| Wheat Dataset | First allele frequency |
| Wheat Dataset | First allele frequency |
| Wheat Dataset | First allele frequency 0.4 0.6 0.8 |
| Abstract | Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies . |
| Framework Genome-Wide Genotypes | We used the subset of 271,486 SNVs With minor allele frequency (MAP) 2 5% for phasing and imputation in this study. |
| Introduction | We demonstrate that the accuracy of the algorithm is above 99% regardless of minor allele frequency , with a call rate of approximately 77%. |
| Pedigree-based Imputation | Furthermore, we compared the Alternative Allele Frequency (AAF) in the Hutterites and CEU sample set. |
| Whole Genome Sequencing and QC | The allele frequency distribution and functional annotation of the final set of 7,008,666 variants in the 98 Hutterites with WGS are shown in Fig S2 in 81 Text. |
| SF3B1 mutant allele frequency | SF3B1 mutant allele frequency |
| SF3B1 mutant allele frequency | Mutant allele frequency was determined by calculating per-base coverages using unique properly paired reads With samtools mpileup for the SF3BI locus and counting the number of reads supporting either the reference or alternate alleles. |
| Supporting Information | Attenuated cryptic 3’SS selection is visible for the K7OOE mutant With only 8.4% allele frequency . |