Genetic adaptation to future environmental conditions is crucial to help species persist as the climate changes. Genome scans are powerful tools to understand adaptive landscapes, enabling us to correlate genetic diversity with environmental gradients while disentangling neutral from adaptive variation. However, low gene flow can lead to both local adaptation and highly structured populations, and is a major confounding factor for genome scans, resulting in an inflated number of candidate loci.Onithochiton neglectus), taking advantage of a natural geographical contrast in the levels of genetic structure between its populations. O. neglectus is endemic to New Zealand and distributed throughout an environmental gradient from the subtropical north to the subantarctic south. Due to a brooding developmental mode, populations tend to be locally isolated. However, adult hitchhiking on rafting kelp increases connectivity among southern populations.Here, we compared candidate locus detection in a marine mollusc  and two genotype–environment association (GEA) tests (BayeScEnv and RDA). To limit issues with false positives, we combined results using the geometric mean of Genome scans for outliers detected a large number of significant outliers in strong and moderately structured populations. No high‐confidence GEA loci were detected in the context of strong population structure. However, 86 high‐confidence loci were associated predominantly with latitudinally varying abiotic factors in the less structured southern populations. This suggests that the degree of connectivity driven by kelp rafting over the southern scale may be insufficient to counteract local adaptation in this species.Our study supports the expectation that genome scans may be prone to errors in highly structured populations. Nonetheless, it also empirically demonstrates that careful statistical controls enable the identification of candidate loci that invite more detailed investigations. Ultimately, genome scans are valuable tools to help guide further research aiming to determine the potential of non‐model species to adapt to future environments. Reliably interpreting results of genome scans in non‐model species is challenging. Applying genome scans to an understudied brooding chiton and using a new strategy to focus the results on a true signal, this work shows potential for local adaptation even in the face of gene flow. We compared four spatial scales: a broad New Zealand‐wide scale with low overall gene flow, including 16 ) Figure ; Table 1q‐values between the methods. This aims to balance a mid‐way point between taking all significant results from any method  and only shared significant results across methods . Furthermore, to identify GEA loci with a higher probability of reflecting a true association (high‐confidence GEA loci hereafter), after combining q‐values across methods we also excluded loci that showed associations with randomly generated variables. Finally, we compared the DNA sequences of high‐confidence GEA loci with a related chiton genome (Acanthopleura granulata)  with those from the remaining loci, to identify potentially different evolutionary histories for these sets of loci. Third, we tested for genotype–environment association (GEA) using two methods (RDA and BayeScEnv) to identify loci correlated with variable environmental factors across the species range (GEA loci hereafter). While false positives can be problematic with genome scans, combining methods can achieve better signal‐to‐noise ratio, at the cost of lower power  were estimated for all datasets, with the r package hierfstat v. 0.5‐7 .Population differentiation and genomic diversity  and the analysis was re‐run with three principal components for the NZ‐wide dataset, two principal components for the NI dataset, one for the SI dataset, and six for the southern dataset, using the same settings as above. After checking their distribution , the corrected p‐values were transformed into q‐values with the qvalue package v. 2.16 in r  can outweigh no support from the other method . For example, a locus with q‐value = 0.001 in one method and 0.2 in another has a combined q‐value (by the geometric mean) of 0.01, which is significant for a threshold  of 0.05. This approach recognizes that different methods have different assumptions and may return complementary results, but also reduces the effect of false positives from taking the union of significant associations, while being less stringent than the common practice of taking the intersection of methods .The results of both methods were combined by calculating geometric means of the q‐value smaller than an arbitrarily chosen 0.05 cut‐off were checked for observed heterozygosity and missing data across populations. Past demographic patterns were inferred for the combined ‘outlier’ markers and for the remaining loci (i.e. having removed combined ‘outlier’ markers from the dataset) in the NZ‐wide dataset with the program Stairway Plot 2 , the genome scans, PCAs and allele frequency plots were also done for these groups by subsampling the NZ‐wide SNP dataset into the North Island, South Island and southern regions (Table S2).‘Outlier’ markers with a combined 2.4r package vegan v. 2.5.6 . Environmental variables (described below) were mean‐centred and scaled to a variance of one. RDA implemented in the For BayeScEnv  as netcdf files, and converted to a data frame with the ncdf4 v. 1.16.1 r package  were then examined for association with the randomly generated variables. Those loci that also significantly associated with fewer than five random variables (i.e. 5% of the random runs) were treated as high‐confidence associations. Since BayeScEnv runs included the environmental variables correlated with latitude (r2 > 0.7), q‐values of all latitude‐correlated variables were combined using the geometric mean, and significant associations to latitude in the ‘broad sense’ are reported. High‐confidence GEA loci were checked for observed heterozygosity and missing data across populations.The results of the two GEA methods were combined using the same method as for GSO: calculating geometric means of the Acanthopleura granulata genome and transcriptome  were extracted and run in another BLAST search against other molluscs (taxid:6447) using the nucleotide (nr/nt) database, and the program selection blastn, optimizing for somewhat dissimilar sequences.Sequences of high‐confidence GEA loci were submitted in a BLAST search against a custom database containing the 33.1The SNP calling pipeline resulted in a NZ‐wide dataset consisting of all 16 populations and 10,987 loci, with population‐average 0.2% missing data . The allele frequency spectrum plots reveal a broadly different pattern for all populations in the South clade as compared to populations from other clades, as southern populations have a higher proportion of low‐frequency alleles . Note that the population allele frequency spectra are based on counts from the NZ‐wide datasets, and the larger number of South populations means the south alleles are the majority, and thus taken as reference. The 2D allele frequency spectra between clades show no correlation pattern, with most of the high‐count alleles in one clade missing or infrequent in the other, indicating no evidence of introgression . The distribution of observed heterozygosity per population NZ‐wide shows most loci with low heterozygosity . Finally, relative migration estimates show much higher connectivity among populations of the South clade than among populations of other clades, with no support for migration between clades .All population differentiation and genomic diversity analyses support the subdivision of 3.3q‐values resulted in a larger number of ‘outlier’ markers NZ‐wide than each of the methods separately, and in intermediate numbers in the other datasets . The ‘outlier’ markers show lower heterozygosity than the remaining loci in most populations of all datasets, except for populations of the South clade in the NZ‐wide dataset (Table S6).The NZ‐wide population structure derived from all SNP loci Figure  is simil3.4F = 27.05, p‐value < 0.001, NI scale RDA full model F = 5.95, p < 0.001, SI scale RDA full model F = 1.65, p < 0.001, the southern scale RDA full model F = 3.69, p < 0.001).Many of the environmental variables tested are strongly correlated with latitude at all scales . The exceptions are precipitation rate, surface pressure, sea surface height relative to geoid and longitude, which are only weakly correlated with latitude. The RDA full model indicates a significant relationship between loci and the associated environmental variables . In comparison, BayeScEnv identified 3,093 associations from the New Zealand‐wide dataset (28% of the loci), 15 from the NI dataset (0.13% of the loci), 10 from the SI dataset (0.13% of the loci) and 135 from the southern dataset (1% of the loci) . These two O. neglectus loci (#14183 and #46591) are associated with latitude. BLAST analysis of the A. granulata sequence flanking these matches did not return annotated genes in other molluscs.BLAST analyses of the sequences flanking the high‐confidence SNPs against 4O. neglectus population divergence by comparing patterns for all populations  and NI populations (moderate population structure) with patterns in the SI and southern scales (showing lowest population structure). Our overall results agree with the expected artefactual inflation in the number of candidate loci under strong background population structure , but bottlenecks might be present , further increasing the likelihood of outliers not due to selection. There is insufficient genomic information for this species to assess the impact of genome‐wide variation in mutation and recombination. Ultimately, more genomic resources are necessary to completely disentangle natural selection from the confounding factors mentioned above.Apart from population structure, there are other factors that can lead to an increase in false‐positive rates of genome scans (GSO and GEA). Not all loci in the genome will follow the same evolutionary path, which results in some loci that are more divergent among populations than others . Overall, the two methods did not detect a large proportion of loci in common , or no associations at all (RDA method) Table , but the4.3q‐values across methods and then, for the GEA methods, to remove loci that are also associated with 5% or more of the random variables. This approach was useful to find candidate loci that are less likely to be false positives and have spurious associations, although we acknowledge that true‐positive loci are likely to have been excluded in the process. Previous work has shown that considering the intersection of genome scan methods tends to focus the result on a true signal . There was a match of one 5O. neglectus, a novel method was used to increase the probability of identifying a reliable set of candidate loci for selection, but also indicates that it is still a challenge to correctly identify true positives in this confounding scenario of population structure. However, our methods have provided powerful insight within the southern region, showing that the potential for local adaptation has not been eliminated by gene flow. Furthermore, among the high‐confidence GEA loci found, we identified a functional genomic region that could have an adaptive role in the evolution of these populations. More empirical assessments and comparisons of ‘challenging’ populations can help with outlining the variation expected under real scenarios, prompting further development to better accommodate such diversity of the natural world. In addition to the ongoing growth in the availability of genomic resources, advances in methodological approaches will enable more comprehensive understanding of local adaptation and its underlying causes in the wild, leading to a better understanding of the complex responses of organisms to changes in their environment.The use of genomic scans for outliers and genotype–environment association methods has been providing great insight into understanding the distribution of potentially adaptive variation in natural populations, which is crucial for appropriately managing biodiversity in a changing climate (Flanagan et al., The authors have no conflict of interest to declare.All authors helped plan the research and contributed to writing the manuscript; P.d.V. also helped with statistical analysis and designed the novel statistical approach used in this work; P.M.S. undertook sampling, laboratory work, statistical analyses and writing. All authors gave final approval for publication.Data S1Click here for additional data file.


TRAP gene to be under strong balancing natural selection. This study is the first attempt to analyze genetic diversity, natural selection, phylogeography and population structure in 199 clinical samples from Saudi Arabia using the full-length PfTRAP gene. We found the rate of nonsynonymous substitutions to be significantly higher than that of synonymous substitutions in the clinical samples, indicating a strong positive or diversifying selection for the full-length gene and the Von Willebrand factor (VWF). The nucleotide diversity was found to be π~0.00789 for the full-length gene; however, higher nucleotide diversity was observed for the VWF compared to the thrombospondin repeat region (TSP). Deduction of the amino acid sequence alignment of the PNP repeat region in the Saudi samples revealed six genotypes characterized by tripeptide repeat motifs . Haplotype network, population structure and population differentiation analyses indicated four distinct sub-populations in spite of the low geographical distance between the sampling sites. Our results suggest the likeliness of independent parasite evolution, creating opportunities for further adaptation, including host transition, and making malaria control even more challenging.The thrombospondin related anonymous protein (TRAP) is considered one of the most important pre-erythrocytic vaccine targets. Earlier population genetic studies revealed the  P. falciparum being the most virulent cause accounting for the majority of cases globally. According to global malaria statistics, there are an estimated 228 million malaria cases and 405,000 deaths worldwide each year [Malaria persists as a serious public health issue, with ach year ,2. Althoach year . As per ach year . All theAnopheles arabiensis and A. sergentii continue to cause transmission [Plasmodium that are known to transmit to humans, P. falciparum accounts for the majority of the total malaria cases in Saudi Arabia , and only 1% are attributable to P. vivax [P. vivax has been reported to be rising since 2015, as seen from data from India, Pakistan and Ethiopia [In Saudi Arabia, over the past twenty years, the malaria control program has efficiently lowered the incidence of malaria annually. However, disease infections are reported to still exist, and they have been increasing since 2016  in the rsmission ,9,10. Amsmission ,9,11. OfP. vivax . HoweverEthiopia . AccordiEthiopia . PreviouEthiopia ,10,15,16Ethiopia ,8,17.Anopheles mosquitoes bite humans and inject sporozoites. Sporozoites rapidly move to the liver and start infecting hepatocytes [Plasmodium spp. and non-malarial pathogens, including the HIV virus, hepatitis C virus, Ebola virus, dengue virus, etc., thus widening the scope of the challenge [Malaria infection in humans begins when female atocytes . As malaatocytes ,19,20. Thallenge . The leahallenge ,23. The hallenge . In malahallenge .P. falciparum circumsporozoite protein (CSP), thrombospondin related anonymous protein (TRAP) and liver-stage antigens (LSAs), are released from the sporozoite surfaces as soon as they are exposed to the host cell [P. falciparum strains, which was ascribed to the vast parasite diversity and allele-specific immune response detected among field isolates [PfTRAP gene by gene knockout interrupts sporozoite gliding movement and invasion of the salivary gland [Plasmodium proliferation, this gene is the most widely studied Plasmodium transmembrane protein. The extracellular domain of PfTRAP is comprised of three motifs: the A-motif, the thrombospondin repeat (TSR) motif and a proline-rich portion at the C-terminal [PfTRAP. Part of the VWA motifs contain the inserted domain (I) as integrins, constituting metal ion-dependent adhesion sites (MIDAS) attached with a Mg2+ ion at the central position of the ligand-binding site [During the parasite hepatic phase, various surface proteins, such as ost cell . Howeverost cell ,28. The ost cell . Althougost cell . Unfortuisolates . Howeverisolates . TRAP isisolates ,34. It pisolates ,33,35. Ary gland . Due to ing site ,37. The ing site ,38.TRAP gene to be acting under diversified selection on the genes of both PfTRAP and PvTRAP. Earlier studies revealed strong diversifying selection for the TRAP gene in both P. falciparum and P. vivax, indicating TRAP to be an attractive vaccine target [PfTRAP gene in clinical samples from Saudi Arabia. Therefore, the present study aimed to elucidate the genetic diversity, natural selection and population structure of PfTRAP from 199 clinical samples from Saudi Arabia, along with sequences from three other countries , using the full-length PfTRAP gene.A previous study on the Gambian and Thai populations reported high frequencies of nonsynonymous to synonymous single-nucleotide polymorphisms (SNPs) of TRAP . This ste target ,39,40. HP. falciparum. These were patients attending various health clinics in the Jazan province   of King Fahad Central Hospital (KFCH), Jazan. For the study, finger-pricked blood samples were collected on filter paper from 440 RDT-positive patients infected by Al-Twal) . InformeDNA extraction was carried out from whole blood using a DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions.TRAP gene was amplified in three separate segments. Nucleotide sequences for all primers used in the amplification of the three segments are shown in The The following PCR amplification conditions were used for segment 1. For round one, the PCR mixture included 5 µL of DNA template, 1X final concentration of GoTaq Green Master Mix  and 0.2 µM for each primer (S1_For-1 and S1_Rev-1), with 30 µL final volume. For the nested amplification, 2 µL of round one amplicon was used, along with 1X final concentration of GoTaq Green Master Mix (Promega) and 0.16 µM for each primer (S1_For-2 and S1_Rev-2), with 30 µL final volume. Cycling parameters for both rounds 1 and 2 were 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 56 °C for 30 s and 72 °C for 90 s and a final extension at 72 °C for 5 min. For segment 2, 2 µL of DNA template was used, along with 1X final concentration of GoTaq Green Master Mix (Promega) and 0.125 µM for each primer, with 40 µL final volume. Cycling parameters were 95 °C for 5 min, followed by 45 cycles of 95 °C for 30 s, 56 °C for 30 s and 72 °C for 30, and a final extension at 72 °C for 5 min. For segment 3, the mixture for round one included 5 µL of DNA template, 1X final concentration of GoTaq Green Master Mix (Promega) and 0.13 µM for each primer (S3_For-1and S3_Rev-1), with 30 µL final volume. For the nested amplification, 1.5 µL of round one amplicon was used, along with 1X final concentration of GoTaq Green Master Mix (Promega) and 0.1 µM for each primer (S3_For-2 and S3_Rev-2), with 30 µL final volume. Cycling parameters were 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 57 °C for 20 s and 72 °C for 30 s and a final extension at 72 °C for 5 min. PCR products were run and visualized on 2% agarose gel.PCR products were gel-purified and subjected to dideoxy Sanger sequencing using forward and reverse M13 as sequencing primers. The resulting sequencing chromatograms were assembled using SeqMan Pro 15  and a consensus FASTA file was saved in EditSeq 15 (DNASTAR Lasergene). MegAlign 15 (DNASTAR Lasergene) was used to perform multiple sequence alignment of nucleic acids and/or proteins.PfTRAP gene and the reference strain PF3D7_1335900were determined using DnaSP v5.0 software [In this study, sequence diversity (π) was determined using DnaSP v5.10 software . The numsoftware . Domain-software . Using aPfTRAP gene was created using the median-joining method in NETWORK version 4.6.1.2 software . The genetic structure of the Plasmodium falciparum parasite population was determined using STRUCTURE V2.3.4 software, which is based on a Bayesian Markov chain Monte Carlo (MCMC) model [PfTRAP were also conducted by removing the P-N-P regions from the genes using STRUCTURE software. The pairwise differences (STF) between populations were also computed using ARLEQUIN version 3.5.1.3 [STF compares the frequencies of alleles within and between populations to calculate genetic variability within and between populations. STF can be interpreted as no genetic differentiation when values are 0, low differentiation at <0–0.05, moderate at 0.05–0.15 and high at 0.15–0.25.For the haplotype network and population structure analyses, we categorized the sampling locations from Saudi Arabia into five regions based on distances of 50 km . For haplotype network analysis, 42 samples from Malaysia, Thailand and Gambia were also included. The haplotype network for the C) model . Using aC) model . Re-anal 3.5.1.3 . FST comPfTRAP, a single exon that contains specialized domains, such as the von Willebrand factor, thrombospondin type (TSP) repeat and (P-N-P)n repeats, is shown in PfTRAP gene was found to be π = 0.00789 ± 0.00027, with seven singleton variable sites that were tri-variants. The nucleotide diversity of the TSP domain was lower than that of the Von Willebrand factor (VWF) at 0.00580 ± 0.00048 and 0.00971 ± 0.00033, respectively , of which 2 were synonymous and 63 nonsynonymous substitutions . Domain-wise analysis also showed significant positive natural selection  for the Von Willebrand Factor; however, the TSP domain was not significant (p < 0.05) (p < 0.05) and F*  values were found to be negative and significant (p < 0.05). Natural selection analysis of 241 PfTRAP sequences revealed similar results, with VWF showing strong natural selection  . A graphnificant . Analysielection .PfTRAP clinical samples from Saudi Arabia, Malaysia, Thailand and Gambia. In this study, in addition to the PNP motifs, we identified three other novel tripeptide repeat motifs . In accordance with the arrangement of PNP repeat motifs and the three novel tripeptide motifs, ten genotypes were identified (named Type I to Type X in this study). Among the Saudi samples, six genotypes (Type I to Type VI) were observed, with highest prevalence for the Type III (23.6%) and Type IV (23.6%) genotypes, followed by Type II (15.07%), Type I (6.03%) and, lastly, Type IIA, Type V and Type VI, which had similar and equal prevalence , respectively  n, also resulted in four sub-populations (K = 4)  . Re-anal (K = 4) .STF) obtained between region 2 (Beash) and region 3  equaled 0.981 with p < 0.05, which was significant, and the lowest Fst value was observed between region 1  and 5 (Al-Darb)   . A moderPfTRAP, gene which is one of the leading malaria vaccine candidates. Therefore, this study is the first attempt to understand genetic variations, natural selection and population structure in clinical samples obtained from the port region of Jazan, Saudi Arabia.Among the hepatic-stage antigens, which are localized on sporozoite surfaces, PfTRAP plays a significant role in sporozoite gliding movement and invasion into liver cells. During release of sporozoites into blood, these antigens are directly exposed to host immune responses and, therefore, are considered potential vaccine candidates and are being assessed in various clinical trials ,45,46,47PfTRAP exon. The results obtained from this study showed positive and significant selection for the full-length gene (dN-dS = 5.386) and VWF domain (dN-dS = 4.018). This was due to the significantly higher value in the nonsynonymous substitution rate as compared to the synonymous substitution rate. The (P-N-P)n repeat region of the PfTRAP gene showed different allele frequencies of varying PNP repeats. The PNP repeats of Saudi Arabia and Thailand exhibited similar repeat types, while those of Gambian clinical isolates exhibited variations in the P-N-P repeat region.Moreover, the highly diverse nature of the parasite population revealed from this study is suggestive of a high degree of transmissibility and may pose a barrier for malaria control efforts in the region. The high antigenic diversity and the creation of new haplotypes correlated to transmission intensity, which might have been due to the presence of multi-clonal infections and multiple recombination events at the parasite sexual stage . MoreovePfTRAP gene, significant and high Fst values were obtained between the different geographical locations of Saudi Arabia. Although regions 2 and 3 had low geographical separation, interestingly, the genetic differentiation between them was found to be high . Previous reports have revealed an increase in malaria transmission during the last few years in Saudi Arabia. The rise in the cases has been reported to be possibly related to the constant flow of immigrants from India, Pakistan, Sudan and Yemen [Noticeably, in spite of the low geographical distance between the sampling sites in Saudi Arabia, strong and significant genetic differentiation was observed, resulting in four sub-populations (K = 4). This may imply that the migrant population has a role in transmitting the infection, leading to local transmission. The haplotype network analysis revealed that the clinical samples had high genetic differentiation, which led to clustering of four different sub-populations. Analysis of population differentiation between parasite populations originating from different locations of Saudi Arabia showed high genetic differentiation. Despite having low nucleotide diversity (π = 0.0078) for the nd Yemen . Yemen ind Yemen . The hignd Yemen . FurtherPfTRAP gene using clinical samples from Saudi Arabia. The levels of polymorphism within the sequences were moderate; however, a high level of haplotype diversity was noted, indicating the population expansion of the parasite. Multiple analyses found four distinct sub-populations within the Jazan province of Saudi Arabia, indicating that any TRAP-based vaccine has to consider the alleles defining these sub-populations. The data presented and discussed in this study will be helpful in understanding and rationally designing a TRAP-based vaccine for malaria.This study is the first attempt to understand the genetic diversity and population genetic structure of the


Genetic variation inherent in landraces is essential for broadening the genetic diversity of our crops. This study pioneers the development of a theoretical framework to link molecular inventories of plant genetic resources to phenotypic variation, allowing an informed choice of landraces and their crossing partners. We show that genome-based prediction of genetic values can be implemented successfully in landrace-derived material, despite a strongly reduced level of relatedness compared with elite germplasm. Theoretical derivations are validated with unique experimental data collected on two different landraces. Our results are a pivotal contribution toward the optimization of genome-enabled prebreeding schemes. Discovery and enrichment of favorable alleles in landraces are key to making them accessible for crop improvement. Here, we present two fundamentally different concepts for genome-based selection in landrace-derived maize populations, one based on doubled-haploid (DH) lines derived directly from individual landrace plants and the other based on crossing landrace plants to a capture line. For both types of populations, we show theoretically how allele frequencies of the ancestral landrace and the capture line translate into expectations for molecular and genetic variances. We show that the DH approach has clear advantages over gamete capture with generally higher prediction accuracies and no risk of masking valuable variation of the landrace. Prediction accuracies as high as 0.58 for dry matter yield in the DH population indicate high potential of genome-based selection. Based on a comparison among traits, we show that the genetic makeup of the capture line has great influence on the success of genome-based selection and that confounding effects between the alleles of the landrace and the capture line are best controlled for traits for which the capture line does not outperform the ancestral population per se or in testcrosses. Our results will guide the optimization of genome-enabled prebreeding schemes. Genetic improvement is essential to secure sustainable crop production. Future crops will have to combine high yield potential with major sustainability factors, such as stress tolerance and resource efficiency. To meet these demands, plant breeding will require a reservoir of genetic variation much larger than what is currently found in commercial varieties . For mai5Landraces have been shown to harbor beneficial alleles for traits with limited genetic variation in breeding populations , but for11h2) of the target traits, the sample size (N), and the number of markers (M).In outcrossing species, population improvement generally includes three distinct phases : 1) sampHere, we developed the quantitative genetic framework for two fundamentally different concepts for establishing training populations from landraces. The two concepts are displayed in SI Appendix, Fig. S1A). In both landraces, each population showed a small percentage of segregating markers that were fixed in one or both of the other two populations due to independent sampling from the ancestral landrace. The capture line FV2 carried a SNP allele not present in the LS and DH lines at 13,315 (KE) and 11,488 (PE) genomic positions, thus contributing about half of the private polymorphisms of the GC lines. For both landraces, allele frequencies observed in DH and GC corresponded with allele frequencies estimated from LS and FV2 .We developed populations of DH and gamete capture (GC) lines from two flint maize landraces, Kemater Landmais Gelb (KE) and Petkuser Ferdinand Rot (PE). The French inbred FV2 derived from population Lacaune served as the capture line. Both populations were produced from the same seed batch of the respective landrace, which we defined as the ancestral landrace. In addition to the derived DH and GC populations, a random sample from the ancestral landrace (LS) was genotyped. Across populations, 85 and 92% of the total 472,169 single nucleotide polymorphisms (SNPs) were polymorphic in KE and PE, respectively. The majority of the polymorphic markers (80.9% for KE and 78.4% for PE) segregated in all three populations . In the GC populations, the allele frequencies of the capture line need to be accounted for. In the experimental LS and DH populations, mean and range of pairwise genetic distances were similar but not identical, with SI Appendix, Fig. S1C). For this reduced set of markers, the variation of genetic distances to FV2 in GC reflects the effect of Mendelian sampling, as GC-S0 plants are fully heterozygous, and the resulting genetic distances should be equivalent to what is expected in the F2 generation of a biparental cross.Mean pairwise genetic distances of genotypes enotypes B. Mean gp being the frequency of the allele carried by the capture line . For GC, the expected total molecular variance amounts to SI Appendix, Table A3). Consequently, for a given locus, SI Appendix, Fig. S1D). Thus, the observed molecular variances for LS, DH, and GC  meet expectations.For all three types of populations, we derived expectations of the total molecular variance and its decomposition between and within genotypes assuming absence of selection. For a single locus, the total molecular variance calculated based on biallelic SNP allele frequencies is expected to be identical for DH and the sample from the ancestral landrace (LS) with δ), for which the pairwise LD of markers on the same chromosome was greater than r2 > 0.2, was slightly higher in the GC than in the DH lines and was higher in populations derived from KE  than from PE (399 ≤ δ ≤ 660 kb) (The linkage disequilibrium (LD) decay distance ( 660 kb) A. Within 660 kb) B. Across 660 kb) C. AveragSI Appendix, Fig. A1 based on the theoretical expectations given in SI Appendix, Table A2. In hybrid breeding, selection candidates are evaluated not only for their per se performance (PP) but mainly, for their combining ability with a tester from a different heterotic group. We, therefore, considered both the PP of the LS and of GC-S1:2 and DH lines as well as their testcross performance (TP) with an inbred line from the dent heterotic pool. Assuming absence of epistasis, the PP of fully inbred generations  and of all testcrosses can be described with a purely additive model . For PP of the LS and for GC-S1:2 lines, the mean and dispersion of the genotypic values depend on unknown landrace- and capture line specific dominance effects The conceptual differences between the pure and the admixed approach with respect to means and genetic variances in DH and GC are visualized in ectively .SI Appendix, Fig. S2). Phenotypic values should be indicative of genotypic values, as heritabilities were high, ranging between 0.85 and 0.93 . The LS flowered significantly earlier than most DH lines, and estimates of In the following, we use the trait flowering time exemplarily to link theoretical and experimental results . If the capture line contributes alleles not present in the landrace at many loci and the allelic effects at these loci differ substantially from the respective landrace alleles, the genetic variance among GC lines can be equal to or even larger than the variance among DH lines.We derived expected genetic variances of the DH and GC populations . The DH 2+14σA*2 , with σASI Appendix, Fig. S2 A and B). For PP, the ratio SI Appendix, Fig. S2 D and E and Table S2).Estimates of genetic variances for flowering time were significant for PP and TP in both landraces  , demonstrating that GC lines enriched with earliness alleles can be selected without strong overrepresentation of the FV2 genome.One concern with the GC population is the overrepresentation of the capture line genome in the progeny after selection. The proportion of FV2 genome of GC-SSI Appendix, Figs. S3 and S4. In general, experimental results were in agreement with theoretical expectations and highly consistent across landraces. Estimates of SI Appendix, Table S2).Results for the other traits are presented in ρ of genome-based prediction is the success criterion for genomic selection. Increasing the sample size of the training set affected the magnitude and precision of ρ, and no plateau was reached up to N = 250 . With respect to marker density, an increase in prediction accuracy could be observed up to 15,000 SNPs . Prediction accuracies ρ were consistently higher in DH lines compared with GC lines for the two plant height traits for all tested sample sizes of the training set. Differences were most pronounced for small sample sizes. For flowering time, differences between the two types of populations were negligible. Yield and dry matter content were assessed in testcrosses only. Accuracies for yield exceeded 0.5 in DH lines even with sample sizes N < 200 . However, in the GC lines, prediction for yield failed (ρ = −0.09 in KE) and was very low for early plant height. The strong decrease in prediction accuracies of testcross traits in the GC can partially be accounted for by a combination of nonsignificant genetic variances in a high number of training sets  and the limited size of the prediction sets in cross-validation (N = 25). In DH lines, however, testcross accuracies exceeded those of per se performance in some cases, despite lower genetic variances, lower heritabilities, and smaller training set size .The accuracy SI Appendix, Fig. S5 C and D) were low . The higher accuracies observed within DH lines  were not reflected in prediction across populations. Accuracies were similar irrespective if the prediction model was trained on DH to predict GC or vice versa. We also investigated if combining the two populations yielded a predictive advantage over within-population prediction. Despite a substantial increase in sample size, accuracies changed only marginally (from 0.53 to 0.55 on average across traits) , which was not expected considering the increase in prediction accuracy within populations with increasing N .Prediction accuracies across the two types of populations . For GC lines, higher values were obtained especially for final plant height and flowering time (0.25 ≤ ρ ≤ 0.29), most likely due to shared haplotypes originating from FV2, resulting in much higher linkage phase similarities of GC compared with DH populations  yielded estimates of ulations C.ρ varied substantially for the different (cross-)validation runs  within and across populations as well as across landraces. In testcrosses with a training set size of N = 75 (per se N = 200), this was most pronounced. The variation was in part attributable to nonsignificant estimates of the genetic variance in the training set, which was more common in GC and most pronounced in testcross prediction.Estimates of Extraction of beneficial haplotypes from landraces is a long-term endeavor. In landrace genomes, favorable alleles for one trait are often in high LD with unfavorable alleles for the same or other traits, and consequently several rounds of recombination and selection are required to close the performance gap to elite material and reduce linkage drag . With thPrediction accuracies achieved in this study with landrace-derived DH lines clearly demonstrate that genome-based selection has great potential. Cross-validated accuracies for prediction of total dry matter yield were as high as 0.58 in testcrosses of PE DH lines (0.51 in KE) and even higher for other traits, despite sample sizes of less than 150 DH lines in the training set. Correlated estimated genetic values for PP and TP indicate effective genomic selection for traits like flowering time and plant height on the per se level, carrying over to correlated response for TP.SI Appendix, Fig. S1).In landrace-derived DH populations, prediction accuracies should be merely a function of LD between markers and quantitative trait loci (QTL), as gametes are sampled at random from the ancestral population. Thus, it was surprising that accuracies were of similar magnitude as reported for elite maize germplasm with much higher LD and substantial relatedness between genotypes . InflatiN ≥ 200 due to large additive genetic variance, high heritabilities, and moderate LD.We, therefore, conclude that in DH populations derived from landraces preselected for molecular and phenotypic properties as suggested by Mayer et al. , predictSI Appendix, Fig. S1), affecting locus-specific contributions to the total genetic variance. If the capture line carries an allele present in the ancestral landrace, the locus-specific variance in the GC decreases compared with the DH population, except for loci with extreme allele frequencies in the ancestral landrace . If the capture line carries an allele not present in the ancestral landrace, the locus-specific variance in the GC will depend on the effect of this allele as well as on the frequencies and effects of the alleles in the ancestral landrace. If the allele of the capture line exhibits dominance over the landrace alleles . Thus, when training the model on DH or GC lines, the weight assigned to individual SNPs can differ markedly between the two populations, explaining the fairly low prediction accuracies across populations, irrespective if model training was conducted on DH or GC lines.Some landrace populations carry high genetic load, leading to low efficiency of DH production. Thus, crossing the landrace with an inbred capture line from a different genetic background might be the only option to avoid homozygous deleterious allele combinations. So, what are the consequences for prediction accuracies in comparison with DH populations? As expected from theory and observed in the experimental populations of this study, average allele frequencies of polymorphic SNPs were shifted toward more unbalanced allele frequencies in the GC lines . For quantitative traits, however, many loci contribute to the genetic variance, and unless a large proportion of causal variants for the traits of interest is known, molecular parameters will provide little guidance on the choice of capture line. In this study, the phenotypic per se performance of inbred line FV2 compared with the LS and the mean of the DH lines provided a first indication for which traits the capture line might contribute alleles not present in the ancestral landrace and which type of gene action to expect. It remains to be shown for other GC populations derived from different landraces and capture lines if this pattern holds. We could also show that dominance interactions with the tester alleles differed for landrace and capture line alleles, affecting prediction accuracies in the DH and the GC populations differently. Thus, not only the capture line per se but also its interaction with the tester had a direct effect on the genetic variance accessible for selection.In this study, we investigated the potential of genome-based prediction to increase the frequency of favorable alleles of target traits in landrace-derived populations. We conclude that the pure approach is to be preferred over the admixed approach, because with the admixed approach a substantial reduction in prediction accuracy must be expected unless prediction is driven by capture line alleles. When implementing the admixed approach, the choice of capture line will have a major impact on the success of the prebreeding program. It determines the mean and genetic variance of the GC population and the risk of masking favorable landrace alleles. Molecular data can inform about locus-specific allele frequencies in the ancestral landrace and the capture line, and under certain assumptions, these allele frequencies translate directly into expectations for the molecular and genetic variance in the GC population  the sample of the ancestral landrace (LS), 2) the landrace plants used for DH induction, and 3) the landrace plants used to pollinate the capture line. For production of testcross seed, randomly chosen lines from each population as well as FV2 and plants sampled from the ancestral landrace were hand-crossed as pollinators onto the inbred line F353 , a prominent line of the European dent heterotic group.We applied two different strategies  for samp−2.The DH and GC populations were evaluated in adjacent field trials connected through common checks. Field experiments for the DH populations were described in detail by Hölker et al. ; phenotyα-lattice design with 200 entries was used. Testcrosses of the LS and of two inbred lines together with six commercial hybrids were included as checks in all trials, and FV2 was included in GC trials only. Plots were double rows of 6 m length, with 0.75 m distance between rows and planting density of 9 or 11 plants m−2. Sowing, fertilization, and plant protection in per se and testcross evaluation followed standard agricultural practice at the experimental stations.Testcross performance (TP) was evaluated in two environments (ROG and EIN) in 2019. Testcrosses of DH lines were grown in four 10 × 10 lattice designs; for GC lines, a generalized The traits plant height at V6 stage , final plant height , flowering time , dry matter content , and total dry matter yield  at forage harvest were investigated.i = 1, 2, 3, 4 denotes four groups ; j = 1, 2, 3, 4 denotes the different populations ; μ is the overall mean; i; j in groups i = 1 and 2; k nested in group i and population j; o; j and environment o; and k and environment o. The effects j = 1, 2, 3, 4), assuming that DH and GC lines across and within landraces were stochastically independent. Residuals were assumed to be normally distributed with mean zero and four heterogeneous variances, one each for α-lattice design used in GC trials. Variance components and their SEs were estimated with ASReml-R package 3.0 (h2) and variance component estimates exceeding twice their SEs were considered significant. For obtaining best linear unbiased estimates (BLUEs) of the genotypic value of each entry, the model from t-tests) between LS, DH, GC, and FV2 in linear contrasts calculated with the package asremlPlus , DH lines, and GC plants were genotyped with the 600k Affymetrix Axiom Maize Array . The quaWe sampled 80 gametes from each population  and landrace with 500 replicates for comparing the number and percentage of polymorphic markers across populations.r2  between populations were calculated according to Schopp et al.  in several scenarios for PP and TP, always applying the modelVanRaden , and σg2on 3.6.0 ] and theon 3.6.0 . Variancon 3.6.0 .ρ) is reported as the correlation between predicted and unobservable true genetic values. Estimates of ρ were obtained from the Pearson correlation between the observed phenotypes and the estimated genetic values divided by the square root of h2 of the prediction set  or N = 75 (TP) lines were sampled randomly from one population (either DH or GC) for training the model. The prediction set always comprised a disjoint set of N = 50 (PP) or N = 25 (TP) lines either from the same or from a different population. Sampling was repeated 100 times. The same sampling procedure was applied for investigating across landrace predictions using the same type of population of the other landrace as the prediction set.For comparing the prediction accuracy Supplementary File


Acacia hilliana) and curry wattle (Acacia spondylophylla), two co‐distributed, widespread, Acacia shrubs of low stature, across the arid Pilbara region of north‐western Australia. Using chloroplast sequence and nuclear microsatellite data we evaluated patterns of population genetic and phylogeographic diversity and structure, demographic signals, ratios of pollen to seed dispersal, evidence for historical refugia, and association between elevation and diversity. Results showed strong phylogeographic  and contemporary  genetic structure in both species. This indicates limited genetic connectivity via seed and pollen dispersal associated with Acacia species of small stature compared to taller tree and shrub acacias across the Pilbara bioregion. This effect of stature on genetic structure is superimposed on moderate levels of genetic diversity that were expected based on widespread ranges . Contemporary genetic structure was congruent at the greater landscape scale, especially in terms of strong genetic differentiation among geographically disjunct populations in less elevated areas. Measures of diversity and connectivity were associated with traits of greater geographic population proximity, population density, population size, and greater individual longevity, and some evidence for range expansion in A. hilliana. Results illustrate that low stature is associated with limited dispersal and greater patterns of genetic differentiation for congenerics in a common landscape and highlight the complex influence of taxon‐specific life history and ecological traits to seed and pollen dispersal.Widespread plant species are expected to maintain genetic diversity and gene flow via pollen and seed dispersal. Stature is a key life history trait that affects seed and potentially pollen dispersal, with limited stature associated with limited dispersal and greater genetic differentiation. We sampled Hill’s tabletop wattle ( Acacia hilliana) and curry wattle (Acacia spondylophylla). Both species showed strong phylogeographic and contemporary genetic structure indicating reduced genetic connectivity when compared to taller shrub and tree Acacias in the Pilbara but maintained levels of genetic diversity comparable to that of other widespread AcaciaWe assessed phylogeographic and population genetic diversity and structure in two Acacia shrubs of low stature with widespread ranges and co‐distributions across the arid Pilbara region of north‐western Australia: Hill’s tabletop wattle ( SpeciesIdentifying macroevolutionary patterns of biogeographic concordance among co‐distributed taxa should also be balanced by biologically informed approaches that determine the contribution of taxon specific life history and ecological traits on levels and structuring of genetic diversity on more microevolutionary temporal and spatial scales , has increased haplotype diversity, a more complex haplotype network, and some evidence for population expansion  sequencing data and nuclear microsatellite (nDNA) data to assess intraspecific patterns of phylogeographic and population genetic diversity and structure in Hill’s tabletop wattle , that may also result in observable differences in contemporary patterns of genetic diversity and differentiation between the species.Limiting comparisons to congeneric species that share key life history traits that primarily affect dispersal allows for refined hypotheses regarding the contributions of life history and ecological traits to diversity, differentiation, and demography. Here, we test hypotheses based on intrinsic taxon‐specific life history and ecological traits to improve understanding of macro and microevolutionary drivers of genetic diversity and differentiation for a Figure . Both spAcacia shrubs of small stature is placed in the context of highly comparable studies, that used a similar suite of chloroplast sequencing data and nuclear microsatellite data and the same sampling approach and sampling numbers, on several Acacia with variable species ranges across the Pilbara and that are either trees or large shrubs. We make the following predictions for A. hilliana and A. spondylophylla; (i) they will exhibit levels of haplotype and nuclear diversity and contemporary genetic connectivity comparable to those of other widespread Acacia present in the Pilbara; (ii) given that low growing habit likely leads to more restricted seed dispersal, they will exhibit stronger phylogeographic structure compared to that of tall shrub and tree Acacia present in the Pilbara. Patterns of contemporary genetic structure and diversity are likely to be largely congruent for A. hilliana and A. spondylophylla as they have experienced the same historical geographic, geological, and climatological phenomena at the regional scale, although a greater degree of population disjunction, lower localized population density, smaller populations, and shorter individual longevity and lower fecundity in A. spondylophylla leads to another prediction of (iii) decreased genetic diversity within populations and increased genetic variation among populations of A. spondylophylla. We also assessed phylogeographic concordance for A. hilliana and A. spondylophylla in terms of the major landscape features of the Hamersley and Chichester Ranges, that may act as potential historical refugia, and the impact of population elevation on measures of contemporary diversity.We use a standardized comparative approach at macro and microevolutionary spatial and temporal scales to gain insight into how levels and patterns of genetic diversity are influenced by taxon‐specific life history and ecological traits. Our assessment of two widespread 22.1Acacia trees and shrubs over spinifex (Triodia spp.) hummock grasslands. To the north, the basaltic, granitic/greenstone Chichester Range (up to 367 m) comprises an escarpment and tableland with jagged peaks, gorges, rolling hills, and tree‐lined water courses with spinifex, hummock grasslands and Eucalyptus leucophloia steppe. The region is recognized as a significant center of species endemism across the flora and fauna biota of the Australian continent  is a semi prostrate sub‐shrub reaching 0.3–1 m in the Pilbara. Plants have bright yellow, erect, cylindrical flower spikes to 45 mm from April to November  grows slightly larger but is still a small, spindly shrub typically 1–1.5 m. Plants have resinous leaves, stems, and pods that produce a curry‐like odor, with larger, bright yellow, round inflorescences appearing from May to August . Plants grow in shallow, sandy or rocky soil on ironstone hills and in gullies, as well as along creeks and colluvial flats on spinifex plains .Curry wattle . Sampling sites (herein populations) were selected to cover the distributional range of each species across the Pilbara. Collections of phyllode material were made from 24 plants in each of 22 populations of A. hilliana and 14 populations of A. spondylophylla .Distributional information on each species across the Pilbara bioregion was determined from specimens lodged at the Western Australian Herbarium through the FloraBase website  were tested in each species with DNA amplification and analysis following Byrne and Hankinson (https://agrf.org.au). Regions were selected for further analysis based on sequence quality and nucleotide diversity. Eight individuals randomly selected from each population were sequenced at each region using ndhA, psbA‐trnH, rpl32‐trnL and trnV‐ndhC for A. hilliana, and PetD, rpl32‐trnL and trnV‐ndhC for A. spondylophylla. Sequence reactions were carried out following Byrne and Hankinson (www.ncbi.nlm.nih.gov/genbank) database under accession numbers MW138725‐MW138756 and MW696776‐MW696791.Non‐coding chloroplast regions known to show variability in Australian plant species  database under accession numbers MTO80657–MTO80688.Species specific nuclear microsatellite markers were developed for each species through partial genome sequencing on a 454 platform at the AGRF, using the methods of Gardner et al. . The resp < .0001 with Bonferroni correction applied) departure from HWE was found in 15 out of 352 locus/population combinations for A. hilliana and in nine out 240 locus/population combinations for A. spondylophylla, but never for more than four loci per population. There was potential evidence of null alleles at some locus/population combinations for both species. Null allele frequencies ranged from 0.00 to 0.376 for A. hilliana and from 0.00 to 0.365 for A. spondylophylla  so null alleles are likely to have little effect on related analyses  LD was detected in 14 out of 120 locus combinations for A. hilliana and in two out of 120 locus combinations for A. spondylophylla.Utility of loci was assessed for each species. Departure from Hardy–Weinberg equilibrium (HWE) and Linkage Disequilibrium (LD) among locus pairs were tested using exact tests implemented in GENEPOP v4.2  and the number of private haplotypes (Hp) detected in each population of each species were counted. Estimates of nucleotide and haplotype diversity including the total number of variable sites (S), total number of haplotypes (h), overall haplotype diversity (Hd), nucleotide diversity (π), and parameters of neutrality tests that can changes in population size , were obtained for each species using DNASP v6.12.03  and total diversity (hT), as well as coefficients of phylogenetic differentiation for unordered (GST) and ordered alleles (NST), were calculated with PERMUT v2.0 following Pons and Petit (NST was significantly greater than GST using the U test for 1000 permutations in PERMUT.The number of haplotypes (nd Petit . Phylogeε was set to 0 and MJ networks were verified using the Reduced Median (RM) algorithm. No star contraction pre‐processing was applied. Haplotypes present in each population were mapped across species distributions.Maximum parsimony phylogenetic networks , expected (He) and observed (Ho) heterozygosity, and proportion of polymorphic loci (%P), as well as the Fixation index (FIS), were calculated using MSA v4.05  was calculated using GenAlEx v6.5 , and the maximum in ΔK  values for all individuals were graphed and mapped for populations. Given the propensity of the ad hoc model likelihood statistic ΔK to identify K = 2 when further genetic structuring is present . We then conducted hierarchical structure analysis on each subcluster in the same manner as for the full data set.Genetic structure was investigated using the Bayesian assignment approach in FST) and Jost’s estimate of allelic differentiation  was conducted based on pairwise individual genetic distances using a standardized data set for populations of each species in GenAlEx. Global and pairwise among population measures of genetic divergence including allelic fixation (FST (FST/1−FST)) with log geographic distance among populations. Significance tests were conducted using 999 permutations of sample site locations with all analyses conducted using GenAlEx.Mantel tests were used to test for the association of genetic differentiation (linearized H) and nuclear diversity estimates  between species were assessed using single factor ANOVA without replication performed in R v3.6.2  and nuclear  diversity measures between the topographically complex Hamersley and Chichester Range populations and populations located in the surrounding lowlands were undertaken separately for each species using single factor ANOVA in R. Analysis of differences in chloroplast (H) and nuclear  diversity measures with elevation (m) were also undertaken for each species using regression with bounded genetic diversity measures (Ho and He) arcsine transformed prior to analysis. The ratio of pollen to seed dispersal was calculated for each species following Ennos (A(1+FIS)−2C]/C, where A = (1/FST)−1 and where C = (1/GST)−1.Differences in chloroplast diversity  between species (Table F(1 (df between groups), 34 (df within groups)) = 1.711, p < .199). Acacia hilliana had greater overall haplotype diversity (Hd) and lower genetic differentiation among populations than A. spondylophylla  was not significantly greater in populations located on the ranges compared to populations located in the rest of the Pilbara for either species  = 1.03, p = .322 for A. hilliana; F = 0.809, p = .386 for A. spondylophylla).For A. hilliana, two dominant haplotypes with central positions within the network (H10 and H11) were present in a number of central , southern  and far north‐eastern populations . However, the most divergent haplotypes  were also located in central (BAL) and north‐eastern populations  was present in central populations  and another common haplotype with a central position within the network (H1) was present at both the most north‐eastern (BAT) and the most western populations  were all significantly greater for A. hilliana  = 38.869, p < .000; F = 17.634, p < .000; F = 26.856, p < .000). Observed heterozygosity was lower than expected heterozygosity at most populations for each species. Although fixation indices varied among populations, mean values were low and generally positive for both species  at K = 6 and at K = 8 although with some standard deviation, and further leveling off at K = 9 with no deviation  increased steadily leveling off at K = 5 but with a large standard deviation, then continued to increase leveling off with no significant deviation at K = 11  are indicated  increased steadily leveling off at K = 4 with little significant deviation , are indicated  increased steadily leveling off at K = 4 with no significant deviation  are indicated  than for A. spondylophylla , although allelic differentiation among populations was greater for A. hilliana  than for A. spondylophylla (DST 0.448 p < .001). Pairwise values of fixation (FST) among populations were variable, ranging from 0.071 to 0.406 for A. hilliana  compared to A. spondylophylla (49%).AMOVA revealed that each species had the greatest degree of genetic diversity partitioned within populations. The degree of genetic diversity partitioned among populations was lower for A. hilliana . There was a weak but significant positive correlation across the sampled region for A. spondylophylla .There was a significant positive correlation between geographic distance and genetic differentiation across the sampled region for He) was observed in populations of A. hilliana located on the ranges compared to populations located in the rest of the Pilbara  = 4.53, p = .046), and values of Ho and He were significantly greater for A. hilliana populations with higher elevation . Pollen to seed dispersal ratios were moderate; 12.1:1 for A. hilliana and 15.6:1 for A. spondylophylla.Significantly greater contemporary diversity  shrub A. ancistrocarpa  in populations of A. hilliana located on the Hamersley and Chichester Ranges compared to populations located in the rest of the Pilbara, and significantly greater diversity (He and Ho) for A. hilliana populations with elevation.Genetic differentiation among populations located on the Hamersley and Chichester Ranges was further pronounced for 4.2A. hilliana and A. spondylophylla were not consistent with our prediction of genetic connectivity via contemporary gene flow being comparable to that of other widespread Acacia in the Pilbara. Values of genetic differentiation for A. hilliana and A. spondylophylla  were much greater than those reported for Pilbara populations of the widespread A. ancistrocarpa  and north‐western (PAN) geographical extremes. This may be a result of homoplasy rather than long distance seed dispersal, and a similar incidence of a geographically disjunct haplotype has been noted in A. ancistrocarpa, which has a similar population range within the Pilbara  and fecundity than A. spondylophylla  and sparsely scattered  or centrally located populations also largely on these ranges (A. spondylophylla), were differentiated from more peripheral populations at lower elevation. Limited admixture and high measures of differentiation indicate limited contemporary gene flow between these differing landscapes. Significantly greater contemporary diversity was also observed in populations located on the ranges compared to populations located in the rest of the Pilbara. A similar pattern was found for the near Pilbara endemic A. atkinsiana ; formal analysis ; writing – original draft (lead); writing – review and editing (lead). Rachel Maria Binks: Data curation (lead); formal analysis ; methodology ; writing – review and editing (supporting). Sarah‐Louise Tapper: Data curation (supporting); formal analysis . Bronwyn Macdonald: Data curation (supporting); formal analysis (supporting); resources (supporting). Shelley McArthur: Resources (supporting). Margaret Hankinson: Data curation (supporting); formal analysis (supporting). David J. Coates: Resources ; writing – review and editing (supporting). Stephen vanLeeuwen: Conceptualization ; funding acquisition (lead); project administration (supporting); resources ; writing – review and editing (supporting). Margaret Byrne: Conceptualization ; methodology (lead); project administration (lead); resources ; writing – review and editing (supporting).7The authors declare no conflict of interest.8Table S1.Table S2.Click here for additional data file.


Ammopiptanthus mongolicus, a broad-leaved evergreen shrub species in a relatively homogeneous temperate Asian desert environment, to explore the environmental factors that limit the expansion of desert plants. Temperate deserts are characterized by severe drought, a large diurnal temperature range, and seasonality. Long-standing adaptation to the harsh desert environment may confine the genetic diversity of A. mongolicus, despite its distribution over a wide range of longitude, latitude, and altitude. Since range edges defined by climate niches may have different genetic responses to environmental extremes, we compared genome-wide polymorphisms between nine environmental core populations and ten fragmented peripheral populations to determine the “adaptive peripheral” populations. At least four adaptive peripheral populations had similar genetic-environmental association patterns. High elevations, summer drought, and winter cold were the three main determinants of converging these four adaptive peripheral populations. Elevation mainly caused similar local climates among different geographic regions. Altitudinal adaptation resulting from integrated environmental-genetic responses was a breakthrough in breaching niche boundaries. These peripheral populations are also located in relatively humid and warmer environments. Relaxation of the drought and cold constraints facilitated the genetic divergence of these peripheral populations from the core population’s adaptive legacy. We conclude that pleiotropic selection synchronized adaptative divergence to cold and drought vs. warm and humid environments between the core and peripheral populations. Such parallel adaptation of peripheral populations relies on selection under a background of abundant new variants derived from the core population’s standing genetic variation, i.e., integration of genetic surfing and local adaptation.Range expansion requires peripheral populations to shift adaptive optima to breach range boundaries. Opportunities for range expansion can be assessed by investigating the associations of core-periphery environmental and genetic differences. This study investigates differences in the core-periphery adaptation of  Different responses of populations to environmental stress may result in adaptive divergence of species . Many evEcological niche modelling (ENM) theoretically predicts a species’ distribution range under the assumption of phylogenetic niche conservatism (PNC), i.e., slow temporal changes in the hypothetical fundamental niche . In pracEnvironmental heterogeneity may lead to fundamental niche differences in organisms with overlapping geographic space (G-space) . Thus, eAmmopiptanthus mongolicus (Maxim. ex Kom.) S. H. Cheng (Leguminosae), a second-grade vulnerable (VU) plant of the China Red List of Threatened Species (the Red Book)  , to asseA. mongolicus tends to expand under climate warming and is affected mainly by temperature seasonality and precipitation in the winter (coldest quarter) and summer (wettest month) (A. mongolicus under different warming scenarios: eastward under the mild warming scenario (greenhouse gas emission scenario RCP4.5) but westward under severe warming (RCP8.5)  resistance gene expression in A. mongolicus  . Since p(RCP8.5) . Its het(RCP8.5) . In addi(RCP8.5) , the geo(RCP8.5) , and loc(RCP8.5)  also detngolicus , suggestA. mongolicus. Briefly, GEA analysis is a non-hypothesis-driven genome-wide association approach to determining selective drivers by identifying genes [or even anonymous single nucleotide polymorphisms (SNPs)] correlated with individual environmental predictors (A. mongolicus (Genome-environment associations (GEAs), also known as genotype-environment associations, provide an opportunity to quantify the core-periphery adaptive divergence of edictors . The seledictors , and theedictors . These tngolicus  and the A. mongolicus, environmental heterogeneity has been proposed as a limiting factor in its dispersal. New variants arising from genetic surfing may drive the expansion and colonization of peripheral populations. We adopted the E-space concept to define the core and peripheral populations and compare their genomic diversity patterns. Selective drivers that diverged the core and peripheral populations were then determined using the GEA strategy.We hypothesized that genetic surfing in peripheral populations facilitates adaptive divergence from the core populations. Despite the high dispersibility of A. mongolicus, they did not provide the exact location of the modelling. We used a topographic variable, the altitude (m), and 55 environmental variables at a spatial resolution of 2.5 arc-minutes (approximately 4.5 km × 4.5 km at the equator) from WorldClim ver. 2 (–2 day–1), 12 monthly water vapour pressures (kPa), and 12 monthly wind speeds (m s–1). Variance inflation factor (VIF) analysis was first conducted to remove collinear factors with VIF < 6, which left seven factors: altitude , March solar radiation (srad03), January water vapour pressure (vapr01), precipitation in the wettest month (bio13), mean diurnal range (bio2), mean temperature in the driest quarter (bio9), and precipitation in the driest month (bio14). We conducted the maximum entropy model in MaxEnt v3.3.3  using the seven environmental variables. The PCA scatterplot was visualized by the R package ggplot2 . We defiA. mongolicus in G-space, while the populations outside the core area present a more sporadic distribution. It is difficult and unrealistic to collect all sporadic peripheral populations comprehensively. Nevertheless, the sampled peripheral populations in this study have almost covered most of the entire distribution range of A. mongolicus, including Inner Mongolia, Ningxia, and Gansu in China  and ten peripheral populations outside the suitable area . We prefixed “c” and “p” to represent the core and peripheral populations, respectively. The core population defined here is also the main distribution range of in China . Each poA. mongolicus is relatively large, approximately 820 Mb [2n = 18, A. mongolicus. Genomic DNA was extracted using the DNAquick Plant System kit  and digested by the enzymes RsaI and HaeIII. After ligation and PCR amplification, fragment sizes of 364–414 bp were sequenced by 125 paired-end protocols on the MiSeq Illumina platform. Trimmomatic v0.38 , Wright’s inbreeding coefficient (FIS), observed (Ho), and expected heterozygosity (He), nucleotide diversity (π), and private alleles of each population using the populations program in STACKS v2.53 . Two-tailed P < 0.05 (standard deviation > 1.96) was set as the outlier threshold to define the candidate SNPs on each significant constraint axis. Then, the candidate adaptive SNPs were associated with each environmental variable, with the most correlated environmental variable being the potential driver of local adaptation.Distance-based redundancy analysis (dbRDA) was used to test the explanatory proportion of the seven climate variables for genetic composition in terms of allele frequency. We transformed the raw allele frequency data into a Bray-Curtis distance matrix  for prinatistics  by the aK, which describes the clustering of individuals into populations adequately and the gradient of selection  to define the strength of the environmental association. The false discovery rate (FDR) was set to 0.05 to determine the environmental-associated SNPs. These SNPs were further used to conduct PCA to visualize the patterns of clustering of populations by each climate variable.Because dbRDA tests the effect of climate on overall instead of specific genetic variation, we further performed latent factor mixed model (LFMM) analysis by applying the hierarchical Bayesian method to identify the specific effect of each climate variable by controlling for residual population structure . The latelection , was detA. mongolicus is mainly distributed in central and western Inner Mongolia and northern Ningxia Province  was between 0.130 and 0.226, indicating outcrossing or a nearly panmixia mating system (FIS = -0.193 ∼ 0.009) in A. mongolicus. According to the sNMF, dbRDA, and LFMM, the ten peripheral populations were divided into non-adaptive  and adaptive peripheral populations  (see below). The adaptive peripheral populations harboured the most private SNPs (63.75 ± 41.22 SNPs), followed by the non-adaptive peripheral populations (13.00 ± 9.22 SNPs), while the core populations had the fewest (5.22 ± 5.90 SNPs). The higher frequencies of private SNPs in the peripheral populations reflect the founder phenomenon and rapid expansion. However, compared with the core populations , genetic diversity dropped only in the non-adaptive populations  and not in the adaptive peripheral populations   to 7,335 SNPs (68.52%) in each population, including 0–123 private SNPs. The ± 0.009) , suggestK = 2, which separated the peripheral populations pAYE, pYL, pZS, and pAZM from the other populations , pAZM was separated as an independent group, and pZS was admixed between pAZM and pAYE+pYL  within the core populations and between the core and peripheral populations  revealed that environmental factors explained 55.58% of the overall genetic variation. Among all transformed axes of the dbRDA, only the 1st axis significantly affected genetic variation  that significantly rejected the null model (P = 0.090) associated with higher elevation, with an explanatory proportion of 7.8% (P = 0.312) and the other environmental factors with non-significant and relatively low explanations , 151 , 48 (bio2), 31 (bio13), 27 (bio14), and 11 (srad03) associated SNPs .We then performed PCA on these environment-associated adaptive SNPs to examine their groupings. The results showed that altitude and bio13 clearly separated pZS, pAYE, pYL, and pAZM from the other populations . These fA. mongolicus compared with other desert plants [see A. mongolicus, thus explaining the apparent population genetic differentiation. The genome-wide SNPs also confirmed the IBE inferred by Unlike the apparent population genetic clustering inferred by dominant markers , the genants see . Jiang eA. mongolicus, the environmental peripheral populations are not necessarily at the geographical margins. For example, pAZM, which is close to the geographic core, was defined as a peripheral population by high loadings of altitude and precipitation in the driest month (bio14)  . This congolicus . Under Pngolicus , which ixpansion .FST) from the core to peripheral populations. A notable exception is the core population cAG defined by E-space, which possesses genetic characteristics like a peripheral population. Almost identical genotypes with high frequencies of private alleles in cAG samples suggest a newly established population. This new population may be expanded from a few founders or tillered from the adventitious roots of broken branches. The four peripheral populations  form distinct clusters with respect to altitude- and climate-associated SNPs as a result of spatial selection and sorting, whereas the other peripheral populations have fewer private SNPs with low genetic diversity, indicating that adaptation did not occur.Peripheral populations defined according to the E-space concept share the same characteristics as G-space edge populations: low genetic diversity, high frequencies of rare and novel alleles, and low genetic supplementation  between the core and the adaptive peripheral populations indicates that mating systems are unaltered by distribution (HO) in peripheral populations not only suggests the newly derived mutations from standing variations but also implies the environmental tolerance for alloploidy, i.e., relaxation of selective constraints  may constrain adaptative flexibility  in desert organisms because of the reduction in the G × E effect . Altitudngolicus . Such geR2 > 0.6 and P < 0.0001) were bio5 , bio7 , bio10 , and summer solar radiation . Thus, altitudinal adaptation appears to be related to summer temperature and sunshine. In deserts, summer is the rainy season, which is the regeneration and growing season of this desert shrub. The negative correlations  suggest that low light and low temperature in the mountains adversely affect the growth of A. mongolicus. The distinct grouping of altitude-related SNPs demonstrates adaptive changes in these four adaptive peripheral populations in high-altitude environments, despite the unclear functions of these anonymous SNPs.Elevation is a topographic factor involving multiple climate factors . The cliA. mongolicus must rely on physiological adaptation to temperature fluctuations because cold damage cannot be prevented via physical defoliation  diverged the four adaptive peripheral populations from the other populations, revealing adaptive divergence. an 30 cm . Moisturngolicus . A. mongneration . Three oneration . The adanditions , a wettenditions . This ecA. mongolicus. Under long-term aridity and extremely cold conditions, 1,594 cold and drought unigene sets (namely the AmCDUnigene set) were identified , which respond to dehydration stress  (AmNAC11)   and NAC AmNAC11) . These seiotropy . HoweverA. mongolicus. Thus, relaxation of selective constraints more readily explains the genetic divergence of these four adaptive peripheral populations, which is also supported by the increase in new mutations (private SNPs). By contrast, the core populations underwent long-term adaptation ca. 4.5 Mya to the harsh desert environment after the rapid uplift of the Qinghai-Tibetan Plateau .  Plateau . That isA. mongolicus but also evidence that adaptive divergence may occur through local adaptation and through escape from long-term adaptive constraints via pleiotropy.In relatively homogeneous desert environments, it is difficult to track adaptations to heterogeneous environments in the context of a long-standing adaptive legacy. This study demonstrates that peripheral populations have a greater chance of escaping the selective constraints of harsh environments by acquiring and retaining new mutations. The (adaptive) optimal shift at high altitude and the relaxation of cold and drought pressures were associated with convergence of the genetic characteristics of allopatric peripheral populations according to the phenomenon of parallel adaptation. Clustering in populations exposed to similar  multiple environmental stresses suggests the importance of pleiotropic selection in adaptation to dramatic annual climate fluctuations in temperate deserts. This study provides not only a more accurate species distribution and spatial-genetic structure of https://data.mendeley.com/, DOI: 10.17632/9g8fd4t2nj.2.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: R-HG and P-CL conceived and designed the research. L-DP provided and checked all occurrence data of the study species. Y-ZY, L-DP, and R-HG collected the samples. Y-ZY and M-XL conducted the experiments. M-XL, J-TC, and P-CL analyzed the data. P-CL wrote the draft with the input of M-XL and J-TC. All authors read and agreed to the published version of the manuscript.


Southeast Asia comprises 11 countries that span mainland Asia across to numerous islands that stretch from the Andaman Sea to the South China Sea and Indian Ocean. This region harbors an impressive diversity of history, culture, religion and biology. Indigenous people of Malaysia display substantial phenotypic, linguistic, and anthropological diversity. Despite this remarkable diversity which has been documented for centuries, the genetic history and structure of indigenous Malaysians remain under-studied. To have a better understanding about the genetic history of these people, especially Malaysian Negritos, we sequenced whole genomes of 15 individuals belonging to five indigenous groups from Peninsular Malaysia and one from North Borneo to high coverage (30X). Our results demonstrate that indigenous populations of Malaysia are genetically close to East Asian populations. We show that present-day Malaysian Negritos can be modeled as an admixture of ancient Hoabinhian hunter-gatherers and Neolithic farmers. We observe gene flow from South Asian populations into the Malaysian indigenous groups, but not into Dusun of North Borneo. Our study proposes that Malaysian indigenous people originated from at least three distinct ancestral populations related to the Hoabinhian hunter-gatherers, Neolithic farmers and Austronesian speakers. Despite this fascinating diversity, the genetic history of the region remains under-studied and several outstanding gaps regarding the peopling of this region by anatomically modern humans (AMH) still exist. The four most-debated issues concerning the history of AMH in SEA relate to 1—The timing of their arrival in SEA; 2—Origins of hunter-gatherer populations in SEA and their relationship to the Hoabinhian culture; 3—Process of transition from foraging to farming lifestyle, and 4—Development of the cultural groups today recognized as Austroasiatic and Austronesian3. According to archeological and early mitochondrial (mt) DNA investigations, the presence of AMH in SEA dates back to around 70–50 k years ago (kya)8. Later, genome-wide and ancient DNA studies postulated that the AMH entered the region following the “Out-of-Africa” human migration, perhaps via the southern coastal route, and subsequently spread into East Asia (EA), Papua New Guinea, and Australia13. Subsequently, migrations from EA during the late-Pleistocene and Holocene, and population movements within the region, have shaped today’s population structure of SEA15. The geographical location of Malaysia, a country that is physically split between mainland Asia and Borneo with significant population diversity, provides us with an opportunity to study the population history in SEA.Southeast Asia (SEA) has rich demographic, linguistic, and genetic diversity. The region is home to around 1249 ethnic groups belonging to five language families16. The myriad indigenous communities of East and West express high ethno-linguistic and cultural diversity. The indigenous populations of Peninsular Malaysia are known as Orang Asli . They comprise 0.7% of the Peninsular Malaysia population and are divided into 3 major groups including Negrito, Senoi, and Proto-Malay based on their morphological and ethnolinguistic characteristics17. Malaysian Negrito are hunter-gatherers who reside in the rain-forests of northern Peninsular Malaysia and are proposed to be descendants of the first settlers of Malaysia18. They speak the Northern-Aslian dialect of the Austroasiatic (AA) language family, and their tradition involves egalitarianism and a patrilineal descent system. Senoi inhabit the central parts of Peninsular Malaysia. They speak the central and southern dialects of the Aslian language, and they traditionally practice slash-and-burn farming. Proto-Malay speak the Malay dialect of the Austronesian language family. They mainly live in the southern parts of Peninsular Malaysia. Proto-Malay practice farming and rain-forest harvesting and their traditions involve a marked social hierarchy. Each OA group is further subdivided into 6 subgroups, which makes up 18 OA subgroups. In Sarawak, the indigenous people are collectively known as Orang Ulu  and comprise 40% of Sarawak’s population. The indigenous populations of Sabah make up 58.6% of Sabah’s population and are divided into 39 tribes. Dusun, Murut, Paitan, and Bajau are the major indigenous groups in Sabah16.Malaysia is divided into a western part comprising Peninsular Malaysia and an eastern part on the Island of Borneo comprising the States of Sarawak and Sabah. Indigenous populations comprise 13.8% of the about 32 million population of Malaysia19. Another theory by Benjamin (1985) proposed an in situ development and diversification of OAs20 following the first wave of human migration into Asia. Bellwood (1993) suggested that the ancestors of today Senois are associated with early Austroasiatic agriculturists who entered Peninsular during mid-Holocene era21. Later interactions between these Neolithic farmers and local hunter-gatherers (ancestors of Negritos) resulted in language shift in Negritos as well as intermediate phenotypical features in Senois. He suggested that Proto-Malays originated from Austronesian speaking farmers who migrated to Malaysia during “Austronesian expansion” approximately 5–7 KYA. Early mtDNA studies found both haplogroups unique to Peninsular Malaysia, and those stablished in Indochina in OAs which suggest gene flow from neighboring populations in SEA into OAs22. These studies identified two haplogroups of M21 and R21 in Negrito and Senoi with TMRCA around 30–50 KYA. Higher frequency of these two ancient haplogroups in Negritos could indicate that they are the most direct descendants of the earliest settlers of Peninsular Malaysia. Proto-Malay mainly harbor N21 and N22 haplogroups which may be associated with Austronesian expansion via Island Southeast Asia14. Genotyping studies highlighted genetic affinity between Malaysian Negritos, Andamanese and Filipino Negritos. This may represent an ancient link between these populations23. Whole genome-sequencing showed that Malaysian Negritos has the deepest divergence time from EA compared with the other two OA groups. This study also traced some level of gene flow from South Asia in OAs24.Early anthropological studies proposed multiple competing theories about the origin of OAs. The “layer-cake” theory postulated that all three OA groups originated outside of Peninsular Malaysia and entered Malaysia at different timesTo advance our knowledge of the genetic structure and history of Malaysia’s indigenous people explore their relationship with the ancient hunter-gatherer and agriculturist communities of Malaysia, we performed high-coverage whole-genome analysis of 15 Orang Asli and Orang Ulu individuals including Negritos , Senoi (MahMeri), Proto-Malay , and Dusun, and report the results of our analysis here.24. We used BWA v0.7.12 software to align the sequences to GRCh38 and GATK v3.5.0 for the variant calling. This dataset was merged with Human Genome Diversity Project (HGDP)-CEPH panel data63, Andaman Islanders64, Malay individuals from Singapore Genome Diversity Project (SSM)65. We also constructed a dataset using OAs and ancient AMH samples from southeast Asia  and reaches the highest frequency in Peninsular Malaysia32. Haplogroup N22a which was observed in MahMeri appears to be restricted to Peninsular Malaysia, although N22 has been recorded in low frequency elsewhere in SEA such as Philippine33 and Sumatra34. The E1a, M711, R9c1 haplogroups are prevalent in island Southeast Asia (ISEA) and are widely believed to be associated with the Austronesian expansion35.We determined the Y-chromosome and mitochondrial DNA haplogroups in the OAs and North Bornean samples Table . For mtD36. Interestingly, we found haplotypes R2a and R1a1ab in Malaysian Negrito. Haplogroup R2a is mainly present in SA38 and at lower frequencies in Central, Southwest, and EA40, while the R1a1a1b, and its sub-clades, comprise the major R1a sub-clades in Central and South Asia43.For the Y chromosome, OA harbor the R1a1a1b2a, R2a, K2b, K2b1, and O2b1 haplogroups. The K2b haplogroup and its subclade K2b1, which were observed in Malaysian Negrito and Seletar, have been reported in other SEA Negritos and OceaniaTo estimate the effective population size (Ne) and divergence time in OAs, we carried out a Multiple Sequentially Markovian Coalescent MSMC2) analysis. In order to have a better resolution, we first included four randomly selected individuals (8 haplotypes) from each population in the MSMC2 analysis. This limited us to only Jehai and Seletar tribes which have sufficient sample size  separated from each other approximately 2.6 kya.For the divergence time, we found that the split between Malaysian Negritos and EA took place around 14–13 kya Fig. b which i47. We also detected gene flow within OA groups, notably from Jehai (Negrito) to Jakun , and from MahMeri (Senoi) to Mendriq (Negrito).To investigate potential gene flow in the history of indigenous Malaysian and modern and ancient populations, we performed a TreeMix analysis Figs.  and S12.TreeMix analysis of Malaysian indigenous and ancient SEA samples revealed similar topology. The Hoabinhian samples clustered separately and next to the Andamanese-Papuan clade, whereas Neolithic SEA clustered with modern EA. Interestingly, our analysis revealed gene flow from two ancient samples from Malaysia, namely Ma911 (Hoabinhian hunter-gatherer) and Ma912 (Neolithic farmer), to the Malaysian Negritos.To better explore the existence of gene flow between OAs and the neighboring populations, within different OA groups and to further confirm the link between modern-day Negritos, Hoabinhian hunter-gatherer and Neolithic farmers, we conducted f4 tests Table . To asce6. Between 13 to 3 kya Hoabinhian hunter-gatherers occupied the Peninsular. The Hoabinhian culture with a stone tool industry characterized by unifacial pebble tools, are believed to originate from south China and spread throughout mainland Southeast Asia (MSEA) and island Southeast Asia (ISEA)48. Since 4 kya, this South East Asian nation also witnessed at least two waves of migration from Neolithic farmers and Austronesian speakers49. These different human migrations and settlements have resulted in Malaysia's rich present-day linguistic and anthropological diversity. Our study leverages new whole-genome sequencing data to dive deeper into the understanding of the genetic structure and history of the indigenous people of this nation.Malaysia which lies at the crossroads of South East Asia, has experienced multiple massive human movements over millennia. Archeological and genetic evidence shows that the presence of AMH in Malaysia dates back to at least 40 kya24, our new ADMIXTURE analysis revealed traces of South Asian ancestral component in OAs of Peninsular Malaysia. We could not detect this component in Dusun in Borneo. The presence of SA ancestral component in OAs has been previously reported51. This ancestral component might be attributed to the first wave of human migration into SEA via the southern coastal route or later gene flow from SA during the expansion of Indian culture into Peninsular Malaysia in the first century A.D.52. Archeological sites in the state of Perak provide evidence of Hindu civilization. Being on the maritime route between China and South India, the Malay peninsula was involved in this trade. The Bujang Valley, being strategically located at the northwest entrance of the Strait of Malacca as well as facing the Bay of Bengal, was continuously frequented by Chinese and south Indian traders. Such was proven by the discovery of trade ceramics, sculptures, inscriptions and monuments dated from the 5th to fourteenth century CE53. More studies are needed to address the source of SA ancestry in Peninsular Malaysia and its absence in Borneo.While indigenous Malaysians are genetically closer to EA populations, consistent with previous studies54. The Hoabinhian phase when the shelter was used for habitation and occasionally for burial, and the Neolithic phase when it functioned as a cemetery55. Radiocarbon dating showed that the Hoabinhian occupied the Gua Cha from 9 kya and later the Neolithic farmers used this site from 3 kya56. Our outgroup-f3 analysis is consistent with the archeological findings regarding the transition from hunting-gathering to farming lifestyle in the Gua Cha cave. While the Ma911 (Hoabinhian layer) shared most alleles with the Malaysian Negritos, the Ma912 (Neolithic farmer) was closer to the Senoi agriculturists. Our results confirm that modern Malaysian Negritos have been derived genetically from two ancient populations: the Hoabinhian hunter-gatherers and the Neolithic farmers who originated from South China or MSEA.Analysis of OAs with ancient DNA from the Gua Cha revealed the contribution of populations genetically close to these samples into the Malaysian Negritos gene pool. The Gua Cha site is a rock shelter in northern Peninsular Malaysia. Based on Sieveking (1954), two archeological phases are recognizable at this site57. For example, Jinam et al. (2013) reported recent admixture between Jehai and their neighboring Malay, whereas such admixture was absent in Kensiu (another Negrito group). We did not find any traces of Negrito or Hoabinhian ancestry in Dusun. Likewise, Yew et al. (2018) reported the absence of Negrito ancestry in North Borneo, Dayak, and Bidayuh populations. Considering the demographical and archeological evidence which supports the presence of Austro-Melanesian people on Borneo Island58, the best explanation for the absence of Negrito ancestry in Borneo could be the replacement of initial Austrolo-Melanesian inhabitants of the island by the Austronesians.Our analysis detected gene flow between different OA tribes, notably between Malaysian Negritos, with MahMeri and Jakun tribes. The admixture between neighboring OA tribes or between OAs and the Malay population has been reported previously32. Our results are consistent with Jinam et al. (2012) who reported only 4 mtDNA haplogroups (with N9a6b making up of 71% of mtDNA haplogroup frequency) in Seletar. Seletar are sea nomads who live along the strait of Johor . The history of Seletar is not well-documented. They are usually associated with the Orang Laut , a conglomerate of sea nomad tribes who occupied the strait of Melaka59. Our TreeMix and ROH results indicate that the Seletar are genetically closer to the Austronesian speakers, but they experienced severe genetic drift.Interestingly, all the Seletar samples carried mtDNA N9a6b haplogroup. N96a haplogroup seems to be confined to the ISEA and reaches the highest frequency in MalaysiaIn summary, our study suggests that at least 3 ancestral components were involved in shaping today's indigenous Malaysian populations, the Hoabinhian hunter-gatherers, Neolithic farmers, and Austronesian speakers. We also showed the genetic interaction between different Orang Asli tribes of Peninsular Malaysia.This study was reviewed and approved by the Monash University Human Research Ethics Committee (MUHREC), the Department of Orang Asli Development , the Research and Ethics Committee of the University of Technology MARA [Ref no: 600-RMI (5/1/6)], and the University of Malaysia Sabah Medical Research Ethics Committee [code: JKEtika 4/10(3)]. All methods were carried out in accordance to the principles of the Declaration of Helsinki. Before sample collection, we paid a courtesy visit to each tribe and obtained approval from the tribe’s chieftain and district offices. We also received approval from the chairperson of the Committee for Village Development and Security for the North Bornean samples.For this study, we only recruited unrelated volunteer participants above 18 years old who provided written consent. We recruited 11 individuals including 5 Negrito (5 Jehai), 1 Senoi (MahMeri) and 5 Proto-Malay (4 Seletar and 1 Jakun). We collected peripheral blood (6 ml) from each participant and recorded their self-reported ethnicity and family pedigree Fig. .60 and the DNeasy Blood and Tissue kit (Qiagen) for the North Bornean samples. We performed sequencing with Illumina HiSeq 2000 at approximately 30 × sequencing coverage and paired-end read length of 100 bp. We also included 2 Negrito Mendriq and 2 North Bornean (Dusun) fastq files from our previous study24. We mapped paired-end reads to GRCh38 using the Burrows-Wheeler Aligner61 (bwa mem) version 0.7.12. We removed PCR duplicate reads using the Picard MarkDuplicates tool version 1.93 (http://broadinstitute.github.io/picard/). We also performed post-alignment processing, for example, base quality recalibration or local indel realignment. To identify single nucleotide variants (SNVs) and small indels, we used GATK HaplotypeCaller62 version 3.5.0 on each individual separately.We extracted the genomic DNA using a modified salting-out method63, Andaman Islanders64, and Malay individuals from Singapore Genome Diversity Project (SSM)65. To investigate the historic relationships between indigenous populations of Malaysia with other populations in Southeast Asia, we downloaded ancient SEA data49. We used the UCSC LiftOver tool to convert the genome coordinates of Andamanese and Malay and ancient SEA data from hg19 to GRCh38. We constructed two datasets. The first dataset comprised 15 indigenous Malaysians along with 1035 unrelated individuals from HGDP, SSM, and Andamanese dataset. After quality control for each population including missing rate per SNP < 0.05, minor allele frequency > 0.05, and Hardy–Weinberg exact (HWE) test (P < 10−6), 3,374,375 shared SNVs remained. The second datasets included dataset 1 and 43 ancient SEA samples with 3,347,752 overlapping SNVs.To compare the indigenous Malaysian populations with worldwide populations, we downloaded the Human Genome Diversity Project (HGDP)-CEPH panel data2 > 0.8) to eliminate the effects of excessive LD. After LD pruning, 812,971 and 806,229 SNVs remained from dataset 1 and dataset 2, respectively. In addition, we normalized the sample size by randomly selecting 10 individuals from each population in the HGDP and SSM datasets  and ancestry estimation to assess the genetic structure of different indigenous groups within Malaysia and also the relationship between these groups and other neighboring populations. For this analysis, we filtered out SNVs with linkage disequilibrium  changes and divergence time of populations. We generated the VCF and masking files for 4 individuals per population, where applicable, according to the MSMC2 recommended parameters. For phasing the data, we used Eagle version 2.4.170. We also assumed a mutation rate of 1.25 × 10−8 per base per human generation and a generation time of 29 years71.We conducted a Multiple Sequentially Markovian Coalescent (MSMC2) analysis15. We used blocks of 500 SNVs (-k 500) to account for LD and added 5 migration edges sequentially with 100 replications for each migration edge and Yoruba as root population. We examined gene flow between indigenous Malaysians and surrounding populations using ADMIXTOOLS package72.To further investigate the relationship between the populations and potential migration events, we inferred a maximum likelihood drift tree using TreeMix version 1.1373 and MitoSuite74 to assign the mitochondrial haplogroups. For the Y-chromosome, we called the genotypes with SAMtools/BCFtools version 1.975. We restricted calling to the 10.3 Mb region previously identified to be accessible for short-read sequencing76. We used yhaplo77 to assign the Y-chromosome haplogroups. We used ggplot2 version 3.3.3 package (https://ggplot2.tidyverse.org) in R version 4.0.4 (https://www.R-project.org/) for visualization79.We analyzed the full Y-chromosome and mitochondrial sequences by annotating them with the mutations commonly used for nomenclature. We used HaploGrepThis study was approved by the Ministry of Health, Malaysia, Monash University Human Research Ethics Committee (MUHREC), the Department of Orang Asli Development , the Research and Ethics Committee of the University of Technology MARA [Ref no: 600-RMI (5/1/6)], and the University of Malaysia Sabah Medical Research Ethics Committee [code: JKEtika 4/10(3)].Both verbal and written informed consent was obtained from all individual participants included in the study.Supplementary Information.