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We generate simulation by randomly mating individuals and generating a pedigree using a recombination rate of 10−.
For computational convenience, we set the size of the simulator's non-recombinant segments between 0.01 and 0.025 cM, as specified in Section 3, always using a recombination rate of 1cM/Mb.
Therefore, to include recombination rate as an extra filter would not be prudent, for example, using the QCed post-imputation QC filtered data and applying a further filter using a recombination rate threshold of >1 cM/Mb would eliminate 2075 SNPs, only 24 of which are significantly different.
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The simulation can also be used for modeling allopatric populations because geographical isolation would be represented in the simulation by using an interpopulation recombination rate of zero.
Direct sequencing of the S1 S29 segment in GE, US and JP cohorts, and data analysis using Hotspotter [15] yielded a recombination rate for the JP SZ-F cohort falling within the recombination hotspot range of 10 120 fold the genome average recombination rate of 0.89 cM/Mb [16], [17]; the rates for other GE, US and JP cohorts also approached this range (Figure 1B).
The acceptance probability is given by New θ is proposed according to a sliding window with reflecting boundary at 0. The acceptance probability is given by We also use a variable recombination rate model in our analysis that includes background rate variation and hotspots.
We find that the extremely low haplotype diversity observed in Yorkshire and Landrace is not generated by the simulation even when a recombination rate of zero is used.
The LDhat program used an average recombination rate resolution of 386 bp.
Linear regression was performed using regional recombination rate as the response variable using a stepwise backward method.
Using a rate of recombination of 3.5 × 10−6/bp, a rate of outcrossing of 2 × 10−5/generation, and an effective population size of 1.6 × 10 cells (Ruderfer et al. 2006), we expect diversity to be increased by a factor of 10 and 2, 13 bp and 113 bp from a site underbalancing selection, respectively.
Despite its widespread use and popularity, this approach has disadvantages: the fitting of a polynomial function across a chromosome artificially increases spatial autocorrelation of recombination rate estimates, which renders most statistical analyses using multiple recombination rate estimates derived from this function inappropriate.
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