Signals of variation in human mutation rate at multiple levels of sequence context

Aikens, Rachael C.; Johnson, Klein; Voight, Benjamin F.

doi:10.1101/385096

Cited by 10 publications

(16 citation statements)

References 17 publications

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“…Another signature, *AC → *CC, has been observed at higher frequency in East Asians compared to Africans in the 1kGP and the SGDP (Aikens et al, 2019;Harris and Pritchard, 2017). These two studies also found heterogeneous frequency of this signature among 1kGP Japanese individuals.…”

Section: Mutational Signaturesmentioning

confidence: 59%

“…Strong population enrichments of a mutational signature suggests important genetic or environmental differences in the history of each population (Harris, 2015;Harris and Pritchard, 2017). Harris and Pritchard further identified distinct mutational spectra across a range of populations, which were further examined in a recent publication by Aikens et al (Harris and Pritchard, 2017;Aikens et al, 2019).…”

Section: Mutational Signaturesmentioning

confidence: 99%

“…A systematic difference within the Japanese population would suggest sustained environmental or genetic differences across sub-populations within Japan with little to no gene flow. This observation could not be reproduced in SGDP due to the small number of Japanese samples (Aikens et al, 2019). We therefore decided to follow up on this observation by using a newly sequenced dataset of Japanese individuals from Nagahama.…”

Section: Mutational Signaturesmentioning

confidence: 99%

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Legacy Data Confounds Genomics Studies

Anderson-Trocmé

Farouni

Bourgey

et al. 2019

Preprint

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Recent reports have identified differences in the mutational spectra across human 13 populations. While some of these reports have been replicated in other cohorts, most have been 14 reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative 15 population stratification within the Japanese population, we identified a previously unreported 16 batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population 17 stratification. Because the 1kGP data is used extensively, we find that the batch effects also lead to 18 incorrect imputation by leading imputation servers and suspicious GWAS associations. 19 Lower-quality data from the early phases of the 1kGP thus continues to contaminate modern 20 studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data. 21 Cohorts, Imputation 23 24 29 Consortium et al., 2015), and the Simons Diversity project (Mallick et al., 2016), for example, have 30 made thousands of genome sequences publicly available for population and medical genetic analy-31 ses. Many more genomes are available indirectly through servers providing imputation services 32 (McCarthy et al., 2016) or summary statistics for variant frequency estimation (Lek et al., 2016). 33 The first genomes in the 1kGP were sequenced 10 years ago (van Dijk et al., 2014). Since 34 then, sequencing platforms have rapidly improved. The second phase of the 1kGP implemented 35 multiple technological and analytical improvements over its earlier phases (1000 Genomes Project 36 Consortium, 2012; Consortium et al., 2015), leading to heterogeneous sample preparations and 37 data quality over the course of the project. 38 Yet, because of the extraordinary value of freely available data, early data from the 1kGP is still 39 widely used to impute untyped variants, to estimate allele frequencies, and to answer a wide range 40 1 of 24 of medical and evolutionary questions. This raises the question of whether and how such legacy 41 data should be included in contemporary analyses alongside more recent cohorts. Here we point 42 out how large and previously unreported batch effects in the early phases of the 1kGP still lead to 43 incorrect genetic conclusions through population genetic analyses and spurious GWAS associations 44 as a result of imputation using the 1kGP as a reference. 45

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Section: Mutational Signaturesmentioning

confidence: 59%

Section: Mutational Signaturesmentioning

confidence: 99%

Section: Mutational Signaturesmentioning

confidence: 99%

See 1 more Smart Citation

Legacy Data Confounds Genomics Studies

Anderson-Trocmé

Farouni

Bourgey

et al. 2019

Preprint

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“…Direct sequence data from plants and insect also reveal a mutagenic effect of heterozygosity (32). Finally, different three-base combinations show markedly different mutation probabilities in different human populations (34,35) an observation that has been extended more generally to sequence context (36). That the types of mutations differ so strongly between human populations lends credence to the idea that the overall rate might also vary.…”

Section: Introductionmentioning

confidence: 84%

Differential base-sharing between humans and Neanderthals: inter-breeding or greater mutability in heterozygotes?

Amos

2019

Preprint

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The idea that humans interbred with other Hominins, most notably Neanderthals, is now accepted as fact. The finding of hybrid skeletons shows that fertile matings did occur. However, inferences about the size of the resulting legacy assume that back-mutations are rare enough to be ignored and that mutation rate does not vary. In reality, back-mutations are common, mutation rate does vary between populations and there is mounting evidence that heterozygosity and mutation rate covary. If so, the large loss of heterozygosity that occurred when humans migrated out of Africa would have reduced the mutation rate, leaving Africans to diverge faster from our common ancestor and from related lineages like Neanderthals. To test whether this idea impacts estimates of introgressed fraction, I calculated D, a measure of relative base-sharing with Neanderthals, and heterozygosity difference between all pairwise combinations of populations in the 1000 genomes Phase 3 data. D and heterozygosity difference are ubiquitously negatively correlated across all comparisons, between all regions and even between populations within each major region including Africa. In addition, the larger sample of populations in the Simons Genome Diversity project reveals a pan-Eurasian correlation between Neanderthal and Denisovan fraction. These correlations challenge a simple hybridisation model but do seem consistent with a model where more heterozygous human populations tend to diverge faster from Neanderthals than populations with lower heterozygosity. Indeed, the strongest correlation between Neanderthal content and geography indicates and origin where humans likely left Africa, exactly mimicking the pattern seen for loss of heterozygosity. Such a model explains why evidence for inter-breeding is found more or less wherever archaic and human populations are compared. How much of variation in D is due to introgression and how much is due to heterozygosity-mediated variation in mutation rate remains to be determined. Author summaryThe idea that humans inter-bred with related lineages such as Neanderthals, leaving an appreciable legacy in modern genomes, has rapidly progressed from shocking revelation to accepted dogma. My analysis explores an alternative model in which mutation rate slowed when diversity was lost in a population bottleneck as humans moved out of Africa to colonise the world. I find that, across Eurasia, the size of inferred legacy closely matches the pattern of diversity loss but shows no relationship to where human and Neanderthal populations likely overlapped. My results do not challenge the idea that some inter-breeding occurred, but they do indicate that some, much or even most of the signal that has be attributed entirely to archaic legacies, arises from unexpected variation in mutation rate. More generally, my analysis helps explain why inter-breeding is inferred almost wherever tests are conducted even though most species avoid hybridisation.

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“… Aggarwala and Voight (2016) proposed a statistical model that takes parameters from observed frequency of mutations within 5-mer or 7-mer, while Zhu et al (2017) introduced a log-linear model for mutation frequency analysis that also considers up to 5-mers. It has also been found that variability of mutation in human genome between different populations of human genome are dependent on context beyond the immediate flanking bases ( Aikens et al , 2019 ).…”

Section: Introductionmentioning

confidence: 99%

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Lim

Blanchette

2020

Bioinformatics

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Motivation Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood. Results We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes. Availability and implementation Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM. Supplementary information Supplementary data are available at Bioinformatics online.

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Signals of variation in human mutation rate at multiple levels of sequence context

Cited by 10 publications

References 17 publications

Legacy Data Confounds Genomics Studies

Legacy Data Confounds Genomics Studies

Differential base-sharing between humans and Neanderthals: inter-breeding or greater mutability in heterozygotes?

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

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