Wu et al. 2021 #96
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Wu et al. 2021 #96
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standage
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Nov 1, 2022
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| mh01NH-03 MHDBM-e7a95c5e GRCh38 chr1 184807944,184807966,184808042 1.9085 0.1987 0.1725 10.1016/j.legalmed.2015.06.003 | ||
| mh01NH-03 MHDBM-e7a95c5e GRCh38 chr1 184807944,184807966,184808042 1.9086 0.1987 0.1725 10.1016/j.legalmed.2015.06.003 |
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A couple of these rounding artifacts pop up every once in a while depending on numpy or pandas version. 🤨
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| rsid = fields[2] | ||
| if rsid not in rsids: | ||
| continue |
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One of the markers is spanned by two CNVs, resulting in bookkeeping errors when aggregating the marker SNP data. These change to the build code eliminates that error.
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Will do this today! |
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Thanks @rnmitchell! |
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This branch adds a new marker collection from Wu et al. (2021). Closes #94.
Effective number of alleles (Ae)
This plot shows Ae values averaged over the three Chinese population groups in the 1000 Genomes Project as a function of Ae averaged over all 26 global population groups. Glyphs above the dashed grey line at
y=xindicate markers with more allelic diversity in Chinese populations than the global average. Markers from this study are shown in red, with markers from USC and ALFRED shown in green and blue for comparison.This plot shows Ae values in three distinct Chinese population groups as a function of the global average Ae.