The structure of allelic diversity in the presence of purifying selection

HLA allele and haplotype frequencies in Kazakhstani Russians and their relationship with other populations

HLA class I and class II genotypes from 947 Kazakhstani individuals of Russian origin were analyzed for investigating their most likely origin. The results were compared with similar data from other Russians (East and West), and also Worldwide populations, using standard genetic distances, neighbor-joining dendrograms, correspondence and haplotype analysis. Of the five HLA loci analyzed (HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1) genotyped, 216 HLA alleles were identified. The most frequent alleles were A*02:01 (26.5%), B*07:02 (11.1%), C*04:01 (13.5%) and C*06:02 (12.1%), DRB1*07:01 (13.8%) and DRB1*15:01 (12.2%), and DQB1*03:01 (19.7%). Significant linkage disequilibrium was noted between all HLA pairs. DRB1*15:01 ~ DQB1*06:02 (10.5%), B*07:02 ~ C*07:02 (10.0%), B*07:02 ~ DRB1*15:01 (6.3%), and A*01:01 ~ B*08:01 (4.5%) were the most frequent two-locus haplotypes identified. Subsequent analyses showed that Kazakhstani Russians were closely related to West Russia-residing populations (Northwest Slavic, Vologda, Chelyabinsk, Moscow), East Europeans (Belarus Brest, Ukraine, Poland) and Scandinavians (Swedish, Finns), but distinct from East Russia-residing populations (Tuvians, Siberians from Chukotka, Kamchatka, and Ulchi) and East Mediterraneans (Levantines, Turks, North Macedonians, Albanians), and East Asians (Koreans, Japanese, Taiwanese, Mongolians). These results are in accordance with historical data indicating that the Russians of central Asia originate mainly from European Russia during the migratory flow of 18th and 19th centuries.

HLA-A, -B, -C, -DRB1 and -DQB1 allele and haplotype frequencies in Lebanese and their relatedness to neighboring and distant populations

BACKGROUND

This study examined the origin of present-day Lebanese using high-resolution HLA class I and class II allele and haplotype distributions. The study subjects comprised 152 unrelated individuals, and their HLA class I and class II alleles and two-locus and five-locus haplotypes were compared with those of neighboring and distant communities using genetic distances, neighbor-joining dendrograms, correspondence, and haplotype analyses. HLA class I (A, B, C) and class II (DRB1, DQB1) were genotyped at a high-resolution level by PCR-SSP.

RESULTS

In total, 76 alleles across the five HLA loci were detected: A*03:01 (17.1%), A*24:02 (16.5%), B*35:01 (25.7%), C*04:01 (25.3%), and C*07:01 (20.7%) were the most frequent class I alleles, while DRB1*11:01 (34.2%) and DQB1*03:01 (43.8%) were the most frequent class II alleles. All pairs of HLA loci were in significant linkage disequilibrium. The most frequent two-locus haplotypes recorded were DRB1*11:01 ~ DQB1*03:01 (30.9%), B*35:01-C*04:01 (20.7%), B*35:01 ~ DRB1*11:01 (13.8%), and A*24:02 ~ B*35:01 (10.3%). Lebanese appear to be closely related to East Mediterranean communities such as Levantines (Palestinians, Syrians, and Jordanians), Turks, Macedonians, and Albanians. However, Lebanese appear to be distinct from North African, Iberian, and Sub-Saharan communities.

CONCLUSIONS

Collectively, this indicates a limited genetic contribution of Arabic-speaking populations (from North Africa or the Arabian Peninsula) and Sub-Saharan communities to the present-day Lebanese gene pool. This confirms the notion that Lebanese population are of mixed East Mediterranean and Asian origin, with a marked European component.

Generalization of the Ewens sampling formula to arbitrary fitness landscapes

In considering evolution of transcribed regions, regulatory sequences, and other genomic loci, we are often faced with a situation in which the number of allelic states greatly exceeds the size of the population. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from the population, do not change with time. In the absence of selection, the probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary fitness distributions. Although our approach is general, we focus on the class of fitness landscapes, inspired by recent high-throughput genotype-phenotype maps, in which alleles can be in several distinct phenotypic states. This class of landscapes yields sampling probabilities that are computationally more tractable and can form a basis for inference of selection signatures from genomic data. Using an efficient numerical implementation of the sampling probabilities, we demonstrate that, for a sizable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other evolutionary parameters from population data. We also carry out numerical simulations to challenge various approximations involved in deriving our sampling formulas, such as the infinite-allele limit and the “full connectivity” assumption inherent in the Ewens theory, in which each allele can mutate into any other allele. We find that, at least for the specific numerical examples studied, our theory remains sufficiently accurate even if these assumptions are relaxed. Thus our framework establishes both theoretical and practical foundations for inferring selection signatures from population-level genomic sequence samples.

Genetic diversity and the structure of genealogies in rapidly adapting populations

Positive selection distorts the structure of genealogies and hence alters patterns of genetic variation within a population. Most analyses of these distortions focus on the signatures of hitchhiking due to hard or soft selective sweeps at a single genetic locus. However, in linked regions of rapidly adapting genomes, multiple beneficial mutations at different loci can segregate simultaneously within the population, an effect known as clonal interference. This leads to a subtle interplay between hitchhiking and interference effects, which leads to a unique signature of rapid adaptation on genetic variation both at the selected sites and at linked neutral loci. Here, we introduce an effective coalescent theory (a "fitness-class coalescent") that describes how positive selection at many perfectly linked sites alters the structure of genealogies. We use this theory to calculate several simple statistics describing genetic variation within a rapidly adapting population and to implement efficient backward-time coalescent simulations, which can be used to predict how clonal interference alters the expected patterns of molecular evolution.

Distortions in genealogies due to purifying selection

Purifying selection can substantially alter patterns of molecular evolution. Its main effect is to reduce overall levels of genetic variation, leading to a reduced effective population size. However, it also distorts genealogies relative to neutral expectations. A structured coalescent method has been used to describe this effect, and forms the basis for numerical methods and simulations. In this study, we extend this approach by making the additional approximation that lineages may be treated independently, which is valid only in the strong selection regime. We show that in this regime, the distortions due to purifying selection can be described by a time-dependent effective population size and mutation rate, confirming earlier intuition. We calculate simple analytical expressions for these functions, N(e)(t) and U(e)(t). These results allow us to describe the structure of genealogies in a population under strong purifying selection as equivalent to a purely neutral population with varying population size and mutation rate, thereby enabling the use of neutral methods of inference and estimation for populations in the strong selection regime.