Recombination rate and selection strength in HIV intra-patient evolution

The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Here, we estimate the rate of recombination and the distribution of selection coefficients from time-resolved sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be r=1.4e-5 recombinations per site and generation. Furthermore, we provide evidence that selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible, as soon as data with higher time resolution and greater sample sizes is available.Comment: to appear in PLoS Computational Biolog

UGT1A1 is a major locus influencing bilirubin levels in African Americans

Total serum bilirubin is associated with several clinical outcomes, including cardiovascular disease, diabetes and drug metabolism. We conducted a genome-wide association study in 619 healthy unrelated African Americans in an attempt to replicate reported findings in Europeans and Asians and to identify novel loci influencing total serum bilirubin levels. We analyzed a dense panel of over two million genotyped and imputed SNPs in additive genetic models adjusting for age, sex, and the first two significant principal components from the sample covariance matrix of genotypes. Thirty-nine SNPs spanning a 78 kb region within the UGT1A1 displayed P-values <5 × 10−8. The lowest P-value was 1.7 × 10−22 for SNP rs887829. None of SNPs in the UGT1A1 remained statistically significant in conditional association analyses that adjusted for rs887829. In addition, SNP rs10929302 located in phenobarbital response enhancer module was significantly associated with bilirubin level with a P-value of 1.37 × 10−11; this enhancer module is believed to have a critical role in phenobarbital treatment of hyperbilirubinemia. Interestingly, the lead SNP, rs887829, is in strong linkage disequilibrium (LD) (r2≥0.74) with rs10929302. Taking advantage of the lower LD and shorter haplotypes in African-ancestry populations, we identified rs887829 as a more refined proxy for the causative variant influencing bilirubin levels. Also, we replicated the reported association between variants in SEMA3C and bilirubin levels. In summary, UGT1A1 is a major locus influencing bilirubin levels and the results of this study promise to contribute to understanding of the etiology and treatment of hyperbilirubinaemia in African-ancestry populations

Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

Despite the availability of newer approaches, traditional hierarchical clustering remains very popular in genetic diversity studies in plants. However, little is known about its suitability for molecular marker data. We studied the performance of traditional hierarchical clustering techniques using real and simulated molecular marker data. Our study also compared the performance of traditional hierarchical clustering with model-based clustering (STRUCTURE). We showed that the cophenetic correlation coefficient is directly related to subgroup differentiation and can thus be used as an indicator of the presence of genetically distinct subgroups in germplasm collections. Whereas UPGMA performed well in preserving distances between accessions, Ward excelled in recovering groups. Our results also showed a close similarity between clusters obtained by Ward and by STRUCTURE. Traditional cluster analysis can provide an easy and effective way of determining structure in germplasm collections using molecular marker data, and, the output can be used for sampling core collections or for association studies

Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10−6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease

Inflammatory Genital Infections Mitigate a Severe Genetic Bottleneck in Heterosexual Transmission of Subtype A and C HIV-1

The HIV-1 epidemic in sub-Saharan Africa is driven largely by heterosexual transmission of non-subtype B viruses, of which subtypes C and A are predominant. Previous studies of subtype B and subtype C transmission pairs have suggested that a single variant from the chronically infected partner can establish infection in their newly infected partner. However, in subtype A infected individuals from a sex worker cohort and subtype B individuals from STD clinics, infection was frequently established by multiple variants. This study examined over 1750 single-genome amplified viral sequences derived from epidemiologically linked subtype C and subtype A transmission pairs very early after infection. In 90% (18/20) of the pairs, HIV-1 infection is initiated by a single viral variant that is derived from the quasispecies of the transmitting partner. In addition, the virus initiating infection in individuals who were infected by someone other than their spouse was characterized to determine if genital infections mitigated the severe genetic bottleneck observed in a majority of epidemiologically linked heterosexual HIV-1 transmission events. In nearly 50% (3/7) of individuals infected by someone other than their spouse, multiple genetic variants from a single individual established infection. A statistically significant association was observed between infection by multiple genetic variants and an inflammatory genital infection in the newly infected individual. Thus, in the vast majority of HIV-1 transmission events in cohabiting heterosexual couples, a single genetic variant establishes infection. Nevertheless, this severe genetic bottleneck can be mitigated by the presence of inflammatory genital infections in the at risk partner, suggesting that this restriction on genetic diversity is imposed in large part by the mucosal barrier

The Evolutionary Dynamics of a Rapidly Mutating Virus within and between Hosts: The Case of Hepatitis C Virus

Many pathogens associated with chronic infections evolve so rapidly that strains found late in an infection have little in common with the initial strain. This raises questions at different levels of analysis because rapid within-host evolution affects the course of an infection, but it can also affect the possibility for natural selection to act at the between-host level. We present a nested approach that incorporates within-host evolutionary dynamics of a rapidly mutating virus (hepatitis C virus) targeted by a cellular cross-reactive immune response, into an epidemiological perspective. The viral trait we follow is the replication rate of the strain initiating the infection. We find that, even for rapidly evolving viruses, the replication rate of the initial strain has a strong effect on the fitness of an infection. Moreover, infections caused by slowly replicating viruses have the highest infection fitness (i.e., lead to more secondary infections), but strains with higher replication rates tend to dominate within a host in the long-term. We also study the effect of cross-reactive immunity and viral mutation rate on infection life history traits. For instance, because of the stochastic nature of our approach, we can identify factors affecting the outcome of the infection (acute or chronic infections). Finally, we show that anti-viral treatments modify the value of the optimal initial replication rate and that the timing of the treatment administration can have public health consequences due to within-host evolution. Our results support the idea that natural selection can act on the replication rate of rapidly evolving viruses at the between-host level. It also provides a mechanistic description of within-host constraints, such as cross-reactive immunity, and shows how these constraints affect the infection fitness. This model raises questions that can be tested experimentally and underlines the necessity to consider the evolution of quantitative traits to understand the outcome and the fitness of an infection