiency and de novo mutation sensitive, whereas those autosomal recessive (AR) genes have significantly more non-synonymous variants and regulatory transcript isoforms. In addition, the X-linked (XL) Mendelian disease genes have fewer non-synonymous and synonymous variants. As a result, we derived a new scoring system for prioritizing candidate genes for Mendelian diseases according to the inheritance mode. Our scoring system assigned to each annotated protein-coding gene (N = 18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve  = 0.84) in XL mode.
  
  
  
  The inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores. This systematic study suggests that genes manifesting disease inheritance modes tend to have unique characteristics.
  
  
  
  ISPP is included in KGGSeq v1.0 (http://grass.cgs.hku.hk/limx/kggseq/), and source code is available from (https://github.com/jacobhsu35/ISPP.git).
  
  
  
  mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Psychiatry.
authors.forename: Jacob Shujui
authors.initials: JS
authors.lastname: Hsu
authors.affiliation: Department of Psychiatry.
authors.forename: Johnny S H
authors.initials: JS
authors.lastname: Kwan
authors.affiliation: Department of Psychiatry.
authors.forename: Zhicheng
authors.initials: Z
authors.lastname: Pan
authors.affiliation: Department of Surgery.
authors.forename: Maria-Mercè
authors.initials: MM
authors.lastname: Garcia-Barcelo
authors.affiliation: Department of Psychiatry Centre for Genomics Science, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.
authors.forename: Pak Chung
authors.initials: PC
authors.lastname: Sham
authors.affiliation: Department of Psychiatry Centre for Genomics Science, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.
authors.forename: Miaoxin
authors.initials: M
authors.lastname: Li
mesh_terms: D019540:Area Under Curve; D030541:Databases, Genetic; D005799:Genes, Dominant; D005808:Genes, Recessive; D014644:Genetic Variation; D006801:Humans; D009154:Mutation; D011506:Proteins / Q000235:genetics*
chemical_list: D011506:Proteins