Human populations are genetically affected by their demographic history, which shapes the distribution of their functional genomic variation. However, the genetic impact of recent demography is debated. This issue has been studied in different populations, but never in North Africans, despite their relevant cultural and demographic diversity. In this study we address the question by analyzing new whole-exome sequences from two culturally different Tunisian populations, an isolated Amazigh population and a close non-isolated Arab-speaking population, focusing on the distribution of functional variation. Both populations present clear differences in their variant frequency distribution, in general and for putatively damaging variation. This suggests a relevant effect in the Amazigh population of genetic isolation, drift, and inbreeding, pointing to relaxed purifying selection. We also discover the enrichment in Imazighen of variation associated to specific diseases or phenotypic traits, but the scarce genetic and biomedical data in the region limits further interpretation. Our results show the genomic impact of recent demography and reveal a clear genetic differentiation probably related to culture. These findings highlight the importance of considering cultural and demographic heterogeneity within North Africa when defining population groups, and the need for more data to improve knowledge on the region’s health and disease landscape. Since the first known human presence in North Africa, dated to around 300,000 years old6, different pre-Neolithic cultures succeed each other in the archaeological record until the arrival of the Neolithic from the Middle East13, although genetic continuity in the region has been demonstrated from at least the Paleolithic15. In historical times, several Mediterranean populations sequentially arrived in North Africa 17, although their genetic and cultural impact in the region was limited3. In the seventh century, the Arabs conquered North Africa from the Middle East and, in contrast, had a strong influence in North African peoples that still lasts today, despite later arrivals of the Ottoman Empire and European colonial powers17. Present day North Africa exhibits thus a complex cultural landscape, with a rich diversity of languages, religions, and cultural practices. Its inhabitants have been traditionally divided in two main groups, Arabs and Berbers, a misnomer that traces back to Greco-Roman times  to designate the original inhabitants of the region19, who identify themselves as Amazigh (sing.)/Imazighen (pl.) (free people)20. Imazighen are considered the descendants of Paleolithic North Africans22, and although pre-Arabic incursions did not have a large demographic impact on them, the arrival of the Arabs and mainly the massive Bedouin immigration in the eleventh century had a strong cultural importance on Amazigh populations in language, religion, and customs17. Most North Africans incorporated the new culture, admixed with the newcomers, and began to identify themselves as Arabs, but others escaped this influence, receding to remote and isolated villages, where they maintained their original culture, language (Tamazight), and Amazigh identity26.North Africa has an advantageous location, joining the Mediterranean Sea, the Sahara Desert, and the Middle East. Because of this strategic position, North Africa has been the destination or pathway of many demographic movements involving surrounding regions, shaping the genomes of its inhabitants as a complex amalgam of different ancestral components28. In contrast, other Amazigh groups exhibit similar genetic diversity as non-Amazigh populations3, challenging the notion of Imazighen as a single homogeneous group. In this sense, although two gene-expression studies have been performed to clarify differences among Amazigh populations with different lifestyles in a region of Morocco29, to our knowledge no study has yet assessed the impact of the mentioned demographic events in exome-wide functional differences, characterizing the distribution of variants and its possible biomedical effects in North African populations, until now.Thus, several Amazigh groups are believed to have been affected by severe bottlenecks followed by geographic and genetic isolation since the Arab expansion, leading to small effective population sizes34. Specifically, most genetic load studies measure the mutational load, which is the genetic load caused by the accumulation of deleterious mutations relative to a mutation-free population. Also, purifying selection is predicted to be less efficient in removing deleterious alleles in small populations35. Different populations affected by similar events have been studied44, with contrasting conclusions. On one hand, some studies suggest that a decline in effective population size leads to a decrease in genetic variation, thus reducing the substrate for purifying selection to act upon and lowering its efficacy while increasing the effect of genetic drift. This would cause the accumulation of slightly deleterious alleles and homozygous derived genotypes in a higher proportion than in non-affected populations44. On the other hand, other studies showed that Europeans and sub-Saharan Africans carry the same amount of derived alleles, despite the severe out-of-Africa bottleneck experienced by Europeans, concluding that genetic load is not affected by recent size changes40. Part of the discrepancy lies in how the effect of selection was measured, as there is no universally accepted metric of genetic load or selection efficacy in human populations43. However, many studies have used summary statistics to approximate the mutational load under different allele dominance models: the average number of derived alleles per individual  to calculate the load assuming an additive model , and the average number of homozygous derived genotypes per individual (Nhom) when assuming a recessive model 38. It should be noted that, as mentioned, the arguments about the lack of effect of recent demography on genetic load are based mostly on comparisons between populations of European and sub-Saharan African ancestry44. Studies about populations with a much deeper or recent effect of such demographic events46 agree in the accumulation of deleterious alleles and homozygous genotypes, the increased recessive load, and a shift in variant distribution, with fewer rare and more common variants. They also point to relaxed purifying selection in such populations.Small populations, compared to larger ones, are predicted to be more affected by genetic drift , have lower genetic diversity and, as a consequence, higher genetic load, defined as the reduction in mean fitness in a population relative to the theoretical maximum fitnessIn the present study, we carried out the first analysis on the distribution of variants and its functional consequences in populations living in isolated locations in the complex demographic landscape of North Africa. We studied the Tunisian Amazigh population from two geographically isolated villages in the Tataouine governorate in southern Tunisia, comparing this group to a sample of non-Amazigh individuals from the city of Tunis. We studied high coverage whole-exome sequences from 18 Amazigh and 46 non-Amazigh individuals to test their population structure differences arising from their different demographic histories, and assess their genetic consequences focusing on the frequency distribution of functional variants and their biomedical implications. Our results show the sharp differences in variant distribution and their potential effects on health and disease between the two neighbor populations as a consequence of a distinct demographic background and highlight the significant need for more biomedical and functional studies in North Africa.3, and show both Tunisian groups clustering separately from other populations but with some internal differences between Imazighen and non-Imazighen  of the present sample set, we explored Ne history based on patterns of linkage disequilibrium (LD) with the whole-exome data47. The analysis of long-term Ne  confirms that both Tunisian Amazigh and Mozabite populations have significantly lower long-term Ne than the rest of populations . Long runs of homozygosity point to recent inbreeding and consanguinity, being created when identical haplotypes are inherited from each parent, i.e., when related individuals have descendants. Because of their low effective population size, this is usual in small and genetically homogeneous populations, which are also highly affected by genetic drift. On the other hand, shorter runs of homozygosity reflect a history of past rather than recent small population sizesAfter classifying ROHs by length categories, Tunisian Imazighen exhibit significantly higher per-individual number of ROHs than Tunisian non-Imazighen Fig. , and thee results ; nucleotide diversity for variable sites (πvar) , which is predicted to follow an opposite pattern from θπ36; Watterson’s estimator (θw); and Tajima’s D  less genetically diverse than the rest of North African and Middle Eastern populations analyzed, as we would expect considering their Ne and demographic history. Consistent with these findings, Tunisian Imazighen have a lower average number of segregating sites per individual than the rest of North Africans and Yemenis, and less population-specific segregating sites per individual than the rest of populations analyzed  of functional variation, which is significantly affected by demographic history52. We used the same categorization for deleteriousness of the variants proposed in53: “neutral” (GERP < 2), “moderate” (2 ≤ GERP < 4), “large” (4 ≤ GERP < 6), and “extreme” (GERP ≥ 6). The pattern observed in the general SFS is similar when dividing sites according to their GERP RS scores, with Tunisian Imazighen having fewer variants in the singleton category but more in the common frequency bins  were pooled together and divided into low-frequency variants (singletons and doubletons) and common variants Fig. g, we obsalleles), we observed a significant but very slight increase in the mutational load of both Tunisian populations in comparison with Yemenis (p < 0.001 for the first three GERP categories) and, in the “neutral” category (GERP < 2), with Europeans (p < 0.001).To have a richer view of the burden and distribution of deleterious variation in the studied populations, we estimated the mutational load across them Fig. . Under thom), mutational load is significantly higher in Tunisian Imazighen than in Tunisian non-Imazighen, Mozabites, Yemenis and Yoruba for the “neutral” (GERP < 2) and “moderate” (2 ≤ GERP < 4) GERP RS score categories (p < 0.001), and this is maintained in all comparisons with the exception of Yemenis in the “large” GERP RS score category (4 ≤ GERP < 6). The more deleterious GERP RS score category (GERP ≥ 6) contains much fewer variants than the others  as it assumes an extremely deleterious effect for variants, which means that even in small populations these variants are more likely to be removed by purging selection. This is probably causing the large confidence intervals in this category for both models.When assuming a recessive model  similar results were found . These findings suggest that a higher number of ROHs, as seen in Tunisian Imazighen . For most traits (including hypertension), we found missense deleterious variants with significantly higher frequencies in Tunisian Imazighen whose effects have not yet been studied, although they are found in genes related to the corresponding condition  catalog60 and with variants enriched in Imazighen included many genes associated with traits related to bone and anatomy , some to respiratory function , blood related traits  and other conditions such as metabolite levels, colorectal cancer, inflammatory bowel disease or type 2 diabetes , extracellular matrix (collagen related pathways), fatty acid metabolism, and rRNA processing, with a single IL-12-related pathway not linked with the previous groups (Supplementary Tables The ConsensusPathDB (CPDB) program63 analysis included, among others, cholesterol-related traits, coherent with the OMIM results, although only a number of pathways that can be grouped under the umbrella of olfactory function and signaling meet our top significance criterion (FDR < 0.05)  Resource2 found between SNPs in these peaks , and the first to use our approach in the region. We provide and characterize new high depth whole-exome sequences of North African Amazigh and Arab populations, making this the first whole-exome study with North Africa as the main region of study.The genetic functional consequences of demographic processes have been described and debated in several human population groups25. Hence, we show here the impact of recent demography on human genomes from a functional point of view. Our results confirm that these different population histories have resulted in relevant genetic differences which in turn might lead to distinct biomedical implications. The Amazigh group shows signs of genetic isolation, possibly after a relatively recent bottleneck, and followed by strong genetic drift and inbreeding. We also find evidence of admixture in Tunisian non-Imazighen in more relevant proportions than in the Imazighen group, which might also have an effect in the observed results. The estimations of effective population size and the diversity indexes confirm the particular demographic situation of the population and its sharp difference with the non-Amazigh individuals. As a consequence, Tunisian Imazighen carry significantly more and longer runs of homozygosity than their non-Amazigh neighbors, and a larger fraction of their variants are found at high frequencies, exhibiting thus depletion of rare variants in comparison with Tunisian non-Imazighen. Moreover, this pattern is maintained when assessing only variants with predicted deleterious consequences, which indicates that a higher proportion of variants with a potential damaging biomedical effect can reach a considerably higher frequency in the Amazigh population. Our approximation to mutational load is also in the line of these results, as Tunisian Imazighen exhibit a significantly higher load under a recessive model of dominance, i.e., when the summary statistic used to calculate the mutational load is the per-individual number of homozygous derived genotypes. It is important to note that genetic load cannot be directly calculated from whole-exome data41 and that the summary statistics approach cannot be read as a perfect reflection of real genetic load, but under this widely used approximation we observe results coherent with those arising from the runs of homozygosity and SFS analyses.The main aim of our study was to carry out a genomic characterization of a rural and isolated Amazigh population in the complex demographic context of North Africa (Tunisian Imazighen from two villages in the Tataouine governorate), focusing on variant distribution, and comparing it with a non-isolated non-Amazigh sample of individuals from the city of Tunis. These two populations have experienced different demographic histories, with the main divergence being relatively recent, in the context of the Arab expansion of North Africa (seventh–eleventh centuries)64. Here we approached the question with two main lines of analyses: (i) SFS and distribution of deleterious variation, and (ii) an approximation to mutational load. The SFS is affected by recent demographic history, and its comparison across populations can reveal differences in the effect and strength of genetic drift and natural selection50. As mentioned above, we observe in the SFS that Tunisian Imazighen exhibit a higher proportion of putatively deleterious alleles at high frequencies than non-Imazighen and other non-isolated populations. Concordant with these results and in agreement with previous evidence37, the approximation to mutational load under a recessive model (i.e. the ratio of homozygous derived genotypes) also shows that the Amazigh population exhibit a higher accumulation of homozygous derived genotypes across different deleterious categories. Such differences suggest that, at least under a recessive model, purifying selection may have had a lower impact in removing possibly damaging variation, allowing for this to reach higher frequencies in the isolated population. This is probably a result of Tunisian Imazighen having a much larger effect of genetic drift, an evolutionary force with opposite effect to purifying selection67. The increased effect of genetic drift leads to lower diversity, thus reducing the substrate for purifying selection to act and decreasing its efficiency68. Most studies framed in the debate on the relaxation of purifying selection after a bottleneck or isolation have focused on the out-of-Africa bottleneck and the comparison between European and sub-Saharan African populations44. In these cases, the time passed since the event and the growth of European populations may impinge on the actual effect of the bottleneck. The isolation in Tunisian Imazighen, in contrast, is much more recent and lacks the expansion of European populations which makes it difficult to compare with the out-of-Africa situation. Our results are concordant with other studies in populations with comparable historical isolation or extreme bottlenecks like the Inuit36, the Finns45, or the French-Canadians46, suggesting a possible relaxation of purifying selection in populations affected by such events.These findings lead to the debate of the efficiency of purifying selection in isolated or bottlenecked populations such as the Tunisian Imazighen. Whether selection is affected by changes in population size has been consistently studied and debated over the years74. This is because these populations have high genetic and environmental homogeneity, and variants involved in specific diseases and quantitative traits are sometimes present at higher frequencies than in other populations, increasing the power for association studies. Because of the observed variant distribution of Tunisian Imazighen and the high number of potentially damaging variants at high frequencies, we believe that they constitute a suitable candidate for that type of analysis in the complex context of North Africa. This would provide deeper knowledge on the disease risk and disease prevalence pattern in North Africa, which would have direct beneficial consequences on the health of its inhabitants, while also having the potential to discover new variant associations with impact outside of North African borders, given the extensive gene flow from North Africa to its surroundings76.Population isolates have been widely studied for their potential in mapping and identifying disease- and trait-related genes and variants80. This makes us cautious about the interpretation of this specific result. Taken together, our results indicate that the genetic differences between Tunisian Amazigh and Tunisian non-Amazigh populations could also be the underlying cause of differences in their health and disease profile, i.e., that the particular genetic structure and variant distribution in Tunisian Imazighen could have biomedical consequences different from those of the urban non-Imazighen population. Other non-genetic factors, such as social factors, could also be involved in biomedical differences between these populations, but further data would be needed to fully cover this subject.To assess the possible specific biomedical implications of the observed particular variant distribution in Tunisian Imazighen, we have conducted enrichment analyses examining different sets of variants with the highest frequency difference between both Tunisian populations, specifically those with higher frequencies in Imazighen. Our results revealed the enrichment of different traits in Tunisian Imazighen, with a higher presence of those related to bones and anatomy, respiratory and blood functions, and the olfactory function. This last case is driven by the presence of olfactory receptors and related genes, which vary substantially among populations or even individuals as a result of genetic drift, selective pressures and, more importantly, pseudogenization processes58. More (and more specific) biomedical studies are needed to discern this matter. We believe our results can serve as a starting point for future studies targeting this question that will help to continue describing the biomedical landscape of North Africa, including the differences between its heterogeneous population groups.The main barrier that challenges genetic studies in North Africa is the scarce genetic data available. Despite our interesting findings on conditions, traits, and biological pathways that could present relevant differences between Imazighen and non-Imazighen in Tunisia, there is an urgent need for more phenotypic and disease prevalence data in North African populations. The question remains on whether there is a higher prevalence, risk or drug response for the reported traits and diseases in Tunisian Imazighen as, because of the scarce data, we cannot link these results to previously published biomedical and phenotypic reports in North Africa, which often describe a general population with no attention to cultural differences. In this regard, we have found in our enrichment analyses several missense or even some stop-gain variants whose consequences have not been studied, and do not appear in the corresponding databases, which are clearly biased towards the more studied European-descent populations3, and reinforce the idea of a recent and possibly culture-driven differentiation of the two groups, rather than a difference in their historical origin. Thus, we see here how a socio-cultural event (the Arab expansion) has led to significant differences in the genomic configuration of two populations in a relatively small territory. We present a new perspective to the culture-genetics correlation debate in North African populations, compatible with previous evidence and more biomedically- or functionally-focused than the previous admixture-based studies. As other authors pointed3, not all Tamazight-speaking groups are isolated, and Imazighen are in fact a diverse group of populations with a common origin but different population histories. Nonetheless, some of them are more isolated, probably because of a different impact of Arabization. Our results show how a North African population that maintained an Amazigh identity and way of life has undergone distinct demographic processes not experienced by another population that identifies as Arab, and that these events have influenced their genetic architecture. As revealed in previous studies, genetics and cultural identity do not always go side by side in North Africa, but in the two populations studied in our work we see a clear genetic differentiation with possible socio-cultural causes. For this and other groups, isolation is a distinct and shared trait that differentiates them from Arabs, Arabized Imazighen, and urban Imazighen. Isolation and genetic drift are main drivers of population differentiation67, creating a continuous range of genetic diversity, and they have been of crucial importance in shaping the demographic landscape observed nowadays in North Africa. Isolation in North Africa should then be considered with the same importance as differential admixture and taken into account when defining population groups for biomedical studies. In the light of our results, we believe future studies should differentiate between these different groups of populations in North Africa. We believe more data on new Amazigh and Arab populations is needed, and more North African groups  should be studied following an approach similar to the one here presented, to go deeper in this question and to have a richer knowledge of the cultural and genetic diversity of North Africa, and the relations between them.Although exomes represent only a part of the genome, our population structure results are in agreement with those obtained with genome-wide dataWritten informed consent was obtained from all the volunteers and the present project has the corresponding IRB approval . All the methods were carried out in accordance with relevant guidelines and regulations.North African samples from two Tunisian populations were sequenced in the present study: Amazigh individuals from two villages in the Tataouine governorate in southern Tunisia (n = 20) that were pooled together because of their geographical closeness (~ 20 km apart), and non-Amazigh individuals from the city of Tunis (n = 55). Participants were healthy volunteers with appropriate informed consent. Amazigh individuals were Chela-speakers, a language in the Tamazight family, while non-Imazighen were Arab-speakers. Blood samples were collected for non-related individuals in these populations and whole-exome sequencing was performed with the Agilent SureSelect Human All Exon V6 capture kit.81. Read quality was assessed before and after trimming using the fastqc online tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). We followed the Genome Analysis Toolkit (GATK) Best Practices recommendations82 to obtain variant calls from FASTQ files. Read pairs were mapped to the GRCh37 human reference genome using the Burrows-Wheeler Aligner (BWA) version 0.7.1583. Mapped reads were merged, and PCR duplicates were removed using the MarkDuplicates tool from Picard version 2.18 (https://broadinstitute.github.io/picard/). Coverage and mapping quality control was performed on mapped files (BAM) using GATK version 3.784 before and after removing duplicates. Batch effect was assessed and discarded in this step. Indel realignment and base quality score recalibration were performed using the GATK as well , and SNP and indel discovery were performed with the GATK HaplotypeCaller tool. At this point, we included different worldwide populations to the dataset; 50 individuals were randomly selected from the following populations in the 1000 Genomes Project85 panel: Yoruba (YRI) from Western sub-Saharan Africa, Luhya (LWK) from Eastern sub-Saharan Africa, Utah residents (CEPH) with Northern and Western European ancestry (CEU), Han Chinese in Beijing (CHB), and Indian Telugu in the UK (ITU). We also included eight Mozabite individuals from38, to incorporate another North African and Amazigh population, and 47 Yemeni individuals from86, as a Middle Eastern proxy. The available Mozabite samples were originally mapped to the hg19 human reference genome, which slightly varies from GRCh37 mainly in some labels. We used a custom modified version of the hg19 reference to match the GRCh37 reference for the processing of Mozabite samples. Yemeni samples were whole-genome samples mapped to the GRCh38 human reference genome, so, after the previously described BAM filtering and processing was performed, BAM files were lifted over to the GRCh37 reference using CrossMap87. SNPs and indels were called with the HaplotypeCaller tool from GATK from the BAM files of all samples, including the previously published and the new sequences from the present study. The individual variant files from HaplotypeCaller for all samples were combined with the GenotypeGVCFs tool to obtain a single variant call format (VCF) file for the complete dataset. The intersection of the exome variants from the Yemeni whole genomes was automatically performed in the joint variant calling. SNPs and indels were recalibrated with the VariantRecalibrator and ApplyRecalibration GATK tools. Based on guidelines in37, we used VCFtools 0.1.1488 to exclude those variants that: (1) did not pass the VCFtools internal filters (not labelled as “PASS”), (2) were indels, (3) were located in sex chromosomes, (4) were not biallelic, (5) were monomorphic in our dataset, (6) had a depth of coverage < 5×, had a genome quality (GQ) < 20, (7) presented missingness > 5%, and (8) presented a Hardy Weinberg test value p < 10–3 in at least one of the populations. Low-quality samples were removed according to the following criteria: we required at least 30 × mean coverage (4 samples removed), 85% positions at a minimum 5 × coverage in the BAM file (no sample removed), a total genotype missingness lower than 10% (13 samples removed), and heterozygosity levels within the range of 4 standard deviations lower and higher than the corresponding population average (no samples removed). VCF quality controls were performed before and after applying these filters.Raw sequencing data was stored in FASTQ files, which were then trimmed to remove adaptor sequences using Trimmomatic89 to filter out variants with minor allele frequencies lower than 0.01, and those at linkage disequilibrium calculated using sliding windows of 50 kb with a step size of 5 SNPs, and a square correlation coefficient (r2) threshold of 0.5. With this pruned variant set we assessed relatedness using KING90 and removed individuals so that no third-degree relationship or closer remained in the dataset. A quality control was performed as a final step of the data curation. The final dataset contained 319,297 SNPs with a mean coverage of 56 × and a mean missingness of 5.8%. Ti/Tv ratio was 2.8, an expected value for high quality exome variant datasets91. As described below, for the analyses that required determination of the ancestral allele, the size of this variant set was slightly reduced due to unknown ancestral state in some sites. The final sample count for each population after filtering was: 18 Amazigh Tunisians, 46 non-Amazigh Tunisians, 49 YRI, 48 LWK, 48 CEU, 50 CHB, 50 ITU, 31 Yemenis and 8 Mozabites.Although sampling was performed to avoid related individuals, an estimation of relatedness was performed. We used PLINK 2.092. ADMIXTURE 1.393 was applied in unsupervised mode to explore ancestry patterns. The number of ancestral clusters explored ranged from K = 2 to K = 10 with 50 independent runs for each K using a different random seed in each run. The cross-validation error was assessed for each run and mean values were calculated to determine the range with minimum error. To identify common modes among the different runs for each K and to visualize and plot the results, we used pong in greedy mode94. For both PCA and ADMIXTURE, data was first pruned for linkage disequilibrium with PLINK 2.089 using the same parameters as described above for the relatedness estimation and resulting in a dataset of 260,952 variants. PLINK 2.0 and VCFtools 0.1.1488 were used to obtain intermediate files for the described analyses.Principal component analysis (PCA) was performed with the SmartPCA tool from the EIGENSTRAT stratification correction method implemented in the EIGENSOFT software package version 6.0.1Pairwise Fst values between population pairs of a subset of populations including North Africans (Tunisians and Mozabites) and proxies for the surrounding regions  were computed with VCFtools 0.1.14. Comparisons were made first using all individuals in each population and then using only 8 randomly selected individuals per population, a threshold set by the 8 Mozabite samples available.37, which consist in using the 6-EPO multi-alignment from Ensembl Compara version 59. Sites where the ancestral allele was unknown were removed from the dataset for all analysis apart from those described in the population structure analyses section above, resulting in a dataset of 306,942 SNPs for further analysis.The ancestral and derived state of alleles at each site in our dataset was determined following the approach used ine) for all populations in the dataset was performed using the R-package NeON95, which bases the Ne calculation in LD patterns and was previously used in whole-exome data in47. We calculated the long-term effective population size, which is the harmonic mean of the Ne of the population sizes along the generations in the past, using each chromosome as replicate to calculate the mean (percentile 50th) and the confidence intervals (percentiles 5th and 95th), as provided by the Ne_CI function of the NeON package.Inference of effective population sizes , Watterson’s estimator (θw) and Tajima’s D with custom scripts based on97, and the nucleotide diversity for variable sites (πvar) based on36. Confidence intervals and statistical significance were computed by bootstrapping by site 1000 times dividing in each iteration the exome in 1000 blocks and taking 1000 random blocks allowing resampling, an approach that allows to consider the possible variance introduced by demographic processes98. Confidence intervals were set as the 0.025 and 0.975 quantiles of the bootstrap distribution. Statistical significance of the differences between different population pairs of interest was tested using t-tests.Diversity indexes for all populations in the dataset were calculated based on the SFS for the synonymous sites following the same approach as in88 to output the allele counts at each site in each population.Segregating sites and private segregating sites were directly calculated from the VCF dataset, using VCFtools 0.1.1452. GERP RS scores were collected from the Combined Annotation Dependent Depletion (CADD) online tool100. For SFS and other analyses performed at different levels of predicted deleteriousness, we divided variants according to GERP RS score categories using the same categorization as in53. We also used two other independent methods to assess deleteriousness of the variants: PolyPhen-2101 and CADD scores100. PolyPhen-2 scores were obtained from the Ensembl Variant Effect Predictor54, and CADD scores, from the CADD online tool. To categorize PolyPhen-2 scores we used the proposed categorization in the Ensembl Variant Effect Predictor and left the variants labelled as “unknown” out for the analysis using this categorization. For CADD scores we followed the recommendations in the CADD online site (https://cadd.gs.washington.edu/info) and the Ensembl Variant Effect Predictor. For analyses requiring genomic effect annotation (i.e. synonymous or missense variants), we used the Ensembl Variant Effect Predictor to annotate the variants.Deleteriousness of each variant was assessed using GERP RS scores, which stands for genomic evolutionary rate profile rejected substitution and is a method to predict the effect of allele substitutions based on sequence conservation89 and following the same approach as in47, which is optimized for whole-exome sequence analysis. Data was first pruned for linkage disequilibrium, which was detected using sliding windows of 50 kb with a step size of 5 SNPs, and a square correlation coefficient . The minimum ROH length to be annotated was set to 1 Mb. The rest of the PLINK parameters were set as default. We calculated the per-individual total number or ROHs, the total length , and the average length. To discern between the different demographic interpretations of ROHs depending on their length, we calculated the total number and total length of ROHs in three different length categories: 1–2.5 Mb, 2.5–5 Mb, and > 5 Mb. Statistical significance of the differences between different population pairs of interest was tested using t-tests.Runs of homozygosity (ROH) were detected at individual level using PLINK 2.053 and selected the three categories that predict SNPs to be deleterious . Allele counts needed to select homozygous sites were calculated for each individual using VCFtools 0.1.1488. Ratios were calculated per individual and statistical significance of the differences between categories within each population and GERP RS score category were assessed with t-tests.We also calculated the ratio of missense homozygous derived genotypes to synonymous homozygous derived genotypes in the following different exomic regions: inside ROH tracts, outside ROH tracts, and inside the regions occupied by ROHs 1–2.5 Mb, 2.5–5 Mb and > 5 Mb respectively, resulting in 5 different regions. In each region, we calculated the mentioned ratio selecting only missense deleterious sites in different ranges of GERP RS scores. We used the same categorization as in88 to output the allele counts for each individual population, thinning population sizes to 8 randomly selected individuals . Derived allele counts per population were then grouped to have the number of sites at each derived allele count bin, the two fixed categories were removed, and the remaining were normalized over the total number of remaining sites. Resulting densities were plotted to visualize the SFS.Unfolded site frequency spectrum (SFS) was computed from the ancestral-state-annotated VCF file, using VCFtools 0.1.14SFSs were performed using all sites and also using only variants in each different GERP RS score, CADD score and PolyPhen-2 score categories.To test the statistical significance of the apparent increase in the difference between densities in the SFS between the two Tunisian populations as the GERP RS score increases, we bootstrapped 1000 SFS taking each time 18 random individuals from each Tunisian population  and allowing resampling of individuals. For each replicate, the SFS was computed. Then we calculated the difference in density for the first 10 categories of the SFS and performed t-tests to test determine the statistical significance of the density differences between sequential categories , i.e., to test if the density difference significantly increases with increasing GERP RS scores.For analyses performed only in the two Tunisian populations, we used a dataset that did not contain Yemeni and Mozabite samples, allowing us to filter out all missing sites maintaining a substantial number of sites  and increasing coverage to 63×.In order to compare the fraction of deleterious variants in low and common frequencies between Tunisian populations we grouped all sites with at least one derived allele that had a deleterious GERP RS score (i.e. GERP ≥ 2) and divided them between low frequency sites (singletons and doubletons) and common sites (from tripletons to higher frequencies). 95% confidence intervals were calculated using the Wald method. This was repeated with PolyPhen-2 and CADD scores.alleles) and the ratio of homozygous derived genotypes (Nhom). Ratios were calculated between different population pairs and through the four GERP RS score categories. Nalleles and Nhom were calculated directly from the VCF file using VCFtools 0.1.1488. Statistical significance and confidence intervals were calculated by bootstrapping 1000 times, dividing the variant dataset in 1000 blocks and taking 1000 random blocks at each iteration allowing resampling, to take into account demographic variance as mentioned above. Confidence intervals were set as the 0.025 and 0.975 quantiles of the bootstrap distribution. For this analysis, and following the approach in37, we required a p < 0.001 to declare significance. This analysis was also performed selecting, for variants with GERP ≥ 2, only those labelled as missense in the Ensembl Variant Effect Predictor54, and for those with GERP < 2, only those labelled as synonymous. The load analysis was replicated with PolyPhen-2 and CADD scores instead of GERP RS scores.We calculated the mutational load, which is the genetic load measured as the accumulation of deleterious mutations. To estimate the mutational load differences between population pairs we computed two widely used summary statistics based on individual genotypic data: the ratio of per-individual number of derived alleles  first variants labelled as missense by the Ensembl Variant Effect Predictor54, (ii) then those with the corresponding GERP RS score  or PolyPhen (“possibly damaging” and “probably damaging”) category, and (iii) finally those SNPs with Fst values in the top 5% of the Fst value distribution for the given set. Then, a sixth subset was created by merging the variants resulting from the first two filters in the previous five datasets and selecting the variants with top 5% Fst values from those merged. This sixth dataset is referred in the tables as “All categories” and represents the top 5% Fst-value missense SNPs that are predicted to be deleterious by GERP RS score  or PolyPhen . As a final filtering step before proceeding with the over-representation analysis, we selected from each dataset only those variants with higher derived allele frequency in Tunisian Imazighen than in Tunisian non-Imazighen, filtering out those with higher frequency in Tunisian non-Imazighen. Because these variants are not present in these databases (with extremely few exceptions), we used their corresponding genes as an approximation.The goal of enrichment analyses is to detect the presence of variants or genes related to specific diseases or traits at a higher frequency than what would be expected by chance. This analysis was performed only in Tunisian populations, and Fst values for each SNP between these two populations calculated with VCFtools 0.1.1459 and the Gene Ontology (GO) Resource63 were explored using the online tool WebGestalt or WEB-based GEne SeT AnaLysis Toolkit103, and uploading the complete list of genes in our dataset as reference. For these, the first criteria for a trait to be considered significant was to have an FDR < 0.05, as recommended by WebGestalt. We incorporated two extra significance criteria, in descending order of relevance, to classify the remaining results: traits with p < 0.05 and an enrichment ratio (the number of observed genes divided by the number of expected genes) higher than the mean ratio of the analysis for the corresponding database, and remaining traits that did not pass the two first criteria thresholds but still presented p-values < 0.05. The analysis in the ConsensusPathDB (CPDB)61 was performed using the over-representation analysis tool in the CPDB online site and uploading the complete list of genes in our dataset as reference and selecting the pathway option. In this case we followed the same criteria as in47 for significance of the enrichment, which is to require a q < 0.05, and included a second separate group of enriched traits that have q > 0.05 but p < 0.05. To explore the GWAS catalog60 we downloaded the full catalog associations and extracted a list of genes with associated traits, that was then matched to each of our gene lists counting the number of observed genes associated with each GWAS catalog trait. During the analysis we detected that some of these traits were also the ones appearing at highest frequency in the catalog. To tackle this question and as significance criteria, we conducted a bootstrap analysis for each of our gene subsets. The analysis consisted in performing 1000 iterations taking each time a number of randomly selected genes equal to the number of genes in the corresponding gene subset from (A) the complete list of genes in the GWAS catalog, and (B) the complete list of genes in our dataset. For these resampled gene sets we ran again the enrichment analysis, counting each time the number of appearances of each trait and resulting in 1000 frequency-of-appearance values for each trait. (A) and (B) gave extremely similar results, so we show here the results of (A). For each trait frequency-of-appearance distribution, we calculated the quantile 95 (Q95), which is the cut point value at the 95% of the bootstrap distribution, and only reported a trait as significance if the observed frequency of appearance was equal or higher than the Q95 of the bootstrap distribution for the given trait in the corresponding gene subset .Each gene set was used to look for enriched diseases, traits or biological pathways in four different public databases. We followed different criteria to select significantly enriched traits depending on the database. The Online Mendelian Inheritance in Man (OMIM) catalog88. Variants in Fst peaks were also explored and those in genes related to bone diseases or conditions  were reported in Table 85. Finally, SNPs with high frequency differences and high GERP RS score load were also selected and their corresponding genes were matched with the GWAS catalog to explore its associations.For the genes appearing in the OMIM analysis, we checked the frequencies of associated variants in the general dataset for variants appearing in the OMIM and other variants of our dataset that were interesting because of their genomic or clinical consequences and their frequency differences between Tunisian populations. Allele frequencies were checked with VCFtools 0.1.14Supplementary Information 1.Supplementary Information 2.


Conilurus penicillatus) to compare patterns of genomic diversity and differentiation across the landscape and investigate standardised hierarchical genomic diversity metrics to better understand brush-tailed rabbit-rat population genomic structure. We found strong population structuring, with high levels of differentiation between populations (FST = 0.21–0.78). Two distinct genomic lineages between the Tiwi Islands and mainland are also present. Prioritisation analysis showed that one population in both lineages would need to be conserved to retain at least ~80% of alleles for the species. Analysis of standardised genomic diversity metrics showed that approximately half of the total diversity occurs among lineages . We suggest that a focus on conserving remnant island populations may not be appropriate for the preservation of species-level genomic diversity and adaptive potential, as these populations represent a small component of the total diversity and a narrow subset of the environmental conditions in which the species occurs. We also highlight the importance of considering both genomic and ecological differentiation between source and receiving populations when considering translocations for conservation purposes.Conservation management is improved by incorporating information about the spatial distribution of population genetic diversity into planning strategies. Northern Australia is the location of some of the world’s most severe ongoing declines of endemic mammal species, yet we have little genetic information from this regional mammal assemblage to inform a genetic perspective on conservation assessment and planning. We used next-generation sequencing data from remnant populations of the threatened brush-tailed rabbit-rat ( To conserve threatened species, conservation biologists have historically been required to make decisions with basic, and often inadequate, information Soulé . We now The spatial distribution of genetic diversity is largely driven by patterns of gene flow, genetic drift and local adaptation , and the extinct white-footed rabbit-rat  and the Capricorn rabbit-rat (C. capricornensis)  and a single PNG subspecies (C. p. randi)  geographic distances between some mainland populations. We expect that such findings will (1) help confirm the presence of two Australian subspecies of brush-tailed rabbit-rats, (2) have consequences for the conservation management of the brush-tailed rabbit-rat as well as other species in the region and (3) showcase how the success or failure of conservation efforts to protect this flagship species is likely to inform future management of small- to medium-sized mammals in northern Australia.We obtained 55 tissue samples from multiple research and/or monitoring groups who had trapped mammals, primarily between 2005 and 2019. These samples were typically collected during fieldwork, with a small section of ear tissue taken from live animals and placed into a vial containing either ethanol or dimethyl sulfoxide solution. In addition, six liver samples were obtained from the Australian Biological Tissue Collection of the South Australian Museum, which stored specimens that were collected in 1981/82 from the Mitchell Plateau in Western Australia. Together, this produced a total of 61 tissue samples. These samples covered a large proportion of the species’ extant range Fig.  and no sTissue samples were prepared and extracted in plate format (Qiagen DNeasy 96 Blood & Tissue Kit) following the standard protocol, with lysis consisting of 2 h incubation at 56 °C followed by overnight incubation at 37 °C. Extractions were then quantified using a broad-range dsDNA assay kit for the Qubit Fluorometer 3.0 and normalised to 200 ng in 25 µL for library preparation. A single 96-well plate, consisting of our 61 samples and 34 technical replicates, was sent to the Australian Genome Research Facility (AGRF) in Melbourne, Victoria, for double-digest restriction-associated DNA (ddRAD) sequencing  using the bwa (v0.7.17) mem algorithm , were genotyped in at least 50% of individuals, had a minimum of five reads per locus per sample and had fewer than 1000 reads per locus per sample. Genotypes were called using posterior probabilities assuming a uniform prior, with a posterior probability threshold of at least 0.98 (using GATK genotype likelihoods). This retained a total of 225,646 SNPs, with a mean read depth per SNP per sample of 33.9, and a median read depth per SNP per sample of 22.7. The SNP by sample matrix was then read into the statistical analysis software R v4.0.0 (R Core Team HO) > 0.5. To remove SNPs in linkage disequilibrium, we used the snpgdsLDpruning function of the ‘SNPRelate’ package  falls to less than 0.4  via the rithm Li , with thdf2genind and dist.genpop functions in the ‘adegenet’  using the genet.dist function of the ‘hierfstat’ package (Goudet G″ST) via the pairwise_Gst_Hedrick function of the ‘mmod’ package  the snpgdsIBDMLE function of the ‘SNPRelate’ package, and (2) the beta.dosage function of the ‘hierfstat’ package. Pairwise coefficients with values of either >0.125 (SNPRelate) or >0.25 (hierfstat) were considered closely related.We visualised population genetic structure using a principal coordinate plot. Pairwise population Nei’s genetic distances were calculated using the  Jombart  package.e Goudet , as welle Winter . To deteK) of ancestral populations, allowing for the independent investigation of values for K that have low cross-entropy metrics  was investigated using spatial autocorrelation of multilocus genotypes, plotted on a correlogram. Pairwise geographic distances between individuals were calculated from latitude and longitude observations for each sample via the e Vavrek . Correlaage Dyer  with 999A), the number of effective alleles (AE), observed heterozygosity (HO), expected heterozygosity (HE), Wright’s inbreeding coefficient (FIS) and the locus polymorphic index (PE). All calculations were made using the ‘gstudio’ package , the diversity among lineages/regions (δ), the within-region/lineage diversity (σ), the among-population diversity within each region/lineage (β) and the within-population diversity within each region/lineage (α). As this function does not yet allow for missing data, we removed any SNPs that were missing genotypes for any individual, retaining 6569 SNPs.To investigate the partitioning of genetic diversity among and within geographic regions and individual populations, we used the allel.rich function of the ‘PopGenReport’ package (Adamack and Gruber t)—AR(− i)/(AR(t) − 1), where AR(t) is total allelic richness and AR(−i) is allelic richness over all populations excluding the one in question.We conducted a set of analyses to inform strategies for the conservation of genetic diversity in the species. First, we used the approach of Petit et al.  to quantmarxan , and two topographic variables, elevation  and ruggedness  between the Tiwi Islands (Melville and Bathurst populations) and all other populations, including the nearby Cobourg Peninsula (Table FST = 0.29). The first axis of the principal coordinate plot accounted for 87.1% of the total variation and separated the Tiwi Islands from the remaining populations, with the mainland and Inglis Island populations spreading across the second axis  in the far west separating from the two Cobourg Peninsula populations, with the individuals on Inglis Island (in the far east of the species distribution) showing a mix of Cobourg and Kimberley ancestry  and lowest in the Bathurst Island population (AE = 1.09). A similar trend was also present for expected heterozygosity, observed heterozygosity, Wright’s inbreeding coefficient and the locus polymorphic index. The mean observed heterozygosity across populations was 0.08, with a value of 0.06 for the Bathurst Island and Mitchell Plateau populations, and a value of 0.09 for the Cobourg East and Melville Island populations. One population, Melville Island, showed a much higher level of homozygote excess (FIS = 0.31) compared to the other populations (FIS = 0.02–0.03). While heterozygosity increased for the Melville Island population when closely related individuals were removed, the high FIS did not dissipate . The Tiwi Islands lineage (σ = 0.117) contained more diversity than the mainland lineage (σ = 0.099), although Bartlett’s test for homogenous within-region diversity suggested that the difference was not significant (P = 0.378). However, the mainland lineage (β = 0.014) did have a significantly (P = 0.001) greater level of among-population diversity (Mitchell Plateau vs Cobourg East) than the Tiwi Islands lineage . Within populations, Cobourg East (α = 0.094) had a greater level of diversity than the Mitchell Plateau (α = 0.06), and Melville Island (α = 0.121) had a greater level of diversity than Bathurst Island (α = 0.056).The QDiver analysis allowed us to partition genetic diversity into hierarchical strata via a standardised diversity metric Table , allowinQuantifying the contribution that each a priori population grouping made to the overall allelic richness of the species showed that Melville Island had the greatest unique contribution of alleles (0.12) Table , followemarxan suggested that Cobourg (52% of iterations) was the single most cost-effective region to conserve for allelic diversity in the brush-tailed rabbit-rat , although the conservation benefit from Melville Island (48% of iterations) was very similar   Fig. . If two es) Fig. . Conserv−5) than the mainland lineage (3.53 × 10−2), suggesting that the Tiwi Island individuals persist in a much more restricted set of environmental conditions. We also found no overlap in niche hypervolumes between the two lineages, based on either the Jaccard or Sorenson similarity metrics. This indicates that the environmental conditions occupied by the two lineages are differentiated, with neither niche being a complete or partial subset of the other. Pairwise plots of each niche dimension showed that the niche centroid of mean annual rainfall is substantially different between the two lineages , Butler’s dunnart (Sminthopsis butleri), golden bandicoot (Isoodon auratus) and northern quoll  are now scarce or extinct on the mainland, while remaining extant on one or more islands or archipelago systems. Populations on these islands may be highly differentiated lineages  attempt to translocate wild-caught brush-tailed rabbit-rats from the Cobourg Peninsula to Field Island , despite the species not known to have been on the island historically. However, this translocation did not go ahead as the desired number of individuals (30) to be translocated could not be captured. An earlier attempt to reintroduce brush-tailed rabbit-rats into the Darwin region was also unsuccessful, with four translocation sites  failing to establish viable populations. Future attempts to translocate or reintroduce this species to Kakadu and other parts of the mainland need to be carefully evaluated, including considering whether individuals from the Cobourg Peninsula are ecologically and genetically most appropriate. While Cobourg populations are likely to be much more genetically similar to the original Kakadu populations than those of the Tiwi Islands, our spatial autocorrelation analysis showed that allele frequencies in individuals separated by more than about 150 km are not significantly correlated. It is possible that populations in the Kimberley region, despite its greater geographic distance, may have historically been less differentiated from the Kakadu region than the Cobourg Peninsula. Investigating the ecological niche of historically occupied locations may help to identify populations that are likely to be most suited/adapted to the environmental conditions in Kakadu National Park. Similarly, prior to any reintroduction operation, the conditions in the receiving environment need to be more generally suitable for the persistence of this species, and this requires concerted effort to manage the environment appropriately. This is most likely to involve ongoing suppression of feral cat and herbivore populations, as well as maintaining suitable fire regimes (i.e. reducing the frequency of high-intensity fires).Using niche hypervolume analysis, we showed that the niche conditions occupied by the two major lineages of the brush-tailed rabbit-rat are essentially independent of one another. The Tiwi Islands lineage of the species occurs in a highly restricted climatic and topographic set of conditions that are not found across most or all of the species remaining distributed. While we did not investigate the genomic and niche similarity of the Groote Eylandt population, due to a lack of tissue samples from the island, further work on this population would be highly informative. Populations on Groote Eylandt may, similar to the Tiwi Islands, show high levels of genomic differentiation from the mainland, and the environmental conditions occupied on the island may be more similar to the Tiwi Islands than the remaining occupied mainland sites. Comparison of the historical and contemporary niche conditions of the brush-tailed rabbit-rat shows that, at least in the Northern Territory region, the species has broadly contracted towards areas of mild temperatures, low topographic heterogeneity and high moisture availability (von Takach et al. A range of small mammal species in northern Australia are undergoing severe declines. We have demonstrated that one of these species, the brush-tailed rabbit-rat, has highly distinct lineages between the Tiwi Islands and other populations, and management activities need to aim to conserve multiple populations of the species if we are to conserve its adaptive capacity. This may also be the case for a range of threatened mammals that exhibit similar phylogeographic patterns, and research into the population genomic diversity of other northern Australian species will help to clarify this. Our findings highlight that reliance on islands as natural refuges for the conservation of Australia’s threatened mammal species is likely inappropriate for the long-term conservation of genetic diversity and adaptive capacity. In future decades, if we are able to successfully mitigate both the proximate and ultimate drivers of decline in northern Australia, there will be a need to repopulate vast areas of many species’ former ranges, and understanding patterns of population genomic diversity in the manner presented here will be critical to this management goal. We hope this study emphasises the need for active long-term management of habitats and threatening processes to improve population trajectories across the remaining populations of declining species.Supplemental Material


Lack of diversity in human genomics limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities. To map genetic effects on gene regulation in the underrepresented Indonesian population, we have integrated genotype, gene expression, and CpG methylation data from 115 participants across three island populations that capture the major sources of genomic diversity in the region. In a comparison with European datasets, we identify eQTLs shared between Indonesia and Europe as well as population-specific eQTLs that exhibit differences in allele frequencies and/or overall expression levels between populations. By combining local ancestry and archaic introgression inference with eQTLs and methylQTLs, we identify regulatory loci driven by modern Papuan ancestry as well as introgressed Denisovan and Neanderthal variation. GWAS colocalization connects QTLs detected here to hematological traits, and further comparison with European datasets reflects the poor overall transferability of GWAS statistics across diverse populations. Our findings illustrate how population-specific genetic architecture, local ancestry, and archaic introgression drive variation in gene regulation across genetically distinct and in admixed populations and highlight the need for performing association studies on non-European populations.  Genetically and geographically structured, with a genomic cline of Asian to Papuan ancestry stretching from west to east,All samples were obtained from adult human male subjects. For full information about the new and published samples used in this study, refer to Here, we report two new genomic datasets: (1) 42 samples genotyped with the Illumina Infinium Omni2.5-8 v1.3 BeadChip array, including five Korowai samples from New Guinea, 18 samples from Mentawai, western Indonesia, and 19 samples from Sumba, eastern Indonesia and (2) complete genomes for 70 samples, including 11 Korowai, 30 Mentawai, and 29 Sumba samples.Whole blood DNA from all individuals was extracted with Gentra Puregene for human whole blood kit (QIAGEN) and MagAttract HMW DNA kit (QIAGEN) according to the manufacturer’s instructions. Approximately 1.3 μg of DNA from each of the 73 individuals were sent to Garvan and sequenced with TruSeq Nano v2.5 to an expected mean depth of 30×.,The newly generated genome sequences were processed closely following a previously described protocolThe published data included seven Korowai and ten Mentawai samples, however, two first-degree relatives (MTW-024 and MTW-066) were excluded from further analysis.Next, modern human multi-sample VCF files were merged with two archaic individuals: DenisovanApproximately 1 μg of DNA from each of 42 individuals were sent to Macrogen for genotyping on the Illumina Infinium Omni2.5-8 v1.3 BeadChip array. Samples were processed according to the manufacturer’s instructions. Array data was processed in PLINK v1.9.Array data were imputed with Beagle v5.12 > 0.95 . We extracted these positions from the complete genomes from Korowai, Mentawai, and Sumba (n = 84) to produce a new combined SNP set made of imputed and WGS data. We filtered these data to retain SNPs with a proportion of missing data < 0.3 and minor allele frequency (MAF) > 0.05, which resulted in 4,077,164 variants. Imputed genotypes were further filtered to retain genotypes with genotype probability (GP) > 0.90.We filtered variant sites with bcftools and VCFtoolsRNA sequencing and data processing were carried out as previously described.FASTQ read files underwent quality control with FastQC v0.11.5 , and leaminfi v1.30.0lumi v2.36.0.1 μg of DNA from each sample was shipped to Macrogen, bisulfite-converted, and hybridized to Illumina EPIC BeadChips according to the manufacturer’s instructions. We randomized samples with respect to village and island across two array batches, and three samples were processed on both batches to control for technical variation . DNA metWe used ChromoPainter v2First, East Asian and Papuan reference panels were generated to assign local genomic ancestry in target samples. We selected unadmixed East Asian and Papuan samples by running ADMIXTURE v1.3(1)We performed the initial CP run with ten expectation-maximization steps to estimate prior copying probabilities for each individual and chromosome separately.(2)Estimated prior copying probabilities were averaged across the genome for each individual. The main CP run was performed with a recombination scaling constant and global mutation probability from the first step and genome-wide average prior copying probability.(3)Either East Asian or Papuan ancestry was then assigned to individual SNPs with a probability threshold of 0.85. Unknown ancestry was assigned to SNPs with intermediate copying probability.Next, we painted each of 84 target genomes individually with the East Asian and Papuan reference panels as donors. We used the following protocol.Archaic and removing those intersecting CP Denisovan haplotypes.We defined the high-confidence Denisovan archaic haplotypes as outlined previouslySNPRelate v1.18.1.Principal-component analysis (PCA) of the genotype data was carried out with the R package cis were obtained from 10,000 permutations. We calculated false discovery rate (FDR)-adjusted p values to adjust for multiple phenotypes tested. Significant associations were selected with an FDR-adjusted p value threshold of 0.01. Nominal p values for all sites within the cis-window were obtained with the QTLtools nominal pass. QTL power calculations were carried out with the R package powerEQTL v0.1.7.Variant effects on gene expression and CpG methylation were identified by linear regression as implemented in QTLtools.annotatr v1.10.0.TxDb.Hsapiens.UCSC.hg19.knownGene R package v3.2.2  terms and canonical pathways by using coloc v4,,coloc allows for multiple causal variants per trait with the assumption that if multiple causal variants exist for any individual trait, they are in linkage equilibrium. All SNPs independently associated within a dataset were identified with the function “finemap.signals.” For the pairs of CpGs and genes with multiple signals, colocalization analysis was performed for each pair of signals, restricting the search space to SNPs not in linkage disequilibrium (LD) with any-but-one of each signal SNP. The p value threshold for calling a signal was set to 1 × 10−6, and the maximum r2 between two SNPs for them to be considered independent was 0.01.We used a Bayesian test, as implemented in the R package −8 to 1.0 × 10−4) for which colocalization is supported. Pairs passing the colocalization threshold with a range of ppCCV values from <1.0 × 10−6 to 1.0 × 10−4 (lower bound of ppCCV below 1.0 × 10−6) were considered as showing robust support for colocalization.Pairs with the posterior probability for a common causal variant (CCV) > 0.8 and the ratio of the posterior probability for a CCV and different causal variants (DCVs) CCV/DCV > 5 were considered to show strong evidence of colocalization. As the posterior probability for colocalization is dependent on the prior probability, we used the coloc post hoc sensitivity analysis to determine the range of prior probabilities  and 36 hematological traits by using genome-wide summary statistics from Astle et al.We performed a selection scan using a haplotype-based statistic ,To identify regulatory loci under positive selection, we used a colocalization-based method to detect shared signals between the QTL p values and nSL values. We calculated empirical p values for the nSL values by using an outlier approach by sorting all the scores genome-wide and then dividing the rank by the total number of values in the distribution.2 > 0.7 were considered to exhibit a high correlation with LA. Similarly, we calculated the variance explained by archaic Denisovan and Neanderthal ancestry.We calculated the variance explained by modern LA in the genotype of each significant (FDR-p < 0.01) permutation-based eQTL and methylQTL as previously described.To contextualize the genetic diversity in our dataset, we began by clustering the 115 Indonesian samples A throughWe genotyped our samples by using two separate platforms, whole-genome sequencing  and the Illumina Omni 2.5M genotyping array n = 42; . Using ocis-eQTLs  and methylation (methylQTL) levels, we used a linear regression-based approach . At an Fis-eQTLs  and 48,0thylQTLs . As expethylQTLs . We thenthylQTLs  to bettethylQTLs , Data S4QTLs −6, ; additioRSU1 , and most (97%–100%) shared eQTLs show the same direction of effect in both populations2(TPM) in Indonesia 3.5, in GTEx whole blood 2.6, p < 2.8 × 10−12, To identify the attributes of Indonesia-specific eQTLs, we compared these 245 genes to the set of 105 genes with robust support for colocalization between Indonesia and all European datasets. Although there is no overall enrichment for GO or KEGG terms among Indonesian-specific eGenes, there are clinically relevant genes in this set , includioncogene . ConcordWhile some of the population specificity we detect may be attributable to limited statistical power across all datasets, our findings illustrate the value of performing analyses on diverse populations to achieve a comprehensive understanding of the genetic regulation of molecular traits. Importantly, of all datasets under consideration, the Indonesian one is the smallest, making our inability to replicate Indonesian eQTLs in European studies more likely to be biologically meaningful and clear candidates for functional follow-up.R2 > 0.7) by modern LA, archaic Denisovan introgression, and archaic Neanderthal introgression, respectively  and 270 (72.4%) as differentially expressed/methylated in at least one of the pairwise comparisons between the three study populations.,Differences in genetic architecture between populations, including differences in allele frequencies and patterns of LD, are known to limit the transferability of GWASs and polygenic risk scores across populations.ITGA4 and cg18815117, colocalizing with ten and 16 traits, respectively. ITGA4 has been previously implicated in blood trait GWASs across diverse populations.CRHR1, an important regulator of the hypothalamic–pituitary–adrenal (HPA) axis. Epigenetic changes in the body and promoter of CRHR1 have been found to be highly predictive of major depressive disorder and panic disorder in some cohorts.68First, using the same Bayesian methodology as above, we tested for colocalization between the significant Indonesian QTLs and 36 hematological traits by using genome-wide summary statistics from a GWAS on 173,480 participants of European ancestry.SLC12A7, which colocalized with red blood cell distribution width, a biomarker representing the variability in the size of circulating erythrocytes . Focusing on pairs of colocalized GWAS-eQTLs—rather than unique eGenes—we found that 72 (28%) of the 257 unique significant trait-gene pairs detected across all datasets were shared across all three European datasets A and an hrocytes C. VarianIn addition to these population-shared GWAS-eQTLs, we also identified population-specific colocalized pairs of GWAS-eQTLs. Overall, European eQTLs show more evidence for colocalization with GWAS traits than Indonesian eQTLs A and 4B.HLA superlocus. While these methylQTLs do not significantly colocalize with eQTLs in our data, all four methylSNPs are nominally associated with the expression of the nearby gene ZFP57 . ZFP57 is a transcriptional regulator known to have an important role in DNA methylation, epigenetic regulation, and imprinting during development.ZFP57 is dependent on underlying genetic variation, and while the biology of ZFP57 in adults is not well studied, it has been implicated as the causal gene connecting some GWAS variants to cancer and HIV/AIDS progression.Finally, we sought to identify local ancestry or archaic introgression-driven QTLs that are associated with hematological traits. Among the GWAS-colocalizing QTLs were a Papuan-driven methylQTL that colocalizes with hemoglobin measurements, and notably, four Denisovan-associated methylQTLs that colocalize with platelet count. We further examined these four methylQTLs to gain insight into possible mechanisms underlying the connection with platelet count . All fou,Indonesia is the world’s fourth most populous country and a region that has been vastly understudied, but it is also one that is undergoing a rapid demographic and lifestyle shift giving rise to an expanding middle class and where non-infectious, complex diseases are already contributing substantially to mortality and morbidity. As is happening elsewhere in the Global South, this transition accelerates the need to understand the molecular underpinnings of complex disease, and in this context, our study adds to a growing literature demonstrating the importance of characterizing functional genomics within traditionally understudied populations.cis-regulatory regions. The future identification of such eQTLs is fully contingent on performing large-scale QTL studies in underrepresented populations, and our work demonstrates the value of such approaches. Other drivers of population differences in architecture are likely to include trans effects (themselves most likely driven by population-specific cis effects),We have explored the degree to which functional variation differs between Europeans and Indonesians, and more broadly, the difficult problem of translating eQTL knowledge across populations. Focusing largely on a set of eQTLs that had strong evidence of being colocalized between populations and a set of eQTLs with strong evidence of being specific to Indonesia, we were able to examine potential drivers of population specificity. At least some portion of the population-specific effects we observe are explained by population-specific genomic architecture at a priori appear to be prime candidates for local adaptation, showed no evidence of having been targets of positive selection. This suggests that the relationship between trait architecture, demographic history, and adaptation to local pressures such as pathogens is not straightforward. However, our analysis demands a strong correlation between allelic state and ancestry, making our estimates of LA-driven QTLs highly conservative and identifies loci that have evolved under specific scenarios, e.g., drift to high frequency in the Papuan population. Relaxing these demands may lead to the discovery of other regulatory variants that arose within Papua after divergence from Asia. Furthermore, our analysis is limited by the small sample size. Future sample collection efforts across diverse populations will address this limitation. Understanding how complex gene regulatory landscapes and the polygenicity of most traits constrain the action of natural selection is an open challenge in human genomics, one underscored by the difficulty many studies of genome-wide positive selection in humans have encountered in linking evidence of selection at the DNA level to tangible phenotypes.,In addition to comparisons between Indonesian and European populations, we were able to leverage the unique cline of Asian and Papuan ancestry in Indonesia to identify both QTLs driven by local ancestry or introgression from archaic hominin species. A large proportion of the target genes and CpGs of these LA-driven QTLs were previously identified as differentially expressed or methylated between islands, demonstrating how between-island differences in genetic ancestry can contribute to differences in molecular phenotypes in the region. Notably, these loci, which might Similarly, the modest overlap of GWAS hits with the population-specific QTLs represents a non-trivial challenge in the field of functional genomics: how do we connect population-specific functional variation to loci associated with complex traits identified in European populations? From a practical perspective, we do not anticipate a robust expansion of traditional GWASs’ being carried out in understudied populations. To this end, the field will need to move away from simple intersections of GWASs and QTL hits, which rely upon shared LD structure, and instead integrate genetic variation, GWAS results, context-specific multi-omics , and robust functional validations to define common sets of regulatory elements that contribute to disease and are shared across populations.


The African continent is home to more than 400 million heads of sheep, the majority of which are classified as indigenous and raised primarily for subsistence. They live and thrive well in a wide range of climatic and production conditions, ranging from unfavorable to favorable environments. Recent molecular and archeological studies have hypothesized that these breeds harbor novel genomic regions that give them the ability to adapt to varied production environments. However, the genetic relationship among these populations is poorly understood. Knowledge about the population history and genetic relationships between populations provides an opportunity for the improvement of breeding and conservation programs. We meta-analyzed 399 African sheep breeds mtDNA control region sequences retrieved from the NCBI GenBank database to elucidate their diversity, phylogenetic relationship, and demographic history.To improve sheep breeding and conservation of genetic resources, the mitochondrial DNA control region (mtDNA CR) of 399 sequences of African indigenous sheep breeds from previously published research articles were meta-analyzed to elucidate their phylogenetic relationship, diversity, and demographic history. A total of 272 haplotypes were found, of which 207 were unique and a high level of mtDNA CR variability was observed. Generally, the number of polymorphic sites, nucleotide and haplotype diversity were high . The median-joining (MJ) network of haplotypes produced three major haplogroups , with haplogroup B being dominant. A mixture of populations suggests a common matrilineal origin and lack of and/or a weak phylogeographic structure. Mismatch analysis showed recent expansion of North African breeds, whereas East African and continental populations exhibited selection pressures for adaptation. A slight historical genetic difference was also observed between the fat tail and thin tail sheep breeds. However, further investigations are required using more samples and long sequence segments to achieve deeper levels of conclusions on the African sheep phylogenetic relationship. The present meta-analysis results contribute to the general understanding of African native sheep populations for improved management of sheep genetic resources. Domestic animal agriculture plays a crucial role in maintaining food security, as envisioned in sustainable development goal two  . At presOvis aries musimon) and are more frequent in Europe [Ovis orientalis) which is mainly present in Asian breeds [Archeological evidence suggests that sheep were among the first animals to be domesticated approximately more than 10,000 years ago in present Iran, some Arabic countries and Turkey, formerly referred to as the fertile crescent at the Zagros Mountains of Iran ,13. It in Europe ,6,14, whn breeds . Other an breeds . Furthern breeds . StudiesThe history of domestic sheep in Africa is still inconclusive and thus it is an area calling for further investigation. This could be due to inadequate mtDNA CR studies done on African sheep breeds to explore their matrilineal lineages. However, the available inferences suggest that sheep spread throughout Africa with the pastoral communities after being introduced from the Near East . The rolStudies on the matrilineal lineage of African sheep breeds have yielded consistent results that clades A and B are the most frequent in the region, with clade B being the most common. Haplogroups C and E have been reported but not as common. For instance, it was reported that clade B is most common in some sheep breeds from Algeria , EthiopiPopulation geneticists have utilized DNA polymorphisms to study the “true” genetic structure, relationships of livestock species, within and between populations, for example, by using microsatellite markers ,25, SNPsOvis orientalis) was thin-tailed. Interestingly, fat-tailed sheep are broadly distributed across the African continent and other parts of the world. This could be the evidence of post-domestication selection in response to human preference or harsh climatic conditions [The African continent boasts of over 170 domestic sheep breeds, of which 80% are classified as native populations  producednditions ,34. Acconditions .Studies aiming to shed light on the genetic differentiation, phylogenetic relationships and matrilineal lineages in sheep breeds have been conducted worldwide, targeting specific breeds in specific countries. mtDNA CR markers have been instrumental in these studies. Similar but few studies have been conducted in Africa, with the main aims of assessing the evidence of domestication and inferring the ancestral origin of the sheep breeds by observing the haplogroups present in the populations. However, to the best of our knowledge, there is no literature exploring the phylogenetic relationships among sheep breeds in the African continent. As a result, the current meta-analysis aims to examine the mtDNA CR diversity and phylogenetic relationships between sheep populations in Africa by retrieving and analyzing the mtDNA control region sequences from the NCBI GenBank database . KnowledThe 469 mtDNA CR sequences used in this meta-analysis were retrieved from the NCBI GenBank database  see Tab. ResearcThe retrieved sequences were aligned and edited using the CLUSTALW  and MEGADNA sequence polymorphism software (DnaSP v6)  was usedRepresenting four regions of the African continent, 399 mtDNA CR sequences of 481 bp length were analyzed. The summary of observed polymorphic sites is presented in Out of all 272 haplotypes observed, 76% were unique, suggesting a significant diversity between studied individuals. Of the 24% shared haplotypes, less than 25% were shared transregionally, the rest were within regions and more specifically within specific countries. Further, most of the haplotypes were one mutation step away from one another, suggesting recent linkages. Our sequences yielded three major haplogroups . . Sub-hapTo understand more about the historical background of these populations, we performed Tajima’s D and Fu’s FS values  to distiST of 0.016, with a within 98.43% accounting for within-population haplotype frequency  ,50. We orequency .ST between breeds varied significantly, ranging from 0.098 between East Africa and North Africa to 0.986 between South Africa and West Africa (ST) was high at 0.547.The pairwise Ft Africa . OverallAutochthonous sheep breeds are vital to the economy and subsistence of many farm families and pastoralists over the African continent. They thrive well in ecologically marginal zones like mountains, arid and semi-arid areas (ASALS) where other domestic animals can not economically thrive , and areUnderstanding the history and phylogenetic relationships of native sheep breeds in Africa is fundamental for the development of breeding and conservation strategies. Several archaeological studies are in concurrence that sheep were among the first animals to be domesticated . FurtherA high level of mtDNA CR diversity was observed in all studied populations. Results observed in the present study are comparable to findings observed in other research works in different livestock species. For example, Revelo et al.  observedGenerally, the larger number of haplotypes observed coupled with a higher number of unique haplotypes suggests that a high level of mtDNA CR differences exist among studied individuals and populations. None of the studied regional populations exhibited significantly negative or positive Tajima’s D tests. However, Fu’s FS test returned significant negatives for both the North and West African populations, which could be a suggestive indicator of an expanding population. Mismatch distribution produced a multi-modal pattern for North Africa  and a unThe median-joining network of haplotypes formed three haplogroups  lacking phylogeographic clustering. Haplogroup B  was the most common comprising sequences from all studied regions. However, haplogroup A had breeds only from Egypt while haplogroup C consisted of sequences from all North African countries . This observation suggested that studied sheep populations originated from a common matrilineal ancestor (European mouflon) and differentiated into different breeds adapting to local environmental conditions through natural and human selection. Populations in sub-haplogroup B1 are more specifically from West Africa and Sudan (most frequent in hap 233) seem to have displayed further dispersal characteristics compared to other populations in different haplogroups. On the other hand, Sudan played a crucial role in sheep dispersion, as discussed later in this section. Sub-haplogroup B3 formed by the present sequences exclusively included sheep from South Africa. This was expected, as the sequences were obtained from samples excavated from the second layer of the stone age, which is estimated to be more than 2700 years of age  and thusThe African continent is endowed with sheep breeds and varying phenotypes, among which is tail type. Recent research materials have indicated that the fat tail phenotype is an adaptation to hot and dry environments. In our phylogenetic network, sheep breeds characterized with thin tails mainly dominated haplogroup B1 e.g., OulledDjella, Sardi, D’man and all the West African and Sudan sheep breeds. This could be an indication of a common historical background. According to Muigai , thin-taST value observed was also very high (0.547), indicating high levels of population differentiation. This could be due to the vast geographical representation and vast difference in production environments. Furthermore, the haplotype frequency FST observed was high (0.016), indicating that haplotype differentiation among populations has occurred but not as much. In general, FST is regarded as low when the value is below 0.050, moderate when it is between 0.050 and 0.150, high between 0.150 to 0.250, and very high when the value is above 0.250 [The higher variance (54.696%) among populations as compared to within populations (45.304%) suggested the existence of a possible geographical population structure, although this is weak. These results are comparable to other studies that registered similar findings e.g., ,50,60. IThe African continent holds an abundant population of autochthonous sheep breeds whose potential has not been fully explored due to inadequate studies conducted on them. mtDNA D-loop marker is one of the very crucial molecular markers that have the potential to provide adequate information on the archaeological history and phylogenetic relationships of indigenous sheep. In the present study, the diversity and phylogenetic relationships of 399 sequences of African sheep populations mtDNA CR were meta-analyzed. The observed large number of haplotypes, together with more than 70% unique haplotypes, high nucleotide and haplotype diversity, suggests a very high variability of the mtDNA control region. On the other hand, the median-joining network resulted in the formation of haplotype clusters of mixed populations, suggesting a common matrilineal origin for the sequences studied. Neutrality tests and mismatch distribution analysis indicate a series of population expansions in North and West Africa, and a selection for adaptation in East African sheep populations. Further continental population mismatch analysis pattern suggests selection pressure mostly driven by climate adaptation.The information provided in this study is vital for the improvement of sheep genetic resource conservation and breeding strategies for the long-term attainment of food security. More studies on archeological history and relationships among African sheep populations diversity and relationships on the recent populations are needed. In addition, the genetic and phenotypic characterization of indigenous sheep populations from different climatic zones as a potential factor for adaptation to local environmental conditions is also recommended.


Odorrana kweichowensis is endemic to Guizhou Province, China. In this study, a comparative analysis of the mitochondrial COI and ND2 gene sequences was performed to examine genetic diversity in 109 individuals from ten localities across the geographic range of the species. Haplotype diversity and nucleotide diversity were 0.576 and 0.00055, respectively. Phylogenetic analyses almost nested all haplotypes into one lineage. AMOVA indicated that total variation was mainly derived from variation within individual populations. Neutral tests indicated that a recent expansion occurred in the total population. Fst estimations indicated that genetic divergence was not correlated with geographic distance. Accordingly, the species probably experienced a recent population expansion, and there no obvious population genetic structure is apparent. The findings provide useful information for the conservation of this species.The Guizhou odorous frog  Odorrana kweichowensis Li et al. O. kweichowensis in the northern part of Guizhou Province, China, the species was suggested to occur in parapatry with its phylogenetically closest species, O. schmackeri, and to have a wider distributional range in Guizhou Province and perhaps into northern Guiangxi Province, China  markers are frequently used for inferring levels of population genetic divergence and structure  in Guizhou Province, China that span the known geographical range of howensis ; Table 1howensis . Two frahowensis  and for howensis . Gene frSequences were assembled and aligned using the ClustalW module in BioEdit v.7.0.9.0 Hall  under thh) and nucleotide diversity (π) were estimated using DnaSP v.5 , as implemented in Arlequin 3.11  among the populations was estimated in DnaSP. For the phylogenetic analyses based on COI + ND2 genes, corresponding sequences of one O. schmackeri, one O. tianmuii, one O. huanggangensis, one O. yizhangensis, one O. lungshengensis, and one Rana temporaria were downloaded from GenBank  and aligned to the O. kweichowensis dataset as described above. Phylogenetic relationships were reconstructed using Maximum Likelihood (ML) as implemented in the program PHYML v. 3.0 . The proportions of base types were A = 27.2%, T = 27.8%, C = 31.8%, and G = 13.1%, with (A + T) = 55.0% significantly higher than (C + G) = 44.9%. Fifteen haplotypes were found among the 109 individuals of ten populations of howensis . Haplotyhowensis ; Table 1howensis ; Table 1The total haplotype diversity of all populations was moderate with 0.577, and haplotype diversity was moderate or low in each population . P9 showFs values were significantly negative  that was recovered as sister to O. schmackeri. The haplotype network showed that H2 was occupied by 70 samples, H1–H11, H14 and H15 each independently linked to H2, and H12 and H13 linked to H11  occurred in all populations, indicating that all examined populations of the species might have a recent common origin. The genetic distances between populations was very low (average of 0.2% between samples). Further, AMOVA also suggested that more than 60% molecular variance was attributed to the differentiation within populations rather than between populations. This point was also evidenced by neutrality tests that indicated a significant recent population size expansion in the total population of the species. Obviously, the recent population expansion was mainly derived from the ubiquitous distribution of haplotype H2. However, most populations of the frog have been experienced some minimal divergence, for example, the haplotype network indicated that in P4, haplotypes H12 and H13 were probably derived from H11 rather than the common H2. These results indicate that the frogs of the species have probably experienced shallow population divergences only very recently. Nevertheless, based on current results, we could not deduce the ancestral region or expansion center due to seven populations exhibiting unique haplotypes with low genetic diversity.The geographical topology within the distributional range of O. kweichowensis were relatively low, indicating that it might be a young species or has experienced bottleneck effects (Miracle and Campton In summary, genetic diversity and structure in