Genome-wide scans for signatures of selection in Mangalarga Marchador horses using high-throughput SNP genotyping

Background The detection of signatures of selection in genomic regions provides insights into the evolutionary process, enabling discoveries regarding complex phenotypic traits. In this research, we focused on identifying genomic regions affected by different selection pressures, mainly highlighting the recent positive selection, as well as understanding the candidate genes and functional pathways associated with the signatures of selection in the Mangalarga Marchador genome. Besides, we seek to direct the discussion about genes and traits of importance in this breed, especially traits related to the type and quality of gait, temperament, conformation, and locomotor system. Results Three different methods were used to search for signals of selection: Tajima’s D (TD), the integrated haplotype score (iHS), and runs of homozygosity (ROH). The samples were composed of males (n = 62) and females (n = 130) that were initially chosen considering well-defined phenotypes for gait: picada (n = 86) and batida (n = 106). All horses were genotyped using a 670 k Axiom® Equine Genotyping Array​ (Axiom MNEC670). In total, 27, 104 (chosen), and 38 candidate genes were observed within the signatures of selection identified in TD, iHS, and ROH analyses, respectively. The genes are acting in essential biological processes. The enrichment analysis highlighted the following functions: anterior/posterior pattern for the set of genes (GLI3, HOXC9, HOXC6, HOXC5, HOXC4, HOXC13, HOXC11, and HOXC10); limb morphogenesis, skeletal system, proximal/distal pattern formation, JUN kinase activity (CCL19 and MAP3K6); and muscle stretch response (MAPK14). Other candidate genes were associated with energy metabolism, bronchodilator response, NADH regeneration, reproduction, keratinization, and the immunological system. Conclusions Our findings revealed evidence of signatures of selection in the MM breed that encompass genes acting on athletic performance, limb development, and energy to muscle activity, with the particular involvement of the HOX family genes. The genome of MM is marked by recent positive selection. However, Tajima’s D and iHS results point also to the presence of balancing selection in specific regions of the genome.

Using deep learning to identify recent positive selection in malaria parasite sequence data

Background Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods A deep learning-based approach (called “DeepSweep”) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep. Results Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). Conclusion The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.

Chromosome X-wide analysis of positive selection in human populations: from common and private signals to selection impact on inactivated genes and enhancers-like signatures

The ability of detecting adaptive (positive) selection in the genome has opened the possibility of understanding the genetic bases of population-specific adaptations genome-wide. Here we present the analysis of recent selective sweeps specifically in the X chromosome in different human populations from the third phase of the 1000 Genomes Project using three different haplotype-based statistics. We describe numerous instances of genes under recent positive selection that fit the regimes of hard and soft sweeps, showing a higher amount of detectable sweeps in sub-Saharan Africans than in non-Africans (Europe and East Asia). A global enrichment is seen in neural-related processes while numerous genes related to fertility appear among the top candidates, reflecting the importance of reproduction in human evolution. Commonalities with previously reported genes under positive selection are found, while particularly strong new signals are reported in specific populations or shared across different continental groups. We report an enrichment of signals in genes that escape X chromosome inactivation, which may contribute to the differentiation between sexes. We also provide evidence of a widespread presence of soft-sweep-like signatures across the chromosome and a global enrichment of highly scoring regions that overlap potential regulatory elements. Among these, enhancers-like signatures seem to present putative signals of positive selection that might be in concordance with selection in their target genes. Also, particularly strong signals appear in regulatory regions that show differential activities, which might point to population-specific regulatory adaptations.

Soft sweeps predominate recent positive selection in bonobos (Pan paniscus) and chimpanzees (Pan troglodytes)

Two modes of positive selection have been recognized: 1) hard sweeps that result in the rapid fixation of a beneficial allele typically from a de novo mutation and 2) soft sweeps that are characterized by intermediate frequencies of at least two haplotypes that stem from standing genetic variation or recurrent de novo mutations. While many populations exhibit both hard and soft sweeps throughout the genome, there is increasing evidence that soft sweeps, rather than hard sweeps, are the predominant mode of adaptation in many species, including humans. Here, we use a supervised machine learning approach to assess the extent of hard and soft sweeps in the closest living relatives of humans: bonobos and chimpanzees (genus Pan). We trained convolutional neural network classifiers using simulated data and applied these classifiers to population genomic data for 71 individuals representing all five extant Pan lineages, of which we successfully analyzed 60 individuals from four lineages. We found that recent adaptation in Pan is largely the result of soft sweeps, ranging from 73.1 to 97.7% of all identified sweeps. While few hard sweeps were shared among lineages, we found that between 19 and 267 soft sweep windows were shared by at least two lineages. We also identify novel candidate genes subject to recent positive selection. This study emphasizes the importance of shifts in the physical and social environment, rather than novel mutation, in shaping recent adaptations in bonobos and chimpanzees.

No Evidence for Widespread Positive Selection Signatures in Common Risk Alleles Associated with Schizophrenia

Schizophrenia poses an evolutionary-genetic paradox as it exhibits strongly negative fitness effects (early mortality and decreased fecundity), yet it persists at a prevalence of approximately 1% worldwide. Evidence from several studies have suggested that schizophrenia is evolved and maintained in part as a maladaptive byproduct of recent positive selection and adaptive evolution in human beings. However, inconsistent results have been also proposed, challenging the recent positive selection theory to explain the high population frequency of schizophrenia-associated alleles. Here, we used public domain data to locate signatures of positive selection based on genetic diversity, derived allele frequency, differentiation between populations, and long haplotypes at schizophrenia-associated single nucleotide polymorphisms (SNPs) and randomly selected SNPs (as negative controls). We found evidence for positive selection at 10 out of the 105 schizophrenia-associated SNPs, while 5 of these SNPs involved positive selection for the protective allele. Taken together, the absence of widespread positive selection signals at the schizophrenia-associated SNPs, along with the fact that half of the positive selection favored the protective allele, provide little evidence supporting the positive selection theory in schizophrenia.

Signatures of recent positive selection in enhancers across 41 human tissues

Evolutionary changes in enhancers are widely associated with variation in human traits and diseases. However, studies comprehensively quantifying levels of selection on enhancers at multiple evolutionary time points during recent human evolution and how enhancer evolution varies across human tissues are lacking. To address these questions, we integrated a dataset of 41,561 transcribed enhancers active in 41 different human tissues (FANTOM Consortium) with whole genome sequences of 1,668 individuals from the African, Asian, and European populations (1000 Genomes Project). Our analyses based on four different metrics (Tajima’s D, FST, H12, nSL) showed that ~5.90% of enhancers considered showed evidence of recent positive selection and that genes associated with enhancers under positive selection are enriched for diverse immune-related functions. The distributions of these metrics for brain and testis enhancers were often statistically significantly different compared to those of other tissues; the same was true for brain and testis enhancers that are tissue-specific compared to those that are tissue-broad and for testis enhancers associated with tissue-enriched and non-tissue-enriched genes. These differences varied considerably across metrics and tissues and were generally due to changes in distributions’ shapes rather than shifts in their values. These results suggest that many human enhancers experienced recent positive selection throughout multiple time periods in human evolutionary history, that this selection occurred in a tissue-dependent and immune-related functional context, and that much like the evolution of their coding counterparts, the evolution of brain and testis enhancers has been markedly different from that of enhancers in other tissues.

The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii

Resistance to pyrethroid insecticides is a major concern for malaria vector control, because these are the compounds used in almost all insecticide-treated bed-nets (ITNs), and are also widely used for indoor residual spraying (IRS). Pyrethroids target the voltage-gated sodium channel (VGSC), an essential component of the mosquito nervous system, but substitutions in the amino acid sequence can disrupt the activity of these insecticides, inducing a resistance phenotype. Here we use Illumina whole-genome sequence data from phase 1 of the Anopheles gambiae 1000 Genomes Project (Ag1000G) to provide a comprehensive account of genetic variation in the Vgsc gene in mosquito populations from eight African countries. In addition to the three known resistance alleles, we describe 20 non-synonymous nucleotide substitutions at appreciable frequency in one or more populations that are previously unknown in Anopheles mosquitoes. Thirteen of these novel alleles were found to occur almost exclusively on haplotypes carrying the known L995F resistance allele (L1014F in Musca domesticus codon numbering), and may enhance or compensate for the L995F resistance pheno-type. A novel mutation I1527T, which is adjacent to a predicted pyrethroid binding site, was found in tight linkage with either of two alleles causing a V402L substitution, similar to a combination of substitutions found to cause pyrethroid resistance in several other insect species. We analyse the genetic backgrounds on which non-synonymous alleles are found, to determine which alleles have experienced recent positive selection, and to refine our understanding of the spread of resistance between species and geographical locations. We describe twelve distinct haplotype groups with evidence of recent positive selection, five of which carry the known L995F resistance allele, five of which carry the known L995S resistance allele, one of which carries the novel I1527T allele, and one of which carries a novel M490I allele. Seven of these groups are localised to a single geographical location, and five comprise haplotypes from different countries, in one case separated by over 3000 km, providing new information about the geographical distribution and spread of resistance. We also find evidence for multiple introgression events transmitting resistance alleles between An. gambiae and An. coluzzii. We identify markers that could be used to design high-throughput, low-cost genetic assays for improved surveillance of pyrethroid resistance in the field. Our results demonstrate that the molecular basis of target-site pyrethroid resistance in malaria vectors is more complex than previously appreciated, and provide a foundation for the development of new genetic tools to track the spread insecticide resistance and improve the design of strategies for insecticide resistance management.

Exploring the association between glucose-6-phosphate dehydrogenase deficiency and color blindness in Southeast Asia

BackgroundGlucose-6-phosphate dehydrogenase (G6PD) deficiency poses problems for the treatment of Plasmodium vivax malaria, as the 8-aminoquinolines, used to eliminate liver hypnozoites, cause hemolysis in G6PD-deficient individuals.G6PD deficiency is an X-linked disorder that can be linked to other conditions determined by genes located nearby on the Xq28 band of the X chromosome, including red–green color blindness. A Karen population has undergone recent positive selection for G6PD deficiency with extended long-range haplotypes around G6PD.ObjectivesTo determine the association between G6PD deficiency and color blindness in a Karen population that lives in an area endemic for P. vivax and that is already known to display long-range haplotypes around G6PD because of the recent positive selection of the Mahidol G6PD deficiency allele.MethodWe examined the phenotypic association between G6PD deficiency and color blindness.ResultsOf 186 male participants successfully assessed for color blindness using the Ishihara 38 plates test, 10 (5.4%) were red–green color blind, while 1 individual was totally color blind. There was a nonsignificant trend toward negative association (repulsion) between G6PD deficiency and red–green color blindness; 34/35 individuals with the Mahidol variant of G6PD deficiency had normal vision, while 9 of the 10 red–green color blind individuals were G6PD normal. A single individual had both conditions.ConclusionsDespite the long-range haplotype associated with G6PD deficiency in this population, color blindness is not informative in terms of predicting G6PD deficiency in this population. The most likely explanation is that there are multiple genetic causes of red–green color blindness.

High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. We developed a new method, ASMC, that can estimate coalescence times using only SNP array data, and is 2-4 orders of magnitude faster than previous methods when sequencing data are available. We were thus able to apply ASMC to 113,851 phased British samples from the UK Biobank, aiming to detect recent positive selection by identifying loci with unusually high density of very recent coalescence times. We detected 12 genome-wide significant signals, including 6 loci with previous evidence of positive selection and 6 novel loci, consistent with coalescent simulations showing that our approach is well-powered to detect recent positive selection. We also applied ASMC to sequencing data from 498 Dutch individuals (Genome of the Netherlands data set) to detect background selection at deeper time scales. We observed highly significant correlations between average coalescence time inferred by ASMC and other measures of background selection. We investigated whether this signal translated into an enrichment in disease and complex trait heritability by analyzing summary association statistics from 20 independent diseases and complex traits (average N=86k) using stratified LD score regression. Our background selection annotation based on average coalescence time was strongly enriched for heritability (p = 7×10−153) in a joint analysis conditioned on a broad set of functional annotations (including other background selection annotations), meta-analyzed across traits; SNPs in the top 20% of our annotation were 3.8x enriched for heritability compared to the bottom 20%. These results underscore the widespread effects of background selection on disease and complex trait heritability.