Additional file 6: Table S2. of The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae

List of genes used in the RH analysis with the BY4741 strain that are present in the subtelomeric regions and are not essential. (XLSX 13 kb

Additional file 3: Figure S2. of The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae

Workflow of populations’ selection and sequencing. Cells were grown in complete media (YPD) and synthetic must (SM), and were incubated at either optimum temperature (28 °C) or low temperature (15 °C) until the stationary phase was reached. At this time, the volume required to inoculate at an OD of 0.2 was re-inoculated into 60 mL of fresh medium. The experiment was carried out 8 times after which the selected populations were analyzed and sequenced. (PDF 43 kb

Additional file 2: Figure S1. of The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae

Distribution of private nonsynonymous SNPs in P5 and P24 compared to S288c. An external circle indicates P24 and an internal circle indicates P5. Homozygous changes are colored in green, while heterozygous changes are marked in red. (PDF 243 kb

Inference of recombination from simulated data.

<p>a) A histogram of estimated recombination rates from simulated data under uniform recombination using our likelihood calculation together with the <i>interval</i> tool from <i>LDhat </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062266#pone.0062266-McVean1" target="_blank">[9]</a>. Input recombination rates were chosen to cover a biologically realistic range and were well recovered by the inference. Each of the 100 simulations has 100 segregating sites at 1 kb intervals (other parameters ). b) Inference of recombination rate for a simulation with varying rate. The simulated recombination profile (blue) had three recombination hotspots, with (50,25,50)-fold higher rate than the background; other parameters as before. The inferred profile is in good agreement with the input (red band: 95% confidence interval from 300 bootstrap samples of the single realisation of the crossing simulation).</p

Power to discover recombination hot and cold regions under different crossing designs.

<p>Red (blue) curves show the ability to correctly recover the hottest (coldest) recombining kb at resolution and number of crossing rounds . <b>a, b</b>) Results for an advanced intercross design, comprising 12 generations of crossing. Curves are calculated by comparing the locations of the hottest and coldest regions from the landscapes inferred for each of the ten simulated crossing experiments to the corresponding locations in the true input landscape (that inferred for the two-way cross), taking the mean value of the size of the overlap. <b>c, d</b>) Results for a single generation cross. The advanced intercross design has a clear advantage over single generation experiment.</p

Decay of recombination rates near hotspots.

<p>Dots show the mean recombination rate for a 0.5 kb region at given distance from a region of high recombination rate in the 2-way (blue), four-way (red) and s-way (black) crosses. Statistics are calculated for genomic regions close to the 100 0.5 kb regions of highest inferred recombination rate. Dotted lines show the mean genome-wide recombination rate in each case (for congruence of hotspots see Figure S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062266#pone.0062266.s001" target="_blank">Text S1</a>).</p

Additional file 7: Figure S5. of The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae

Outline of the construction of advanced intercross lines. We carried out a strategy that forces yeast cells through multiple rounds of random mating and sporulation to create advanced intercross lines (AILs). This step can improve genetic mapping in two ways: increasing resolution by reducing linkage and unlinking nearby QTLs. (PDF 168 kb

Genome-wide recombination rates at 10 kb resolution.

<p>Inferred recombination rates shown at 10.0 kb resolution across the genome from individuals generated through the two-way (panel a, red), four-way (panel b, blue) and s-way (panel c, black) crosses. The histograms show a broad distribution of recombination rates for each cross. The four-way cross had a substantially higher mean rate of recombination than the two-way cross, the s-way cross having a higher mean rate than the four-way cross (ratio of means and ).</p

Assessing the robustness of inferred recombination characteristics.

<p>Red histograms show input data (two-way cross) and green inferred values from simulated crossing experiments using the two-way recombination landscape as input. The overlap between distributions is yellow-brown. a) At high resolution kb the inference is underpowered to call the low values leading to a systematic bias for these cold regions. b) Using 10 kb resolution removes this bias almost completely. c) The inference would also work for 0.5 kb resolution for the whole range of recombination rates if we would get ten times more samples. d–f) Analogous figures for a simulated two generation cross show that the bias is much larger and would persist even with ten times more data available.</p

An example crossing experiment.

<p>The initial population has individuals drawn in some proportions from distinct parental strains. These individuals undergo generations of random mating (multiple crossing protocols, including a funnel design, can be analysed), following which haplotypes are sampled. We use sequences from the offspring population to infer a recombination rate profile for the cross.</p