Background/Aims: If the parents of an individual are related, it is possible for the individual to have received at 1 locus 2 identical-by-descent alleles that are copies of a single allele carried by the parents' common ancestor. The inbreeding coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of the genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based inbreeding coefficient f, and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers. Methods: For this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of the ERSA software. Results: Single-point methods were found to have higher standard deviations than other methods. ROHs gave the best estimates provided the correct length threshold is known. HMMs on sparse data gave equivalent or better results than HMMs modeling LD. Provided LD is correctly accounted for, the inbreeding estimates were very similar using the different SNP panels. The HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected inbreeding up to second-cousin offspring. We applied the best method on release 3 of the HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release. Conclusions: We recommend using HMMs on multiple sparse maps to estimate and detect inbreeding in large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500-kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH (Gujarati Indians from Houston in Texas) population.

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The alkaline comet assay, or single cell gel electrophoresis, is one of the most popular methods for assessing DNA damage in human population. One of the open issues concerning this assay is the identification of those factors that can explain the large inter-individual and inter-laboratory variation. International collaborative initiatives such as the hCOMET project - a COST Action launched in 2016 - represent a valuable tool to meet this challenge. The aims of hCOMET were to establish reference values for the level of DNA damage in humans, to investigate the effect of host factors, lifestyle and exposure to genotoxic agents, and to compare different sources of assay variability. A database of 19,320 subjects was generated, pooling data from 105 studies run by 44 laboratories in 26 countries between 1999 and 2019. A mixed random effect log-linear model, in parallel with a classic meta-analysis, was applied to take into account the extensive heterogeneity of data, due to descriptor, specimen and protocol variability. As a result of this analysis interquartile intervals of DNA strand breaks (which includes alkali-labile sites) were reported for tail intensity, tail length, and tail moment (comet assay descriptors). A small variation by age was reported in some datasets, suggesting higher DNA damage in oldest age-classes, while no effect could be shown for sex or smoking habit, although the lack of data on heavy smokers has still to be considered. Finally, highly significant differences in DNA damage were found for most exposures investigated in specific studies. In conclusion, these data, which confirm that DNA damage measured by the comet assay is an excellent biomarker of exposure in several conditions, may contribute to improving the quality of study design and to the standardization of results of the comet assay in human populations.