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Cell Phone Dealer's Group

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Michael Flores
Michael Flores

100k De.txt [VERIFIED]

As a case in point, the NCSC Top 100k list did not flag passwords I had assigned to a handful of test accounts. Specops Password Auditor however did flag these accounts for identical passwords as well as breached passwords. Note the checks in the environment, including:

100k de.txt

Germline hypermutation accounted for 7% of the variance in the germline-mutation rate in the 100kGP cohort. The ascertainment in this cohort for rare genetic diseases probably means that individuals with germline hypermutation are enriched relative to the general population. As a consequence, our estimate of the contribution of germline hypermutation is probably inflated. However, the absolute risk of an individual with a hypermutated germline having a child with a genetic disease is low. The population average risk for having a child with a severe developmental disorder caused by a DNM has been estimated to be 1 in 300 births12 and so a fourfold increase in DNMs in a child would increase this absolute risk to just over 1%. Thus, most individuals with germline hypermutation will not have a genetic disease, and germline hypermutation should also be observed in healthy individuals.

Unexplained hypermutation and additional variance in the germline-mutation rate might be explained by other environmental exposures. One limitation of this study was the lack of data on non-therapeutic environmental exposures. Reassuringly, the narrow distribution of DNMs per individual in the 100kGP cohort suggests that it is unlikely that there are common environmental mutagen exposures in the UK (such as cigarette smoking) that cause a substantive (for example, >1.5 times) fold increase in mutation rates and concomitant disease risk. The germline generally appears to be well protected from large increases in mutation rate. However, including a broader spectrum of environmental exposures in future studies would help to identify more subtle effects and may reveal gene-by-environment interactions.

Mutational signatures were extracted from maternally and paternally phased autosomal DNMs, 24 controls (randomly selected), 25 individuals (father with a cancer diagnosis before conception), 27 individuals (mother with a cancer diagnosis before conception) and 12 individuals with hypermutation that we identified. All DNMs were lifted over to GRCh37 before signature extraction (100kGP samples are a mix of GRCh37 and GRCh38) and, through the liftover process, a small number of 100kGP DNMs were lost (0.09% overall, 2 DNMs were lost across all of the individuals with hypermutation). The mutation counts for all of the samples are shown in Supplementary Table 1. This was performed using SigProfiler (v.1.0.17) and these signatures were extracted and subsequently mapped on to COSMIC mutational signatures (COSMIC v.91, Mutational Signature v.3.1)19,40. SigProfiler defaults to selecting a solution with higher specificity than sensitivity. A solution with 4 de novo signatures was chosen as optimal by SigProfiler for the 12 individuals with germline-hypermutated genomes. Another stable solution with five de novo signatures was also manually deconvoluted, which has been considered as the final solution. The mutation probability for mutational signature SBSHYP is shown in Supplementary Table 3.

To estimate the fraction of germline mutation variance explained by several factors, we fit the following negative binomial GLMs with an identity link. Data quality is likely to correlate with the number of DNMs detected so, to reduce this variation, we used a subset of the 100kGP dataset that had been filtered on some base quality control metrics by the Bioinformatics team at GEL:

(a) Paternal and maternal age against the number of dnInDels. (b) Paternal age against number of paternally phased dnSNVs and maternal age against number of maternally phased dnSNVs. Hypermutated individuals are highlighted in pink (11 individuals in 100kGP) and blue (DDD individuals). 041b061a72


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