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Summary Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms because they may result in markedly different clinical phenotypes. Here, we generated an extensive database of documented germline GOF and LOF pathogenic variants by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF variants and applied machine learning and statistical analyses to identify discriminative features. We found that GOF variants were enriched in essential genes, for autosomal-dominant inheritance, and in protein binding and interaction domains, whereas LOF variants were enriched in singleton genes, for protein-truncating variants, and in protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a broad set of annotated features and downloading of up-to-date versions. These results improve our understanding of how variants affect gene/protein function and may ultimately guide future treatment options.

The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.

International audience; Human genes governing innate immunity provide a valuable tool for the study of the selective pressure imposed by microorganisms on host genomes. A comprehensive, genome-wide study of how selective constraints and adaptations have driven the evolution of innate immunity genes is missing. Using full-genome sequence variation from the 1000 Genomes Project, we first show that innate immunity genes have globally evolved under stronger purifying selection than the remainder of protein-coding genes. We identify a gene set under the strongest selective constraints, mutations in which are likely to predispose individuals to life-threatening disease, as illustrated by STAT1 and TRAF3. We then evaluate the occurrence of local adaptation and detect 57 high-scoring signals of positive selection at innate immunity genes, variation in which has been associated with susceptibility to common infectious or autoimmune diseases. Furthermore, we show that most adaptations targeting coding variation have occurred in the last 6,000-13,000 years, the period at which populations shifted from hunting and gathering to farming. Finally, we show that innate immunity genes present higher Neandertal introgression than the remainder of the coding genome. Notably, among the genes presenting the highest Neandertal ancestry, we find the TLR6-TLR1-TLR10 cluster, which also contains functional adaptive variation in Europeans. This study identifies highly constrained genes that fulfill essential, non-redundant functions in host survival and reveals others that are more permissive to change-containing variation acquired from archaic hominins or adaptive variants in specific populations-improving our understanding of the relative biological importance of innate immunity pathways in natural conditions.