Nonparametric coalescent inference of mutation spectrum history and demography

As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

<p class="MDPI17abstract"><strong>Objective: </strong>This study tries to examine several multivariate methods in classifying genetic diversity using microsatellite allele frequency data.</p><p class="MDPI17abstract"><strong>Methods: </strong>This study used microsatellite allele frequency data from White Leghorn (n = 48), Kampung (n = 48), Pelung (n = 24), Sentul (n = 24), and Black Kedu (n = 25) from Indonesian Research Institute for Animal Production. Allele frequency data were analyzed by the Neighbor-Joining (NJ) method using the POPTREE2 program. The data was also analyzed by the Principal Component Analysis (PCA), Correspondence Analysis (CA), and Hierarchical Clustering on Principal Components (HCPC) methods using the factoextra and FactoMineR packages in the R 4.0.0 program.<strong></strong></p><p class="MDPI17abstract"><strong>Results: </strong>Correspondence Analysis (CA) found Sentul is more closer to Black Kedu. However, based on NJ, PCA, and HCPC showed Sentul is closer to Kampung. Based on the value of Dimension 1, Correspondence Analysis (80.7%) can explain greater variation than PCA (58.9%). However, CA method generated different results compared to NJ, PCA, and HCPC. NJ, PCA, and HCPC found four chicken clusters, namely cluster 1 (White Leghorn), cluster 2 (Pelung), cluster 3 (Black Kedu), and cluster 4 (Kampung and Sentul).<strong></strong></p><p class="MDPI17abstract"><strong>Conclusions: </strong>In conclusion, HCPC is a better multivariate method for analyzing allele frequency data than PCA and CA. HCPC can be used to analyze allele frequency data better than PCA, because HCPC is a combination of methods from hierarchical clustering and principal components.</p>

Multiple Haplotype Reconstruction from Allele Frequency Data

Since haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose a new, computationally efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Our approach seems to be the first that is able to estimate more than one haplotype given such data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. The design matrix, with 0/1 entries, models haplotypes and the columns of the coefficient matrix represent the frequencies of haplotypes, which are non-negative and sum up to one. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.

Adaptive landscape of protein variation in human exomes

The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles had experienced a reversal of fortune in the human lineage. We explored non-adaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Our analyses suggest that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This conclusion is supported by the facts that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and that hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of signatures of non-neutral evolution in the human proteome. We expect this collection to be enriched in beneficial variation. EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.