Supporting Information

Edwards et al. 10.1073/pnas.1407126111 Note 1 Note 2 RNA sequencing datasets were obtained from the NCBI Se- Original sequencing reads were obtained from the authors of quence Read Archive (SRA) from previous studies that analyzed the study (4). Reads were aligned to the S288c reference Sigma and S288c strains from the G.R.F. laboratory collection genome using the aligner bwa version 0.6.2 (3) and SNPs (1). Reads were aligned to the killer toxin sequences shown were identified with the samtools variant caller version below as obtained from GenBank or from previous studies (2). 0.1.19 following the recommended defaults (5). Allele fre- The aligner bwa (3) (version 0.6.2) was used for this analysis. quencies of the SNPs in the ∼1,000 samples were extracted RPKM values (reads per kilobase of target sequence per million and analyzed by chromosome. Fig. S3A shows a typical chro- mapped reads) are reported for each viral element. The se- mosome where variants from both parental strains are seg- quencing data shown in Table S4 verified the high abundance of the M1 toxin sequence in the Sigma strains. The low amount of regating, leading to allele frequencies centered at 50%. In B M1 toxin observed in S288c could reflect a natively low level of contrast,Fig.S3 shows the allele frequencies along the toxin dsRNA or possible contamination at a later stage of the mitochondrial genome. The identified variants are almost sample preparation or sequencing process. A comparative analysis exclusively fixed at 100%, indicating a lack of variation in the of the two strains confirmed Sigma as a strong killer, as supported samples, consistent with identical alleles originating from the by Petri plate assays and direct dsRNA analysis (Fig. S4). parental strain.

1. Wu C-Y, Rolfe PA, Gifford DK, Fink GR (2010) Control of transcription by cell size. PLoS 4. Bloom JS, Ehrenreich IM, Loo WT, Lite T-LV, Kruglyak L (2013) Finding the sources of Biol 8(11):e1000523. missing heritability in a yeast cross. Nature 494(7436):234–237. 2. Schmitt MJ, Tipper DJ (1995) Sequence of the M28 dsRNA: Preprotoxin is processed to 5. Li H, et al.; 1000 Genome Project Data Processing Subgroup (2009) The Sequence an alpha/beta heterodimeric protein toxin. Virology 213(2):341–351. Alignment/Map format and SAMtools. Bioinformatics 25(16):2078–2079. 3. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760.

Edwards et al. www.pnas.org/cgi/content/short/1407126111 1of8 Fig. S1. Chromosomal variants exhibit a minimal dependence on dsRNA presence in an S288c background. Selected heterozygous gene deletion strains (+/Δ) were analyzed in an S288c [kil-k] background, as opposed to the Sigma [kil-k] strains discussed in the main text or the wild-type S288c [kil-0] background. PHO88 spores were not analyzed owing to technical difficulties in the strain creation process. Each row shows four products from a single meiosis, with the wild-type (+) spores circled and the two spores with the deletion allele (Δ) remaining unmarked. The growth advantage of the wild-type spores is not sig- nificantly different from 1.0 (equal growth) for SKI7, SKI8, and VPS16 (P = 0.12, 0.04, and 0.35, respectively, Mann–Whitney U test). For PEP7 and PEP12,the wild-type spores have a small growth advantage (2.41 and 1.78, respectively) that is more similar to the advantage observed in Sigma kil-0 strains (1.47 and 1.82) than the Sigma kil-k strains (89.35 and 64.67). The growth advantage is calculated as the average over all tetrads of the averaged wild-type colony sizes divided by the averaged knockout colony sizes. This quantity is used as a robust unitless measurement that allows for comparison across multiple plates and imaging batches.

Fig. S2. Example quantitative analysis workflow for nested model comparisons. Three models of increasing complexity are assessed for their ability to predict the observed phenotypes. A comparison of killer virus genotypes with a PEP7 deletion segregating is shown in this single example. The flowchart of A→B→C illustrates the analysis procedure, starting from quantitative analysis of spore growth using colony sizes (example colonies shown in A). As detailed in Fig. 1, samples are obtained from four possible strains, designed by taking all combinations of two nonchromosomal genotypes and two chromosomal genotypes. The phenotype measurements from the replicate colonies of each strain are used in three statistical models (B, depicting the same raw data with varying predictive models in i, ii, and iii). Each point in B is a single colony size measurement (vertical axis) for a fixed genotype, with mutant or wild-type chromosomal alleles indicated by horizontal position and killer status by color (blue for kil-k and yellow for kil-0). The first model (i) uses only the gene deletion status to make its phenotype predictions. The predictions of the first model are shown by the two dashed horizontal lines, with one line for the deletion and one line for the wild-type allele. Note that the prediction errors, depicted by the vertical gray lines, are large compared with the prediction errors plotted in ii and iii.The second model (ii) uses both the gene deletion status and the nonchromosomal element in a linear model where each effect is independent of the other. This is reflected by four possible model predictions, again represented by dashed horizontal lines. Because the model is constrained to have only independent linear effects, the difference between the killer and nonkiller predictions for each chromosomal allele is the same. The final model (iii) is a joint model where an interaction term allows for a nonlinear, synergistic effect when the deletion is present along with the nonchromosomal element. There are again four possible model predictions, one for each fixed genotype, but the interaction component allows for nonlinear effects. Significance analysis for the nested models is given in Table S3. The average squared errors between the model-specific predictions and the observed data are computed to determine the amount of observed variance explained by each model. A visual representation of the fraction of phenotypic variation explained by each model is shown in C. The data shown in Fig. 3 were obtained by the sequence of analysis steps diagrammed in A→B→C and applied to each analyzed variant.

Edwards et al. www.pnas.org/cgi/content/short/1407126111 2of8 Fig. S3. Analysis of segregating mitochondrial alleles. Allele frequencies in two genomic regions from a study analyzing a large collection of yeast F1 seg- regants are shown. (A) Chromosome 3 is plotted, where alleles from both parents are segregating in the population (frequencies centered at 50%). (B)The mitochondrial genome is plotted and no alleles are segregating (frequencies fixed at 100%).

Fig. S4. (A) Petri plate assay for the presence of the killer virus. The Petri plate assay involves plating a strain on a [kil-0] lawn of cells that are killed by the toxin secreted by the killer strain [kil-k]. Strains with killer activity leave a halo of killed cells (right side of plate). (B) Gel visualization of killer dsRNA. The dsRNA from killer strains can be visualized on an agarose gel. The lower band is the genome component that encodes the toxin protein. (C) Petri plate assay with multiple strains. The left strain is a Sigma killer control, and the middle strain is an SK1 killer. The S288c strain on the right is a nonkiller.

Edwards et al. www.pnas.org/cgi/content/short/1407126111 3of8 Fig. S5. Strain construction details. (A) Strain construction workflow for dsRNA virus tests. (B) Strain construction workflow for mitochondria tests.

Fig. S6. Transfer of cytoplasmic information without nuclear transfer in a kar cross. The kar1-Δ13 mutation blocks nuclear fusion. The figure shows the + transfer of [rho ] mitochondria (red lozenge bodies) from a strain with nucleus A to a [rho0] strain with nucleus B. The exconjugant haploid cell now has nucleus B with the mitochondria from strain A.

Edwards et al. www.pnas.org/cgi/content/short/1407126111 4of8 Table S1. Prevalence of variant types across multiple yeast strains SNPs per Indels per Average Average High-impact High-impact Strains Chromosome chromosome chromosome bases/SNP bases/indel SNPs (total) indels (total)

S288c vs. Sigma Mitochondrial genome 157 143 546.36 599.85 —— Genome 1,752.9 123.4 443.27 6,350.04 —— (nonmitochondrial) Joint analysis of Mitochondrial genome 3,589 2,451 23.90 35.00 24 70 40 yeast strains Genome 25,029 1,861.4 29.25 391.81 1,687 1,796 (nonmitochondrial)

Assembled sequences or raw sequencing reads were obtained from all nonreference strain genomes available in the Saccharomyces Genome Database (1) and from several papers that sequenced nonreference strains to high coverage (2–6). All obtained sequences were aligned to the S288c reference genome with bwa 0.6.2 (7) and confident variants were identified with the samtools variant caller 0.1.19 (8). The number of confident single-nucleotide variants (SNPs) and short insertions or deletions was computed both for the Sigma 1278b strain alone and for all analyzed strains. The number of variants of each type was computed for each chromosome and the mitochondria genome. The variant types were assessed by snpEff (9), and high-impact variants (nonsense or frameshift mutations) were reported. Finally, the average number of base pairs between variant events of each type was calculated by dividing each S288c chromosome size or the mitochondria genome size by the number of variants of each type. Averages across the 16 yeast chromosomes are reported as the averages in the table.

1. Cherry JM, et al. (1998) SGD: Saccharomyces Genome Database. Nucleic Acids Res 26(1):73–79. 2. Liti G, et al. (2009) Population genomics of domestic and wild yeasts. Nature 458(7236):337–341. 3. Nishant KT, et al. (2010) The baker’s yeast diploid genome is remarkably stable in vegetative growth and meiosis. PLoS Genet 6(9):e1001109. 4. Halfmann R, et al. (2012) Prions are a common mechanism for phenotypic inheritance in wild yeasts. Nature 482(7385):363–368. 5. Magwene PM, et al. (2011) Outcrossing, mitotic recombination, and life-history trade-offs shape genome evolution in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 108(5):1987–1992. 6. Dowell RD, et al. (2010) Genotype to phenotype: a complex problem. Science 328(5977):469. 7. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760. 8. Li H, et al.; 1000 Genome Project Data Processing Subgroup (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16):2078–2079. 9. Cingolani P, et al. (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6(2):80–92.

Edwards et al. www.pnas.org/cgi/content/short/1407126111 5of8 Table S2. Colony measurement summaries Nonchromosomal S288c Measured Average colony Colony size Gene element Experiment type Plate Knockout? Killer? mito.? colonies size, pixels SD, pixels

PEP12 Killer virus Deletion vs. killer G 1 1 0 18 104.1 69.3 1 0 0 15 2,638.9 885.8 0 1 0 18 5,149.8 1,090.1 0 0 0 18 4,105.9 894.3 PEP7 Killer virus Deletion vs. killer F 1 1 0 14 145.1 154.5 1 0 0 18 3,563.7 1,028.4 0 1 0 13 6,744.8 1,552.7 0 0 0 18 5,185.1 1,909.1 PHO88 Killer virus Deletion vs. killer D 1 1 0 14 183.4 73.5 1 0 0 14 361.9 40.2 0 1 0 13 395.2 74.1 0 0 0 14 465.5 74.9 SKI7 Killer virus Deletion vs. killer K 1 1 0 15 0.0 0.0 1 0 0 16 4,394.4 807.8 0 1 0 15 5,458.6 1,122.2 0 0 0 15 4,896.6 834.1 SKI8 Killer virus Deletion vs. killer E 1 1 0 11 638.0 438.2 1 0 0 18 2,481.7 594.7 0 1 0 12 5,973.8 644.3 0 0 0 18 5,603.5 966.8 VPS16 Killer virus Deletion vs. Killer H 1 1 0 18 1,355.4 380.4 1 0 0 14 2,806.1 1,139.7 0 1 0 18 7,326.1 2,168.0 0 0 0 14 5,869.4 2,329.6 PHO88 Mitochondrial Deletion vs. mito. (killer) A 1 1 1 9 51.6 36.0 background 1 1 0 12 184.3 84.1 0 1 1 10 754.4 95.5 0 1 0 12 704.3 144.1 SKI8 Mitochondrial Deletion vs. mito. (killer) C 1 1 1 13 1,328.9 350.4 background 1 1 0 10 288.8 225.7 0 1 1 14 3,762.0 610.1 0 1 0 13 4,434.6 303.1 SKI8 Mitochondrial Deletion vs. mito. (Nonkiller) B 1 0 1 18 290.3 71.5 background 1 0 0 16 117.1 44.0 0 0 1 18 346.3 75.5 0 0 0 16 344.1 85.9 MCM22 Killer virus Deletion vs. killer I 1 1 0 18 2,166.3 260.3 1 0 0 18 2,178.4 332.0 0 1 0 18 2,356.2 246.7 0 0 0 18 2,372.6 263.3 PHO88 Mitochondrial Deletion vs. mito. (nonkiller) J 1 0 1 18 4,389.7 721.3 background 1 0 0 20 4,387.7 806.3 0 0 1 16 4,462.4 661.6 0 0 0 20 4,712.4 574.6

Edwards et al. www.pnas.org/cgi/content/short/1407126111 6of8 Table S3. Significance analysis from nested model comparisons Nonchromosomal Experiment Coefficient Coefficient value Fraction of phenotypic F-test P value Gene element type Plate name (in full model) variance explained (r2) (full model)

PEP12 Killer virus Deletion vs. killer G Deletion −0.394 0.549 5.E-35 Nonchromo. element 0.208 0.688 4.E-19 Interaction (both present) −2.016 0.942 2.E-25 PEP7 Killer virus Deletion vs. killer F Deletion −0.324 0.425 3.E-25 Nonchromo. element 0.278 0.613 4.E-17 Interaction (both present) −2.251 0.921 5.E-22 PHO88 Killer virus Deletion vs. killer D Deletion −0.486 0.278 2.E-06 Nonchromo. element −0.334 0.484 2.E-05 Interaction (both present) −1.100 0.560 6.E-03 SKI7 Killer virus Deletion vs. killer K Deletion −0.064 0.347 7.E-42 Nonchromo. element 0.056 0.656 2.E-40 Interaction (both present) −2.300 0.986 3.E-41 SKI8 Killer virus Deletion vs. killer E Deletion −1.036 0.656 2.E-25 Nonchromo. element 0.099 0.756 2.E-09 Interaction (both present) −1.537 0.897 8.E-12 VPS16 Killer virus Deletion vs. killer H Deletion −1.017 0.705 3.E-21 Nonchromo. element 0.083 0.716 0.083 Interaction (both present) −1.176 0.801 5.E-06 PHO88 Mitochondrial Deletion vs. Mito. A Deletion −1.348 0.806 4.E-21 background (killer) Nonchromo. element 0.092 0.847 2.E-04 Interaction (both present) −1.036 0.913 4.E-06 SKI8 Mitochondrial Deletion vs. mito. C Deletion −2.518 0.717 8.E-23 background (killer) Nonchromo. element −0.198 0.772 6.E-06 Interaction (both present) 1.470 0.904 4.E-10 SKI8 Mitochondrial Deletion vs. mito. B Deletion −2.065 0.343 2.E-12 background (nonkiller) Nonchromo. element 0.036 0.538 2.E-08 Interaction (both present) 1.689 0.716 4.E-08 MCM22 Killer virus Deletion vs. killer I Deletion −0.691 0.113 5.E-03 Nonchromo. element −0.051 0.114 0.88 Interaction (both present) 0.033 0.116 0.942 PHO88 Mitochondrial Deletion vs. mito. J Deletion −0.490 0.028 0.173 background (Nonkiller) Nonchromo. element −0.358 0.037 0.487 Interaction (both present) 0.376 0.047 0.426

The coefficient value column is calculated in the full model that includes all three terms. The explained fraction of phenotypic variance is calculated using nested models of increasing complexity that include one (deletion), two (deletion and nonchromosomal element), or three terms. Thus, the fraction of phenotypic variance is nondecreasing for each tested gene and nonchromosomal element pair.

Table S4. Killer virus RNA sequencing results RPKM

L-A virus L-BC virus M1 virus M2 virus Strain SRA accession nos. (NC_003745) (NC_001641) (U78817) (S88081) M28 virus

S288c SRX523025 and SRX523027 0.162 0.041 11.239 0.000 0.300 Sigma SRX018681 and SRX018683 65.205 14.090 3,496.367 0.000 0.423

Edwards et al. www.pnas.org/cgi/content/short/1407126111 7of8 Table S5. Strains used in this study Name Background Genotype Mitochondria Killer

ASN1681 S288c x, his3Δ 1, leu2Δ 0, lys2Δ 0, ura3Δ 0, ubr2::KAN [rho+]S288c [kil-0] ASN2117 Sigma 1278b a, ura3-52, trp1::hisG, leu2::hisG::hisG, his3::hisG, lys2::HIS3MX6 [rho+]Sigma [kil-k] + ASN2129 Sigma 1278b x, ura3-52, trp1::hisG, leu2::hisG::hisG, his3::hisG, lys2::HIS3MX6 [rho ]Sigma [kil-k] + ASN2289 Sigma 1278b a/x, ura3-52/ura3-52, trp1::hisG/trp1::hisG, leu2::hisG/leu2::hisG, [rho ]Sigma [kil-0] his3::hisG/his3::hisG, lys2::HIS3MX6/lys2::HIS3MX6 ASN2295 Sigma 1278b a/x, ura3-52/ura3-52, trp1::hisG/trp1::hisG, leu2::hisG/leu2::hisG, [rho+]Sigma [kil-k] his3::hisG/his3::hisG, lys2::HIS3MX6/lys2::HIS3MX6 ASN2298 Sigma 1278b a, ura3-52, trp1::hisG, leu2::hisG::hisG, his3::hisG, lys2::HIS3MX6 [rho0] [kil-k] ASN2315 Sigma 1278b a, ura3-52, trp1::hisG, leu2::hisG::hisG, his3::hisG, lys2::HIS3MX6 [rho+]S288c [kil-k] ASN2335 Sigma 1278b x, ura3-52, trp1::hisG, leu2::hisG::hisG, his3::hisG, lys2::HIS3MX6 [rho0] [kil-k] + ASN2377 Sigma 1278b a/x, ura3-52/ura3-52, trp1::hisG/trp1::hisG, leu2::hisG/leu2::hisG, [rho ]S288c [kil-k] his3::hisG/his3::hisG, lys2::HIS3MX6/lys2::HIS3MX6

Edwards et al. www.pnas.org/cgi/content/short/1407126111 8of8