Expected frequencies of nine genotypes under gametic selection.

<p></p

Power of QTL detection under various situations.

<p>Old: the modified method of Wen et al. (2013) with the corrected linkage groups; New: the proposed method in this study.</p

Expected frequencies and relative fitness for one locus of epistatic SDL.

<p>Expected frequencies and relative fitness for one locus of epistatic SDL.</p

Standard deviation and absolute bias for QTL estimates in the new method.

<p><i>a</i>: additive effect; <i>d</i>: dominant effect; and <i>var</i>: residual variance.</p

Multi-QTL Mapping for Quantitative Traits Using Epistatic Distorted Markers

<div><p>The interaction between segregation distortion loci (SDL) has been often observed in all kinds of mapping populations. However, little has been known about the effect of epistatic SDL on quantitative trait locus (QTL) mapping. Here we proposed a multi-QTL mapping approach using epistatic distorted markers. Using the corrected linkage groups, epistatic SDL was identified. Then, these SDL parameters were used to correct the conditional probabilities of QTL genotypes, and these corrections were further incorporated into the new QTL mapping approach. Finally, a set of simulated datasets and a real data in 304 mouse F₂ individuals were used to validate the new method. As compared with the old method, the new one corrects genetic distance between distorted markers, and considers epistasis between two linked SDL. As a result, the power in the detection of QTL is higher for the new method than for the old one, and significant differences for estimates of QTL parameters between the two methods were observed, except for QTL position. Among two QTL for mouse weight, one significant difference for QTL additive effect between the above two methods was observed, because epistatic SDL between markers C66 and T93 exists (<i>P</i> = 2.94e-4).</p></div

Mapping QTL for weight in 333 mouse F₂ individuals.

<p>(a) LOD scores using composite interval mapping (CIM, <i>curve</i>), old (<i>solid vertical line</i>) and new (<i>dashed vertical line</i>) methods. <i>Dashed horizontal line</i> represents critical value for significant QTL. <i>Hollow vertical line</i> indicates value of segregation distortion test for marker interval of QTL; (b) QTL absolute effects, detected by old and new methods. NS and <i>star</i> indicate no difference and significant difference at the 0.01 level between old and new methods, respectively.</p

Simulated parameters in all the Monte Carlo simulation experiments.

<p>Simulated parameters in all the Monte Carlo simulation experiments.</p

for QTL parameters in the paired <i>t</i> test between the old and new methods.

<p><i>p</i>: QTL position; <i>a</i>: additive effect of QTL; <i>d</i>: dominant effect of QTL; <i>var</i>: residual variance. The <i>dashed line</i> represents the critical value at the 0.05 level of significance.</p

Table_1_Evaluating Structural Variation Detection Tools for Long-Read Sequencing Datasets in Saccharomyces cerevisiae.xlsx

Structural variation (SV) represents a major form of genetic variations that contribute to polymorphic variations, human diseases, and phenotypes in many organisms. Long-read sequencing has been successfully used to identify novel and complex SVs. However, comparison of SV detection tools for long-read sequencing datasets has not been reported. Therefore, we developed an analysis workflow that combined two alignment tools (NGMLR and minimap2) and five callers (Sniffles, Picky, smartie-sv, PBHoney, and NanoSV) to evaluate the SV detection in six datasets of Saccharomyces cerevisiae. The accuracy of SV regions was validated by re-aligning raw reads in diverse alignment tools, SV callers, experimental conditions, and sequencing platforms. The results showed that SV detection between NGMLR and minimap2 was not significant when using the same caller. The PBHoney was with the highest average accuracy (89.04%) and Picky has the lowest average accuracy (35.85%). The accuracy of NanoSV, Sniffles, and smartie-sv was 68.67%, 60.47%, and 57.67%, respectively. In addition, smartie-sv and NanoSV detected the most and least number of SVs, and SV detection from the PacBio sequencing platform was significantly more than that from ONT (p = 0.000173).</p

DataSheet_1_Evaluating Structural Variation Detection Tools for Long-Read Sequencing Datasets in Saccharomyces cerevisiae.docx

Structural variation (SV) represents a major form of genetic variations that contribute to polymorphic variations, human diseases, and phenotypes in many organisms. Long-read sequencing has been successfully used to identify novel and complex SVs. However, comparison of SV detection tools for long-read sequencing datasets has not been reported. Therefore, we developed an analysis workflow that combined two alignment tools (NGMLR and minimap2) and five callers (Sniffles, Picky, smartie-sv, PBHoney, and NanoSV) to evaluate the SV detection in six datasets of Saccharomyces cerevisiae. The accuracy of SV regions was validated by re-aligning raw reads in diverse alignment tools, SV callers, experimental conditions, and sequencing platforms. The results showed that SV detection between NGMLR and minimap2 was not significant when using the same caller. The PBHoney was with the highest average accuracy (89.04%) and Picky has the lowest average accuracy (35.85%). The accuracy of NanoSV, Sniffles, and smartie-sv was 68.67%, 60.47%, and 57.67%, respectively. In addition, smartie-sv and NanoSV detected the most and least number of SVs, and SV detection from the PacBio sequencing platform was significantly more than that from ONT (p = 0.000173).</p