Materials and Methods All Python and R code used in these analyses is available at https://github.com/harrispopgen/bxd_mutator_manuscript. We used snakemake (39) to write a collection of pipelines that can be used to reproduce all analyses described in the manuscript.

Construction of the BXD RILs Detailed descriptions of the BXD dataset, including the construction of the BXD recombinant inbred lines, can be found in (15). Briefly, the BXD RILs were derived from crosses of the DBA/2J and C57BL/6J inbred laboratory strains initiated in six distinct "epochs" from 1971 to 2014. RILs were produced using one of two strategies: four epochs were produced using the standard F2 cross, and two were produced using the advanced intercross strategy. In the F2 cross design, a male DBA/2J mouse is crossed to a C57BL/6J female to produce F1 animals that are heterozygous for parental ancestry at every position in the genome. Pairs of these F1 animals are then crossed to produce F2s. To generate each individual recombinant inbred line, a brother and sister are picked from among the F2s and mated; this brother-sister mating strategy continues for many generations. In the AIL cross design, F2s are generated as in the standard F2 cross. However, pseudo-random pairs of F2 animals are then crossed to generate F3s, pseudo-random pairs of F3s are crossed to generate F4s, and so on, for up to 14 generations. Then, to generate inbred lines, brother-sister matings are once again initiated. Schematic diagrams of the F2 cross and AIL strategies are shown in Figure S1.

Whole-genome sequencing, alignment, and variant calling BXD mice (all males) were euthanized using isoflurane. Tissue (spleen) was collected immediately, flash frozen with liquid nitrogen, and placed in a -80 degree freezer for subsequent analysis. All DNA extraction, library preparations and sequencing was carried out by HudsonAlpha (Huntsville, AL, USA). High molecular weight genomic DNA was isolated from 50 to 80 mg of spleen tissue using the Qiagen MagAttract kit (Qiagen, Germantown, MD). The Chromium Gel Bead and Library Kit (v2 HT kit, revision A; 10X Genomics, Pleasanton, CA, USA) and the Chromium instrument (10X Genomics) were used to prepare libraries for sequencing; barcoded libraries were then sequenced on the Illumina HiSeq X10 system. FASTQ files were aligned to the mm10/GRCm38 reference genome using the 10X LongRanger software (v2.1.6), using the "Lariat" alignment approach. Variant calling was carried out on aligned BAM files using GATK version v3.8-1-0-gf15c1c3ef (40) to generate gVCF files; these gVCFs were then joint-called to produce a complete VCF file containing variant calls for all BXDs and founders. GATK Variant Quality Score Recalibration (VQSR) was then applied to the joint-called VCF. A list of known, "true-positive" variants was created by identifying variants which were shared across three distinct callsets: 1) variants identified in DBA/2J in this study 2) variants identified in DBA/2J in (41) and 3) variants identified in DBA/2J in the Sanger Mouse Genomes Project (33). This generated a set of 3,972,727 SNPs, 404,349 deletions and 365,435 insertions; we were highly confident that these varied between the DBA/2J and reference sequences and expected that each should appear in ~50% of the BXD strains. The SNP and indel variant calls from the Sanger Mouse Genomes project (33) were also used as a training resource for VQSR.

Identifying homozygous singleton variants in the BXD RILs To confidently identify singletons (sites with a derived allele in exactly one of the BXD recombinant inbred lines) we iterated over all autosomal variants in the joint-genotyped VCF using cyvcf2 (42) and identified variants that passed the following filters: first, we removed all variants that overlapped segmental duplications or simple repeat annotations in mm10/GRCm38, which were downloaded from the UCSC Genome Browser. We limited our analysis to single nucleotide variation and did not include any small insertion or deletion variants. At each site, we required both founder genotypes (DBA/2J and C57BL/6J) to be homozygous for the reference allele, for each of these genotypes to be supported by at least 10 sequencing reads, and for Phred-scaled genotype qualities in both founders to be at least 20. We then required that exactly one of the BXD RILs had a heterozygous or homozygous alternate genotype at the site; although we only included 94 BXDs in downstream analyses (see section entitled "Criteria for excluding BXD RILs in downstream analyses"), the genome sequences of all sequenced BXDs (except for the small number of BXDs that were isogenic with another line) were considered when identifying potential singletons to ensure that none of the excluded strains possessed a putative singleton allele present in the focal strain. To account for possible genotyping or sequencing errors, we considered heterozygous genotypes with very high allele balance (i.e., fraction of reads supporting the alternate allele >= 0.9) to be effectively homozygous. For candidate heterozygous and homozygous singletons, we required the genotype call to be supported by at least 10 sequencing reads and have Phred-scaled genotype quality at least 20. Finally, we confirmed that at least one other BXD shared a parental haplotype identical-by-descent with the focal strain (i.e., the strain with the putative singleton) at the singleton site but was homozygous for the reference allele at that site. We additionally annotated the full autosomal BXD VCF with SnpEff (20) version 4.3t, using the GRCm38.86 database and the following command: java -Xmx16g -jar /path/to/snpeff/jarfile GRCm38.86 /path/to/bxd/vcf > /path/to/uncompressed/output/vcf

Identifying haplotypes shared IBD between the BXD RILs and the parental DBA/2J and C57BL/6J strains For each in each recombinant inbred line, we used a Hidden Markov Model (HMM) to identify haplotype tracts inherited IBD from each of the two founder lines. First, we iterated over every variant in the joint-genotyped BXD VCF file; we only considered sites where one of the two founder strains (either DBA/2J or C57BL/6J) was homozygous for the alternate allele, and the other strain was homozygous for the reference allele. (Since the reference mouse sequence mm10 is largely derived from the C57BL/6J strain, this was nearly always tantamount to requiring DBA/2J to be homozygous for the alternate allele, except for the rare sites where the BXD founder diverged from the C57BL/6J individual used to generate the mouse reference genome). We further required the genotype of each founder to be supported by at least 10 sequencing reads, and the Phred-scaled genotype quality of each founder genotype to be at least 20. We assumed that these sites represented fixed differences between DBA/2J and C57BL/6J. By comparing the genotypes of all BXD RILs to the two founder genotypes at each of these informative fixed differences, we then identified tracts of sites in which RIL genotypes consistently matched one of the two founders. For each chromosome in each BXD RIL, we constructed an array of length S, where S was the number of fixed differences between C57BL/6J and DBA/2J across that chromosome. Each element i in this array took one of three values: 0 (if the genotype of the RIL at site Si did not match the DBA/2J genotype), 1 (if the genotype of the RIL at site Si matched the DBA/2J genotype), or -1 (if the genotype of the RIL at site Si was unknown, heterozygous, supported by fewer than 10 reads, or had a Phred-scaled genotype quality < 20). We then built an HMM using pomegranate (43) with two states (DBA/2J or C57BL/6J, state and transition probabilities defined in associated code), and inferred long sequences of identical states. These sequences likely represent regions of the genome shared IBD with one of the two founders.

Annotating singletons with triplet sequence contexts and conservation scores For each candidate singleton variant, we were interested in characterizing the 5' and 3' sequence context of the mutation, as well as the phastCons conservation score of the nucleotide at which the variant occurred. To determine the sequence context of each variant, we used the mutyper API (44). mutyper ingests a reference sequence and information about a particular variant, and determines the ancestral and derived alleles, as well as the nucleotides that flank that variant. To annotate each variant with its phastCons score, we downloaded phastCons scores derived from a 60-way placental mammal alignment for the mm10/GRCm38 genome build in WIG format from the UCSC Table Browser. We then converted the WIG files to BED format using bedops (45), compressed the BED format files with bgzip, and indexed the compressed BED files with tabix. Within the Python script used to identify singletons, we then used pytabix (https://github.com/slowkow/pytabix) to query the phastCons BED files at each putative singleton.

Identifying fixed variants for comparison to singletons We aimed to compare the distribution of phastCons conservation probabilities in the BXD singletons to a "control" set of variants. We defined these control variants as mutations present in one of the two founder genomes (which passed the same filtering criteria required of singletons), and present in all BXDs that inherited the founder haplotype with the mutation at that site. We expected that these "fixed" variants had occurred well before the construction of the BXDs in DBA/2J or C57BL/6J founder stocks. To ensure that we sampled control variants from the full length of the reference genome sequence, we first used bedtools (46) to generate 50-kbp windows across the mm10/GRCm38 reference genome. Within each of these windows, we then used the first 5 mutations in the interval that were fixed in a founder genome.

Adjusting singleton counts by the duration of inbreeding in each strain We downloaded a file containing BXD metadata (strain names, provenance, etc.) from (15) (available as SuppTable1.xlsx). A simplified version of this file, containing only the metadata necessary to reproduce the analyses in this manuscript, is provided in the GitHub repository above. In this file, each BXD strain is annotated with its "Generation at sequencing," using the Jackson Laboratories Generation Definitions syntax. For example, a strain that was inbred for 25 generations at its original location and subsequently inbred for 20 generations after arriving at the Jackson Laboratories would be annotated as "F25+F20." To assign an "age" in generations to each BXD RIL, we simply summed the number of total generations each strain had been inbred; in the previous example, the strain's age would be 45 total generations. To estimate each strain’s mutation rate per genome per generation, we used a previously described approach that assumes perfect full-sibling mating during inbreeding (17). Briefly, we divided the count of homozygous singletons in each mouse by the number of generations in which de novo germline mutations could have occurred in the strain, and by the number of haploid base pairs that were callable (i.e., were covered by at least 10 sequencing reads in the strain, and not overlapping segmental duplications or simple repeats). To calculate the callable number of base pairs, we used mosdepth (47) to count the number of base pairs with at least 10 aligned sequencing reads (of at least quality score 20) in each BXD RIL's BAM file with the following command: mosdepth -n -x -b 1000000 -T 10 -Q 20 $PREFIX $BAM

We then used the files produced by this command ($sample_name.thresholds.bed) to count the total number of autosomal base pairs covered by at least 10 high-quality sequencing reads that did not overlap segmental duplications or simple repeat tracks from the UCSC Genome Browser. Threshold files for each BXD are included at the above GitHub repository.

Criteria for including BXD RILs in downstream analyses In this study, our mutation rate estimates were derived from the cumulative number of homozygous singletons that had accumulated in each strain between its founding and its sequencing. During inbreeding, each generation of brother-sister mating halves the expected heterozygosity of an RIL; a line is predicted to be 99.8% homozygous after 20 generations of inbreeding (48, 49). Thus, if a mutator locus were responsible for influencing the mutation rate or spectrum, that locus would have greater than a 99% chance of being fixed or lost after 20 generations. Moreover, each generation of inbreeding provides an opportunity for heterozygous de novo germline mutations to be either fixed or lost. To ensure that any potential mutator loci had been homozygous for a sufficient number of generations to influence the mutation rate or spectrum in each RIL, and that the strains we analyzed had been inbred for long enough to accumulate a substantial number of homozygous de novo mutations, we removed BXD RILs that had been inbred for fewer than 20 generations from our analyses. This effectively removed all RILs from epoch 6, the most recent epoch of the BXD. Also, during construction of the BXDs, 21 RILs were backcrossed to either an inbred DBA/2J or C57BL/6J animal at some point during inbreeding, usually in order to rescue those RILs in cases of severe inbreeding depression (15). During each generation of backcrossing, a RIL could accumulate new mutations from either the DBA/2J or C57BL/6J parent, as well as from the RIL parent. Additionally, assuming a strain had been inbred prior to backcrossing, each generation of backcrossing is expected to remove half of any existing singleton variants that had accumulated during the prior period of inbreeding in that line. Therefore, to avoid the complexity of accounting for founder-derived singleton mutations and the loss of existing singletons in our estimates of generation times, we removed BXD RILs that had undergone backcrossing from our QTL analyses. Finally, a small number of BXD RILs are nearly or completely isogenic (if two mice from the same line were sequenced, or if the same animal was sequenced twice); these strains were not included in any analyses.

Quantitative trait locus mapping We used the R/qtl2 software (19) for QTL mapping in this study. Prior to running QTL scans, we downloaded a number of data files from the R/qtl2 data repository (https://github.com/rqtl/qtl2data), including physical (Mbp) and genetic (cM) maps of the 7,320 genotype markers used for QTL mapping, as well as a file containing genotypes for all BXDs at each of these markers. We inserted pseudo-markers into the genetic map using insert_pseudomarkers and calculated genotype probabilities at each marker using calc_genoprob, with an expected error probability of 0.002. We additionally constructed a kinship matrix describing the relatedness of all strains used for QTL mapping using the leave-one-chromosome-out (LOCO) method. We then performed a genome scan using a linear mixed model (scan1 in R/qtl2), including the kinship matrix, a covariate for the X chromosome, and two additive covariates. The first covariate denoted the number of generations each RIL was intercrossed prior to inbreeding (0 for strains derived from standard F2 crosses, and N for strains derived from advanced intercross, where N is the number of generations of pseudo-random crosses performed before the start of inbreeding in order to reduce the length of parental ancestry tracts), and the second covariate denoted the epoch from which the strain was derived. To assess the significance of any log-odds peaks, we performed a permutation test (1,000 permutations) with scan1perm, using the same covariates and kinship matrix as described above. We calculated the Bayes 95% credible intervals of all peaks using the bayes_int function, with prob=0.95. When performing QTL scans for a particular mutation fraction, we treated the phenotype as the centered log-ratio transform of the fraction of singletons of that type in each strain. When performing scans for mutation rates, we used the untransformed mutation rates (per per generation) as the phenotype values.

Fine-mapping the QTL to a specific -coding To narrow our search for sequence variation underlying the C>A QTL on chromosome 4, we first used the Mouse Genome Informatics (MGI) query tool to identify any protein-coding within the QTL. Using the "Genes and Markers Query Form" (http://www.informatics.jax.org/marker), we searched for protein-coding genes within the Bayes 95% credible QTL interval on chromosome 4 (from 114.8 - 118.3 Mbp). A total of 76 protein- coding genes overlapped the QTL. We then used the MGI "Batch Query" tool to determine which of these genes was associated with the following terms: "DNA replication, "cellular response to DNA damage." We then asked if any of the protein-coding genes within the QTL interval contained sequence differences between DBA/2J and C57BL/6J. We iterated over every variant within the QTL interval that was homozygous for the alternate allele (or heterozygous, with >= 0.9 allele balance) in either DBA/2J or C57BL/6J and homozygous for the reference allele in the other strain; we then examined the SnpEff annotations associated with each of these variants. Importantly, SnpEff may add more than one annotation to a variant if that variant affects more than one transcript sequence. For each high-quality fixed difference between DBA/2J and C57BL/6J, we asked if the variant was annotated as having MODERATE or HIGH impact on any of the transcript sequences reported in the "ANN" entry added to the INFO field of the variant. We considered any variant with at least one MODERATE or HIGH-impact annotation to be of interest. According to SnpEff, each of the five Mutyh missense mutations we discovered in the BXD affects more than one Mutyh transcript; however, the effect of each mutation on the MUTYH protein sequence is the same, regardless of the transcript used by SnpEff.

Identifying correlations between QTL markers and the expression of other genes We used the GeneNetwork online resource (22) to ask if the QTL for the C>A mutation rate on chromosome 4 could be a result of the QTL harboring cis or trans expression quantitative trait loci (eQTLs) for the expression of genes involved in DNA repair or genome integrity. Specifically, we searched for associations between the top SNP marker (i.e., the marker with highest LOD score) from the C>A QTL interval on chromosome 4 (rs52263933) and the expression of protein-coding genes. On the GeneNetwork website (genenetwork.org), we searched the "BXD Genotypes" dataset for rs52263933, after selecting the "DNA Markers and SNPs" type and the "BXD family" group. Using the Trait Data and Analysis page for rs52263933, we then calculated correlations between the marker and expression datasets from a variety of tissues. We limited the results to include the top 100 genes for which expression was correlated with rs52263933, and used "Sample r" as the correlation method. To better understand the relevance of these 100 genes to the C>A QTL, we then entered all of the gene IDs from the correlation results into the Mouse Genome Informatics "Batch Query Tool," and annotated the IDs with Gene Ontology terms. We then searched for genes involved in "cellular response to DNA damage," "DNA repair," or "cellular response to oxidative stress." For each tissue, protein-coding genes that a) had expression values that were significantly (p < 0.05) correlated with BXD genotypes at rs52263933 and b) were annotated with one of the three aforementioned Gene Ontology terms are listed below.

● Amygdala: INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA ○ Nsmce3 ○ Fnip2 ○ Scly ● Hematopoietic Stem Cells: UMCG Stem Cells ILM6v1.1 (Apr09) original ○ Mutyh ○ Nipbl ○ Trp53bp1 ● Kidney: Mouse kidney M430v2 Sex Balanced (Aug06) RMA ○ Prpf19 ○ Helq ○ Mpg ○ Rad51d ○ Immp2l ○ Hipk2 ● Liver: UTHSC BXD Liver RNA-Seq Avg (Oct19) TPM Log2 ○ Mutyh ● Retina: DoD Retina Normal Affy MoGene 2.0 ST (May15) RMA Gene Level ○ Smc6 ● Spleen: UTHSC Affy MoGene 1.0 ST Spleen (Dec10) RMA Exon Level ○ Mutyh

To generate the plots in Figure S4, we downloaded expression data for Mutyh from GeneNetwork in CSV format for each of the six tissue types above. To download the expression data, we queried each of the datasets listed above for the Mutyh gene from the GeneNetwork homepage, by selecting the relevant tissue type in the "Type" dropdown menu, and the relevant data set in the "Dataset" dropdown menu. Downloaded expression data for the six aforementioned tissues are included at the GitHub repository associated with the manuscript.

Comparing mutation spectra in BXDs to COSMIC mutation signatures The Catalogue of Somatic Mutations in Cancer (COSMIC) database reports mutation signatures identified from human tumor sequencing data, which are annotated according to the endogenous or exogenous process that likely generated them. We downloaded mutation signature data for two signatures (SBS18 and SBS36) from the COSMIC web page using the "Download signature in numerical form" button: https://cancer.sanger.ac.uk/cosmic/signatures/SBS/SBS18.tt and https://cancer.sanger.ac.uk/cosmic/signatures/SBS/SBS36.tt. The proposed etiologies for both signatures involve DNA damage by reactive oxygen species, as well as defective base excision repair due to mutations in MUTYH. We then correlated the abundances of each mutation type in either signature with the enrichments of each singleton mutation type observed in BXDs with a D haplotype at the QTL, compared to BXDs with the B haplotype.

Comparing mutation spectra in BXDs to TOY-KO triple knockout germline mutation spectra Exome sequencing was previously performed on a large pedigree of mice with triple knockouts of Mth1, Mutyh , and Ogg1 (in a C57BL/6J background) in order to identify de novo germline mutations in mice lacking base excision repair machinery (23). The authors deposited all 263 de novo germline mutations observed in these mice in Supplementary Data File 1 associated with their manuscript. For each mutation, the authors report the reference and alternate alleles, as well as 50bp of flanking sequence up- and downstream of the mutation. We used this information to construct a 3-mer mutation type (ACA>AAA, ACT>AAT, etc.) for each C>A mutation, and then correlated the fractions of each of the 252 C>A 3-mers with the enrichments of 3-mer C>A mutation types observed in BXDs with a D haplotype at the QTL, compared to BXDs with the B haplotype.

Comparing mutation spectra between groups of Mouse Genomes Project strains Strain-private substitutions were previously identified in 29 whole-genome sequenced inbred strains, which likely represented recent de novo germline mutations that occurred in breeding colonies of these strains (16). To compare the mutation spectra between various groups of these strains (grouped by the number of Mutyh missense mutations they possessed), we first downloaded Supplementary Data File 1 from (16). We used data from Table S3, which includes both the counts of each mutation type in each strain, as well as the total number of A, T, C, and G base pairs that passed filtering criteria in each strain. Assuming we were comparing the spectra between group "A" and group "B," for each mutation type we summed the total number of mutations of that type in group "A" and in group "B," and we collapsed strand complements in our counts (i.e., C>T and G>A are considered to be the same mutation type). We then summed the total number of callable base pairs corresponding to the reference nucleotide and its complement in group "A" and group "B." As an example, if we were comparing the counts of C>T mutations between two groups, we summed the counts of callable "C" and "G" nucleotides in each group. We then adjusted the counts of each mutation type in either group "A" or "B" as follows. If the number of callable base pairs was larger in group "B," we calculated the ratio of callable base pairs between "A" and "B." We expected that if there were more callable base pairs in a group, then the number of mutations observed in that group might be higher simply by virtue of there being more mutable nucleotides. Therefore, we then multiplied this ratio by the count of mutations in group "B" in order to scale the "B" mutation count down. If the number of callable base pairs were higher in "A," we performed the same scaling to the counts of mutations in "A." For each mutation type i, we then performed a Chi- square test of independence using a contingency table of four values: 1) the scaled count of mutations of type i in group "A", 2) the scaled count of singleton mutations of type i in group "B", 3) the sum of scaled counts of mutations not of type i in group "A", 4) the sum of scaled counts of mutations not of type i in group "B."

Generating mutation spectra from wild mouse genomes To identify singleton variants within each wild Mus subspecies, we analyzed a VCF file containing variant calls for 67 wild mice from four Mus species from (31). We iterated over all autosomal variants in the VCF and limited our search to single-nucleotide variants. For each variant, we examined each subspecies (domesticus, castaneus, musculus, or spretus) separately, and considered singleton variants to be mutations present in only one sequenced sample from a particular subspecies. As many of the wild mice are naturally inbred, we allowed singletons to be heterozygous or homozygous in a sample. However, because we examined each subspecies separately, we included singletons that were observed across subspecies. For example, if one of the 27 domesticus samples had a variant at a particular site, and one of the 22 musculus samples had the same variant, we considered the variant to be a singleton in both subspecies (and assumed that the singleton occurred independently in both subspecies). We required singletons to be supported by at least 10 sequencing reads, to have a Phred-scaled genotype quality of at least 20, and for heterozygous singletons to have an allele balance (fraction of reads supporting the ALT allele) between 0.25 and 0.75. We excluded singletons that were observed within either segmental duplications or simple repeats (downloaded from the UCSC Table Browser in mm10 coordinates), and singletons that occurred at nucleotides with a phastCons probability of conservation > 0.05.

Generating phylogenetic comparisons of MUTYH protein sequences We constructed an alignment of amino acid sequences for the 9 species shown in Figure 5 using the Constraint-based Multiple Alignment Tool (COBALT) and the following NCBI accessions: Mus musculus (XP_006503455.1), Rattus norvegicus (XP_038965128.1), Homo sapiens (XP_011539799.1), Pan troglodytes (XP_009454580.1), Mus caroli (XP_029332110.1), Mus pahari (XP_029395766.1), Gallus gallus (XP_004936806.2), Xenopus tropicalis (NP_001072831.1), and Danio rerio (XP_686698.2). We then downloaded the phylogenetic tree generated by COBALT.

Figure S1: Cross design for BXD RIL construction (a) BXDs derived from F2 crosses were subject to many generations of brother-sister mating in order to generate inbred RILs. (b) To generate advanced intercross lines (AILs), pseudo-random pairs of F2 animals were crossed for N generations, and then subject to many generations of brother-sister mating to generate inbred RILs.

Figure S2: Singletons are enriched in highly conserved regions of the genome Distributions of phastCons conservation probabilities in either singletons (n = 47,659) or "fixed" variants (n = 81,186). The latter were present in a founder genome and inherited by all BXDs with the founder's haplotype at that site. P-value of Kolmogorov-Smirnov test comparing distributions of phastCons scores is 3.8 x 10-53.

Figure S3: Results of QTL scans for other mutation rate phenotypes a) Using the same strains and covariates as described in the Materials and Methods, we performed a QTL scan for the overall mutation rate of each strain. The green dashed line indicates the genome-wide significance threshold using 1,000 permutations (alpha = 0.05/15). b) Using the same strains and covariates as described in the Materials and Methods, we performed QTL scans for the rates and fractions of all mutation types other than C>A. Green and blue dashed lines indicate the genome-wide significance thresholds for the rate and fraction scans, respectively, using 1,000 permutations (alpha = 0.05/15).

Figure S4: Expression of Mutyh is higher in BXDs with B haplotypes at the peak QTL SNP in certain tissues Expression measurements for spleen and amygdala are from Affymetrix Mouse Gene 1.0 ST arrays. Measurements for kidney are from Affymetrix Mouse Genome 430 2.0 arrays. Measurements for retina are from Affymetrix Mouse Gene 2.0 ST arrays. Measurements for hematopoietic stem cells are from Illumina Mouse WG-6 v1 arrays. For all tissues except for liver, array expression data are log-transformed and Z-score normalized, such that data are scaled to have a mean of 8 and a standard deviation of 2 (sometimes referred to as 2Z + 8 normalization). Measurements for liver are from RNA-sequencing, and units are in log-2 transcripts per million (TPM).

Figure S5: Similarity of 3-mer mutation sequence contexts between BXD C>A enrichments and MUTYH-associated COSMIC cancer signatures (a) Log-2 ratios of 3-mer mutation fractions in BXD strains with D vs. B haplotypes at the QTL on chromosome 4, correlated with the abundance of each 3-mer mutation type in the SBS36 COSMIC mutation signature. Mutation types significantly enriched in BXDs with D haplotypes are outlined in black and labeled. (b) Same as in (a), but correlated with the SBS18 COSMIC mutation signature.

Figure S6: Similarity of 3-mer mutation sequence contexts between BXD68 and other MUTYH- associated mutation signatures (a) Fractions of all 3-mer singleton mutation types in BXD68, correlated with the abundance of each 3-mer mutation type in the SBS36 COSMIC mutation signature. (b) Same as in (a), but correlated with the SBS18 COSMIC mutation signature. (c) Fractions of 3-mer C>A singleton mutation types in BXD68, correlated with the abundance of each 3-mer C>A mutation type in the TOY-KO germline mutation dataset from (Ohno et al. 2014). (d) Fractions of 3-mer singleton mutation types in BXD68, correlated with the log-2 ratios of 3-mer mutation fractions in BXD strains with D vs. B haplotypes at the QTL on chromosome 4. Mutation types significantly enriched in BXDs with D haplotypes are outlined in black and labeled.

Figure S7: 3-mer mutation sequence contexts enriched in DBA/2J-like Mouse Genomes Project strains Log-2 ratios of 3-mer singleton mutation fractions in BXD strains with D vs. B haplotypes at the QTL on chromosome 4, correlated with the log-2 ratios of 3-mer strain-private mutation fractions in Sanger Mouse Genomes Project strains that are DBA-like vs. C57-like. Mutation types significantly enriched in BXDs with D haplotypes are outlined in black and labeled.

Table S1: BXD RILs used in this analysis

Epoch Year founded Cross strategy Strains with Strains in this WGS analysis

1 1971 F2 25 21

2 1990s (approx.) F2 7 7

3 late 1990s (approx.) Advanced intercross 49 38

4 2008 F2 23 16

5 2010 Advanced intercross 19 12

6 2014 F2 30 0

Total - - 153 94

Table S2: GeneNetwork IDs of mutagenesis-related phenotypes that were used for QTL scans

Phenotype GeneNetwork ID

C>A singleton fraction BXD_24430

C>T singleton fraction BXD_24431

C>G singleton fraction BXD_24432

A>C singleton fraction BXD_24433

A>T singleton fraction BXD_24434

A>G singleton fraction BXD_24435

CpG>TpG singleton fraction BXD_24436

C>A mutation rate (per base pair, per generation) BXD_24437

C>T mutation rate (per base pair, per generation) BXD_24438

C>G mutation rate (per base pair, per generation) BXD_24439

A>C mutation rate (per base pair, per generation) BXD_24440

A>T mutation rate (per base pair, per generation) BXD_24441

A>G mutation rate (per base pair, per generation) BXD_24442

CpG>TpG mutation rate (per base pair, per BXD_24443 generation)

Overall mutation rate (per base pair, per generation) BXD_24444