With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.

Organisms can change their physiological/behavioural traits to adapt and survive in changed environments. However, whether these acquired traits can be inherited across generations through non-genetic alterations has been a topic of debate for over a century. Emerging evidence indicates that both ancestral and parental experiences, including nutrition, environmental toxins, nurturing behaviour, and social stress, can have powerful effects on the physiological, metabolic and cellular functions in an organism. In certain circumstances, these effects can be transmitted across several generations through epigenetic (i.e. non-DNA sequence-based rather than mutational) modifications. In this review, we summarize recent evidence on epigenetic inheritance from parental environment-induced developmental and physiological alterations in nematodes, fruit flies, zebrafish, rodents, and humans. The epigenetic modifications demonstrated to be both susceptible to modulation by environmental cues and heritable, including DNA methylation, histone modification, and small non-coding RNAs, are also summarized. We particularly focus on evidence that parental environment-induced epigenetic alterations are transmitted through both the maternal and paternal germlines and exert sex-specific effects. The thought-provoking data presented here raise fundamental questions about the mechanisms responsible for these phenomena. In particular, the means that define the specificity of the response to parental experience in the gamete epigenome and that direct the establishment of the specific epigenetic change in the developing embryos, as well as in specific tissues in the descendants, remain obscure and require elucidation. More precise epigenetic assessment at both the genome-wide level and single-cell resolution as well as strategies for breeding at relatively sensitive periods of development and manipulation aimed at specific epigenetic modification are imperative for identifying parental environment-induced epigenetic marks across generations. Considering their diverse epigenetic architectures, the conservation and prevalence of the mechanisms underlying epigenetic inheritance in non-mammals require further investigation in mammals. Interpretation of the consequences arising from epigenetic inheritance on organisms and a better understanding of the underlying mechanisms will provide insight into how gene-environment interactions shape developmental processes and physiological functions, which in turn may have wide-ranging implications for human health, and understanding biological adaptation and evolution.