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The genome ancestry problem was modeled as a combinatorial optimization problem in Zhang et al. (2008).
The genome ancestry problem is closely related to haplotype inference with pedigree data.
The result in Figure 5 shows that the knowledge of pedigree structure is indispensable in solving the genome ancestry problem.
As mentioned previously, an accurate solution to the genome ancestry problem is important to subsequent studies such as QTL analysis.
The vast majority of the sequence variations are attributed to single base pair mutations known as single nucleotide polymorphism (SNPs), thus making SNPs ideal for resolving the genome ancestry problem.
Current haplotyping methods for pedigrees are incapable of solving the genome ancestry problem in animal resources for the following reasons: (i) Pedigrees of model animal resources often contain large number of generations to ensure diversity and reproducibility.
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A similar formulation that extends an iHMM to handle multi-population data was first introduced with application to the local ancestry estimation problem (Sohn et al., 2012) but with a different emission model and for a different data type.
While there are clear merits to this idea, one might worry that the haplotype segments of non-African ancestry would pose problems for imputation.
Therefore, we do not expect sample selection bias to be a problem for ancestry inference in human data except when the source populations for admixture are not well-differentiated.
Study on Mexican ancestry to decode health problems led by Stanford Professor of Biomedical Data Science, of Genetics, and, by courtesy, of Biology, Carlos Bustamante, featured in the San Francisco Chronicle.
If I go to a firm like 23 And Me and pay $299£18585) to get my genes analysed, that's not because I want to outsource my identity to machines – it's because the information they give me from a saliva sample will help me learn all sorts of interesting things, from past ancestry to future health problems, that I couldn't have accessed before.
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