To close, I’ll highlight is that our model gives a general prediction of germline mutation rate as a function of generation time. When trio-based estimates aren’t available, this model may perform better than old phylogenetic estimates. www.nature.com/articles/nrg...

Consistent with prior phasing results, we estimated that most species have a higher paternal than maternal mutation rate. However, aye-aye was the exception, corroborating the recent finding of extraordinary maternal bias in the offspring of aged aye-aye mothers! journals.plos.org/plosbiology/...

To explain these results, we show that selection against a mutator allele increasing the mutation rate per year in the germ cells is likely strongest in species with long generation times, since the allele will have more time to create extra mutations during each generation.

Consistent with prior work, the mutation rate per generation is highest in long-lived species with small effective population sizes. However, the opposite is true of the mutation rate per year in the germ cell lineages after puberty! (aye-aye outlier discussed more later in this thread)

We meta-analyzed variation in germline mutation rates among species and reproductive ages in order to disentangle changes molecular parameters like DNA repair from variation caused by demographic parameters like spermatogonial and oocyte aging.

Species with long lifespans tend to have low effective population sizes because they reproduce slowly and use many environmental resources. This could lessen the effectiveness of weak selection to avoid a small number of germline mutations per generation. www.annualreviews.org/content/jour...

We found that PolE hypermutation is most common in endometrial and colorectal tumors from people of East Asian descent, lung cancers from African Americans have more tobacco-associated mutations, and melanomas from Europeans have a higher UV-associated mutation load.

Next, we tested whether genetic ancestry was associated with human tumor mutation spectra. The AMSD revealed a suite of associations between ancestry and mutation load, which could reflect both genetic differences in cancer susceptibility and social determinants of health.

We first used the AMSD to reassess a previous study reporting that many carcinogens failed to cause distinctive mutational signatures in a mouse experiment. We found that many of these carcinogens still significantly perturbed the composition of the mutation spectrum.

We now generalize the AMSD to test which exposures appear to perturb tumor mutation spectra. If this test finds that two groups of tumors have statistically indistinguishable mutation spectra, that saves us overintepreting small differences in their mutational signature profiles.

We previously developed the AMSD with Tom Sasani and @aaronquinlan to test whether two groups of sequences have significantly different mutation spectra. We successfully used it to identify a germline mutator allele in the BXD mice: elifesciences.org/articles/89096

Mutational signature analysis is a very powerful tool for decomposing tumor mutation burdens into different endogenous and exogenous exposures, but when underlying mutation counts are sparse, it is not easy to tell which differences between signature profiles are meaningful.