CSHAP: efficient haplotype frequency estimation based on sparse representation

Motivation Estimating haplotype frequencies from genotype data plays an important role in genetic analysis. In silico methods are usually computationally involved since phase information is not available. Due to tight linkage disequilibrium and low recombination rates, the number of haplotypes observed in human populations is far less than all the possibilities. This motivates us to solve the estimation problem by maximizing the sparsity of existing haplotypes. Here, we propose a new algorithm by applying the compressive sensing (CS) theory in the field of signal processing, compressive sensing haplotype inference (CSHAP), to solve the sparse representation of haplotype frequencies based on allele frequencies and between-allele co-variances. Results Our proposed approach can handle both individual genotype data and pooled DNA data with hundreds of loci. The CSHAP exhibits the same accuracy compared with the state-of-the-art methods, but runs several orders of magnitude faster. CSHAP can also handle with missing genotype data imputations efficiently. Availability and implementation The CSHAP is implemented in R, the source code and the testing datasets are available at http://home.ustc.edu.cn/∼zhouys/CSHAP/. Supplementary information Supplementary data are available at Bioinformatics online.

Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine Exert Inverse Selective Pressure on Plasmodium Falciparum Drug Sensitivity-Associated Haplotypes in Uganda

Background Altered sensitivity to multiple antimalarial drugs is mediated by polymorphisms in pfmdr1, which encodes the Plasmodium falciparum multidrug resistance transporter. In Africa the N86Y and D1246Y polymorphisms have been shown to be selected by treatment, with artemether-lumefantrine (AL) and dihydroartemisinin-piperaquine (DP) selecting for wild-type and mutant alleles, respectively. However, there has been little study of pfmdr1 haplotypes, in part because haplotype analyses are complicated by multiclonal infections. Methods We fit a haplotype frequency estimation model, which accounts for multiclonal infections, to the polymorphic pfmdr1 N86Y, Y184F, and D1246Y alleles in samples from a longitudinal trial comparing AL and DP to treat uncomplicated P falciparum malaria in Tororo, Uganda from 2007 to 2012. We regressed estimates onto covariates of trial arm and selective drug pressure. Results Yearly trends showed increasing frequency estimates for haplotypes with wild type pfmdr1 N86 and D1246 alleles and decreasing frequency estimates for haplotypes with the mutant pfmdr1 86Y allele. Considering days since prior therapy, we saw evidence suggestive of selection by AL for haplotypes with N86 combined with 184F, D1246, or both, and against all haplotypes with 86Y, and evidence suggestive of selection by DP for 86Y only when combined with Y184 and 1246Y (haplotype YYY) and against haplotypes NFD and NYY. Conclusions Based on our model, AL selected several haplotypes containing N86, whereas DP selection was haplotype specific, demonstrating the importance of haplotype analyses. Inverse selective pressure of AL and DP on the complementary haplotypes NFD and YYY suggests that rotating artemisinin-based antimalarial combination regimens may be the best treatment option to prevent resistance selection.