Ophiopogon japonicus, mainly planted in Sichuan (CMD) and Zhejiang (ZMD) province in China, has a lengthy cultivation history. During the long period of domestication, the genetic diversity of cultivated O. japonicus has substantially declined, which will affect the population continuity and evolutionary potential of this species. Therefore, it is necessary to clarify the phylogeography of cultivated O. japonicus to establish a theoretical basis for the utilization and conservation of the genetic resources of O. japonicus.O. japonicus individual plants from 23 sampling sites were analyzed based on 4 chloroplast DNA sequences  to identify the effects of domestication on genetic diversity of cultivars and determine their geographic origins. The results showed that cultivated O. japonicus and wild O. japonicus had 4 and 15 haplotypes respectively. The genetic diversity of two cultivars  was much lower than that of the wild populations , and the level of genetic diversity in CMD  was lower than that in ZMD . There was significant difference in genetic differentiation between the cultivated and the wild (FST = 0.82044), especially between the two cultivars (FST = 0.98254). This species showed a pronounced phylogeographical structure . The phylogenetic tree showed that the genetic difference between CMD and ZMD was not enough to distinguish the cultivars between the two producing areas by using O. amblyphyllus Wang et Dai as an outgroup. In addition, both CMD and ZMD have a closer relationship with wild populations in Sichuan than that in Zhejiang. The results of the TCS network and species distribution model suggested that the wild population TQ located in Sichuan province could serve as the ancestor of cultivated O. japonicus, which was supported by RASP analysis.The genetic diversity and population structure of 266 O. japonicus has experienced dramatic loss of genetic diversity under anthropogenic influence. The genetic differentiation between CMD and ZMD is likely to be influenced by founder effect and strong artificial selection for plant traits. It appears that wild populations in Sichuan area are involved in the origin of not only CMD but also ZMD. In addition, we also raise some suggestions for planning scientific strategies for resource conservation of O. japonicus based on its genetic diversity and population structure.These results suggest that cultivated The online version contains supplementary material available at 10.1186/s12870-023-04247-2. Phylogeography, which is a discipline concerned with various relationships between gene genealogies, phylogenetics and geography , 2, is oOphiopogon japonicus (L. f.) Ker-Gawl., an evergreen perennial herb in the Liliaceae family. is a typical domesticated medicinal plant whose cultivation history can trace back to Song Dynasty. The tuberous root of O. japonicus has been used for the treatment of coughs, sore throats, thirstiness, insomnia and constipation in traditional Chinese medicine (TCM) over 2000 years due to its functions in nourishing Yin, promoting the production of body fluid, moistening lungs and clearing away internal heat [O. japonicus in China planted in Zhejiang and Sichuan respectively. Different geographical locations led to the different growing environment of the two cultivars [O. japonicus has decreased a lot which will affect the long-term survival and evolutionary potential of this species, and then affect the selection and breeding of high-quality medicinal herbs [O. japonicus are essential for designing strategies of protecting the genetic resources and breeding new varieties.nal heat . Zhe maiultivars . Despiteultivars , 8. All ultivars –11 and pultivars –14 betweal herbs , 16. HenO. japonicus, we used 4 chloroplast DNA regions  to figure out the population genetics and phylogeography of wild and cultivated O. japonicus. More specific objectives are to (1) evaluate the genetic diversity of cultivated populations under artificial selection and wild populations, and determine which wild populations can be introduced for the improvement of cultivation; (2) clarify the genetic divergence between cultivated populations of O. japonicus from Sichuan province and Zhejiang province; and (3) disclose the geographical origins and ancestral populations of cultivated O. japonicus.To provide practical information necessary for origin exploration and sustainable cultivation of rpl16) and three cpDNA spacers  were chosen to analyze the genetic variation in O. japonicus from different geographical locations after preliminary variation screening. Based on 266 individuals representing 23 populations of O. japonicus, the nucleotide sequence length was 698 bp, 523 bp, 479 and 708 bp for the atpB-rbcL, rpl16, psbA-trnH and rpl20-5’rps12 regions, respectively. The combined cpDNA sequence length after multiple alignments was 2408 bp. In total, 107 polymorphic sites and 19 haplotypes were detected from 10 cultivated populations and 13 wild populations of O. japonicus. The variation rate of the combined cpDNA sequence was 4.44%, and the aligned sequence variations are summarized in Additional file One cpDNA intron rpl6 and thrO. japonicus was estimated by the number of haplotypes (H), haplotype diversity (Hd) and nucleotide diversity (π) in O. japonicus  were lower than those of ZMD  and wild populations . Furthermore, the result of permutcpSSR showed that the genetic diversity of all 13 wild populations  was much higher than that of the two cultivars .Genetic diversity of us Table . 19 haplO. japonicus occurred mainly between the two cultivars , which is different from wild O. japonicus whose variation mainly occurred among populations (70.09%). Comparison of different groups showed huge genetic variations between CMD and ZMD (98.37%), which is consistent with the low gene flow between the two cultivars (Nm =0.04). The results suggest that there had been a high genetic differentiation between CMD and ZMD. What’s more, the percentage of genetic variation between CMD and wild group (40.30%) was much higher than that between ZMD and wild group (8.64%), because the gene flow between ZMD and wild group (Nm = 1.490) can resist genetic differentiation caused by genetic drift among populations between ZMD and wild groups (FST = 0.72934), while the gene flow between CMD and wild group (Nm = 0.830) was insufficient to resist the genetic differentiation caused by migration or genetic drift between CMD and wild group (FST = 0.82566).According to the AMOVA analysis Table , geneticNST was significantly higher than GST , indicating that geographically close haplotypes were distributed in the same or contiguous population. And the genetic differentiation among population increased with the increase of geographical distance, indicating that there was a significant phylogeographic structure in O. japonicus cpDNA haplotypes. Mantel test, in which only wild populations were included, also supported the result that the genetic distances between O. japonicus populations were significantly and positively correlated with their geographical distances . With the increase of geographical distance, the genetic distance of O. japonicus also showed a trend of increase , this species showed a pattern of restricted gene flow with isolated by distance.A permutation test showed that O. amblyphyllus Wang et Dai (H20) as an outgroup. The result showed that the phylogenetic tree of the 19 cpDNA haplotypes had a comb-like structure  and the tip (derived) haplotypes Fig. . H13, thO. japonicus was clearly multimodal and inconsistent with the bell-shaped curve expected for an expanding population at species level , suggesting that O. japonicus did not undergo a recent expansion in the face of favorable climatic and environmental conditions . However, CMD whose SSD-value was 0.00028 (P = 0.058 > 0.05) and ZMD whose SSD-value was 0.00401 (P = 0.160 > 0.05) had a relatively smooth unimodal distribution, suggesting that CMD and ZMD could be assumed as an expansion model.The mismatch distribution for D  and Fu’s Fs  showed no significant difference from zero, indicating O. japonicus had not passed through a recent demographic expansion. Neither did the wild populations undergo expansion recently . However, the value of Tajima’s D and Fu’s Fs  with subsequent dispersal and vicariance events between the ancestral and areas of present distribution . The AUC score, which represents the average of 10 runs, was higher than 0.8 for the four models built, which supports their predictive power [Based on the geographical distribution of ent Fig. . The AUCve power .O. japonicus distributed in most areas of China during the LIG period  compared with the wild populations  after domestication for several hundred years. Of the 19 cpDNA haplotypes in the species, only 4 haplotypes were detected in cultivated populations. The domestication result of decreased genetic diversity is similar to that of Scrophularia ningpoensis [Angelica dahurica [O. japonicus during such a short domestication history are most likely caused by founder effect and the clonal mode of reproduction. Firstly, the founder effect is that when a population is established and developed by a small number of individuals, the genetic information carried by these few individuals does not completely reflect the genetic information of their source population, resulting in a low genetic diversity of the new population. The loss of genetic diversity for medical plants like Allium mongolicum [Leonurus cardiaca [Lycium barbarum [O. japonicus from the haplotypes of wild populations due to the founder effect, meaning that the founders contained only a few samples of the wild populations with certain genetic information. Secondly, although the domestication history of O. japonicus was shorter than other medical plants like Coix lacryma-jobi L. and Carthamus tinctorius L. [O. japonicus was dramatically decreased because wild O. japonicus could expand via fruits while cultivated O. japonicus was influenced by asexual reproduction and artificial selection. In addition, previous studies have demonstrated that genetic bottlenecks caused by domestication in annual and perennial fruit crops, suggesting that perennial crops can preserve a higher percentage of the genetic variation present in their wild progenitors than annual crops [Hd = 0.06900; π = 0.00049) showed a higher genetic diversity than CMD . And the gene flow of ZMD-wild (Nm = 1.490) was higher than that of CMD-wild (Nm = 0.830), implying that the relatively lengthy juvenile phases coupled with ongoing cultivar–wild gene flow due to the change of habitat may contribute to the milder genetic bottlenecks in perennial ZMD than annual CMD, resulting in relatively high genetic diversity in ZMD.For many cultivated crops, artificial selection, founder effect and genetic bottleneck may consequently reduce their genetic diversity –20, as wdahurica . These dngolicum , Leonurucardiaca  and Lycibarbarum  is more orius L. , the genal crops . And juvAngelica dahurica and between its two cultivars  [O. japonicus, while 98.37% of the total genetic variation occurred between CMD and ZMD (Table O. japonicus (FST = 0.72934) was lower than that between CMD and wild O. japonicus (FST = 0.82566). The results of mismatch distribution and neutrality test indicated that both ZMD and CMD had undergone a recent population expansion. Frequent extinctions and recolonizations of local populations can also be an important source of gene flow[Nm = 1.490) than between CMD and wild populations (Nm = 0.830) is the shrinkage of traditional production regions and the creation of new production areas. In a group of completely isolated populations, genetic drift tends to fix different alleles in different local populations. Genetic drift is the unpredictable change in gene frequency due to finite population size. Gene flow between any populations will prevent complete fixation, but gene flow must exceed a certain level (Nm > 1) to prevent substantial genetic differentiation due to genetic drift [Nm = 1.490) is able to withstand the genetic differentiation caused by genetic drift while the gene flow between CMD and wild populations (Nm = 0.830) is unable.In addition to reducing the genetic diversity of medical plants, domestication also affects the structural patterns of genetic variations . For ins= 0.000) . Our resMD Table . FurtherFST = 0.734 was logene flow. Due to FST = 0.734 was loO. japonicus with a small number of prostrate O. japonicus [O. japonicus rapidly fixes the alleles carried by selected individuals, which means that one or two haplotypes are fixed in each cultivar population, leading to large genetic differences between ZMD and CMD. In addition, the herbal textual studies and the description of the local planters exhibited that there was no record about the introduction of one cultivar of O. japonicus into another cultivar’s growing region, which may have prevented gene flow and then leading to the genetic differentiation between CMD and ZMD. Finally, population differentiation is also influenced by geographical distance. Isolation by distance is a model of the population structure in which genetic differences between populations increase with the geographic scale [NST (0.920) than GST (0.753), indicating the presence of the phylogeographical structure of O. japonicus. Combined with the result of mantel test, it can be inferred that the restricted gene flow with isolated by distance contribute to the population structure of O. japonicus [As for the genetic differentiation between CMD and ZMD, on the one hand, we believe that it is the result of artificial selection and the isolation by distance. Our surveys conducted in the cultivated regions indicated that seedlings of CMD are prostrate CMKY-2, whereas the seedlings of ZMD are mainly erect aponicus . This coic scale . In otheaponicus . GeograpO. japonicus was believed to come from the wild species and be mainly distributed in Henan, Jiangsu, Zhejiang and Anhui provinces. ZMD and CMD cultivation can trace back to Song Dynasty and Ming Dynasty respectively [O. japonicus, we explored the ancestral distribution of O. japonicus and its historical changes based on 4 chloroplast DNA data. ML phylogenetic tree in this study showed that ZMD and CMD were grouped as a cluster as compared with the outgroup  found in CMD and 2 haplotypes (H2 and H4) found in ZMD were derived from H6 and H13 respectively. The 2 haplotypes (H6 and H13) both found only in the wild population BZ and TQ located in Sichuan province. All of the aforementioned haplotypes were derived from H13, which lied at the center of the haplotype network and was only found in the wild population TQ located in Sichuan. It may be the ancestor of the two cultivars in this study. Furthermore, as the most frequent and widely distributed haplotype, H1 was found not only in CMD but also in the wild population NC, while H3, which occurred in most ZMD, was also found in the wild population BZ, suggesting that the wild populations in Sichuan contributed more to the origin of O. japonicus distributed in most areas of China during LIG period  proposes that glaciers promoted differentiation and generation of intraspecific diversity by isolating populations in ice-free refugia [The result of historical biogeography and the model of species distribution indicated that iod Fig. -A. But tins Fig. -B, sugge refugia , which wO. japonicus was widely distributed in China during the LIG period. With the passage of time, the cold climate caused O. japonicus to migrate to the east of Hengduan Mountains and south of Qinling Mountains during the LGM period. The present distribution was formed after several vicariance-dispersal events on the basis. More importantly, wild populations similar to the extant TQ located in Sichuan province may be the ancestor of cultivated O. japonicus.In summary, O. japonicus indicated that cultivated O. japonicus had much lower genetic diversity than wild populations  to CMD, which may reduce the plasticity of cultivated O. japonicus to respond to changes in climate, pathogen populations, agricultural practices, or quality requirements. Hence, it is necessary to improve the genetic diversity of cultivated O. japonicus through sexual reproduction by using wild resources.Wild populations have been considered as critical resources for breeding medical plants because their genetic information are used infrequently and generally exhibit no reproductively isolation from cultivated populations . Our stuons Fig. ; Table 1Angelica dahurica [Aspidopterys obcordata var. obcordate [Vitex rotundifolia [Hd = 0.83300, π = 0.00568; Hd = 0.80300, π = 0.01341; Hd = 0.70500, π = 0.00488) than other populations  and populations distributing in glacial refugia with high genetic diversity (TQ and BZ) should be protected for the conservation of genetic resource and improvement of genetic diversity for O. japonicus. Our findings may help better understand the influence of domestication on genetic diversity of medicinal plants and provide genetic insights into conservation and sustainable cultivation of O. japonicus.In this study, we accessed the level of genetic structure of O. japonicus plant samples were obtained from 23 sites of different distribution areas of China for cpDNA analysis . For the wild and cultivated plants we used in this study, we obtained relevant permission to collect. All voucher specimens were morphologically identified by Professor Luping Qin from the College of Pharmaceutical Sciences, Zhejiang Chinese Medical University (ZCMU) and deposited at the herbarium of ZCMU . Our field study and experimental research complied with local legislation, national and international guidelines.A total of 266 individual sis Fig. ; Table 3atpB-rbcL , rpl16 , psbA-trnH  and rpl20-5’rps12 . The reaction mixture (50 µL) contained 2 µL of total DNA, 1 µL of each primer, 21 µL of ddH2O, and 25 µL of 2× Trans Taq High Fidelity (HiFi) PCR SuperMix . Double-stranded DNA was amplified after 4-min incubation at 94℃; followed by 30 cycles at 94 ℃ for 50 s, 54℃ for 50 s, and 72 ℃ for 90 s; with a final extension at 72 ℃ for 7 min. PCR products were identified on 1.0% agarose gels, visualized with ultraviolet light and photographed by using DNA Marker DL2000 (Takara Bio Inc.). The PCR products with a single band were cleaned by a EasyPure PCR Purification Kit  for direct sequencing. Sequencing was conducted from both ends.Polymerase chain reaction (PCR) was performed in an Veriti96 thermocycler (Thermofisher). The primers unsed in this study are Geneious Pro V4.8.3  was usedHd) and nucleotide diversity (π) of O. japonicus were computed by DnaSP v5.10 [hS), total gene diversity (hT) and the level of population differentiation at the species level (GST), as well as the geographical total haplotype diversity (VT), geographical average haplotype diversity (VS) and an estimate of population subdivisions for phylogenetically ordered alleles (NST) obtained by taking into account the similarities between haplotypes. GST and NST were used to assess the geographical structure that affected population differentiation. To evaluate the existence of correlation between the geographic and genetic distances, Mantel test was performed using GenAlEx v.6.502 [The number of haplotypes (H), haplotype diversity . The lack of significant differences in the SSD values indicated that the population assumed an expansion model.D and Fu’s Fs) were carried out on Arlequin 3.5 [D values suggest that a population has recently increased in size, whose results can tell the bottlenecks, selective effects, population expansion, or heterogeneity of mutation rates of O. japonicus.The neutrality tests  was implemented to infer ancestral distribution using RASP 3.2 . The trehttp://www.worldclim.org/download), and community climate system model version 4 (CCSM4) model simulation was selected in Last inter-glacial (LIG), Last Glacial Maximum (LGM) and Mid Holocene (MID) periods. The current distribution information for O. japonicus was obtained from major databases and information systems, including the Global Biodiversity Information Facility  and the Chinese Virtual Herbarium (http://www.cvh.ac.cn). After a pairwise correlation analysis using SPSS, we selected a smaller set of nine relatively uncorrelated variables (r ≤ |0.8|): mean diurnal range (bio2), isothermality (bio3), temperature Seasonality (bio4), mean temperature of the wettest quarter (bio8), mean temperature of warmest quarter (bio10), mean temperature of coldest quarter (bio11), precipitation seasonality (bio15), precipitation of wettest quarter (bio16), and precipitation of driest quarter (bio17). Replicate runs (10) of MaxEnt using the “bootstrap” method were performed to ensure reliable results. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic plot, with 25% of the localities randomly selected to test the model. Graphics for each predicted species distribution model were drawn using S-DIVA.19 bioclimatic variables at a resolution of 2.5 arc minutes were obtained from the WorldClim website (available at Below is the link to the electronic supplementary material.Supplementary Material 1


The yak is endemic to the Qinghai-Tibetan Plateau (QTP) and serves as an important livestock animal for Tibetan pastoral society. Phenotypic traits vary among domestic yaks. However, genomic characteristics, population structure, and the genetic relationship of domestic yaks with wild yaks have not been effectively characterized until now. In this study, six domestic populations were sequenced, and selective sweep regions involved in yak domestication were detected. Furthermore, divergence time for domestic and wild yaks was estimated. This study revealed new insight into selection for domestic and wild yaks.Bos grunniens) was domesticated in the high-altitude QTP. Research about their genetic diversity and population structure is limited. In this study, we resequenced the genome of 494 domestic yaks using Specific-Locus Amplified Fragment Sequencing (SLAF-seq). The survey was conducted on six populations sampled from isolated locations in China in order to analyze their structure and genetic diversity. These six domestic populations were clearly grouped into two independent clusters, with Jinchuan, Changtai, and Jiulong showing a tight genetic relationship with the wild yak. Nerve development pathways were enriched with GO enrichment analysis of 334 domesticated genes. Major genomic regions associated with the differentiation of domestic yaks were detected. These findings provide preliminary information on the yak genome variability, useful to understand the genomic characteristics of different populations in QTP.The yak ( Bos mutus) by the ancient Qiang people at the beginning of the Holocene [The yak is endemic to the QTP and can live in harsh, challenging conditions, such as low oxygen supply, extreme cold, and scanty flora ,2. The yHolocene ,4,5. It Holocene ,7. DomesGenome-wide analysis using resequencing technique gives the opportunity to detect genetic variation between wild and domestic plants and animals ,9,10. AfIn this study, a total of 494 domestic yaks were sequenced, and 15 wild yaks were collected from published databases. Domestic yaks included Changtai, Jiulong, Jinchuan, Fenzui, Quanhei, and Fuluo populations, with Fenzui, Quanhei, and Fuluo belonging to the Maiwa population. Using large-scale resequencing, a genomic variation map was constructed. Population structure analysis revealed a genetic relationship between domestic and wild yaks. Genomic differentiation between and within wild and domestic yaks were analyzed, which uncovered selection in genes associated with yak domestication. The results of this study provided valuable genomic resources to study genomic characteristics of yak breeds/populations in QTP.This study was conducted following animal welfare requirements. The procedures approved for experiments were based on the recommendations of the Regulations for the Administration of Affairs Concerning Experimental Animals of China. The Institutional Animal Care and Use Committee of Southwest Minzu University approved all the animal experiments in this study (No. 16053).Sampling covered four different regions of QTP, including Jiulong , Jinchuan , Changtai , and Hongyuan . Blood samples were collected from 494 yaks belonging to six populations: Jiulong, Jinchuan, Changtai, Quanhei, Fenzui, and Fuluo. In detail, Quanhei (black body), Fenzui , and Fuluo (black at birth and grey as adults) are considered part of the Maiwa conserved population, which is reared at Hongyuan Yak Breeding Farm. Adult yaks of similar age, health status, and size were sampled. After the yak vein was pierced with a disposable blood sampler (China), blood was dropped into a 5 mL anticoagulant test tube and fully shaken. The samples were immediately put on ice and sent to Novogene Bioinformatics Technology Co., Ltd., for sequencing. DNA was extracted from individual blood samples with an TIANamp Blood Genomic DNA Kit . DNA purity and concentrations were measured with a Nanodrop 2000 spectrophotometer  based on OD260/280 values. The integrity of samples with an OD260/280 of 1.6–1.8 and a concentration of 50–1000 ng/µL were separated via 1% agarose gel electrophoresis (AGE) to ensure sample quality. Those samples that exhibited a single distinct AGE band were diluted to 50 ng/µL and stored at −80 °C. The specific-locus amplified fragment sequencing (SLAF) libraries were constructed following the procedure described by previous study, except that here two restriction enzymes, HaeIII , Menlo Park, CA, USA)) and Hpy166II , were used to digest genomic DNA . Pair-enLow quality and adaptors containing reads were filtered using trimmomatic software . The reshttps://evolution.genetics.washington.edu/phylip.html) (accessed on 20 January 2023): (1) Individual genetic distances were calculated using plink software [Phylogenetic tree was constructed using phylip software  (accessed on 20 January 2023) was used to draw an enriched GO heatmap. GO terms with FDR ≤ 0.05 were selected.The longest transcript for each gene was used for functional annotation. The protein sequence was blasted against NR, COG, KOG, SWIFF databases using diamond software with cutoff of 1 × 10−5 . Next, G1 × 10−5 . R packa1 × 10−5 . R packaxi, i = 1 or 2, which means the reference and alternative alleles, respectively. Nucleotide diversity was averaged in a 10 Kb window.To calculate nucleotide diversity in a given population, the following formula was used:To identify the regions under selective sweep between wild and domestic yaks, both Fst and π ratio analysis were applied, as this strategy can effectively identify sites under positive selection. Firstly, Fst values between wild and domestic yaks were calculated in 100 Kb windows with 10 Kb step. Secondly, π values were calculated for each wild and domestic population in 100 Kb windows with 10 Kb step. π ratio was calculated by comparing wild to domestic yaks in each corresponding genomic window. The top 5% genomic regions under selective sweep were selected. Third, the overlapping genomic regions resulting from Fst and π ratio analysis were considered as selective sweep regions for yak domestication.http://abacus.gene.ucl.ac.uk/software/paml.html) (accessed on 20 January 2023) was used to estimate divergence times on the basis of the evolutionary tree and to further describe the differentiation time among different yak populations.Gene flow patterns between different populations were investigated using a maximum likelihood approach implemented in Treemix software with parameter of migration ranged from 0 to 4 . The MCMA total of 494 yaks representing six diverse populations in the QTP of China were collected for resequencing with SLAF-seq, including Jinchuan, Changtai, Jiuilong, and Maiwa  . These dDEFB gene was associated with specific SNP identified in Quanhei yak and responsible for basic coat colors in cattle [Population-specific SNPs reflected the genetic diversity of the different populations. A total of 4402 private SNPs were identified for wild yak, followed by Maiwa, Fuluo, Changtai, and Jinchua yaks, which suggested a higher level of genetic diversity in wild yaks . A totaln cattle . TherefoTo study the genetic relationship between wild and domestic yaks, a large-scale phylogenetic tree was generated using the neighbor-joining method with wild yak as the root B. The phPopulation structure analysis was performed to study the genetic composition of wild and domestic yaks A. AncestThe results of the population structure were resembled with the PCA output, with PC1 and PC2 explaining 20.76% and 17.72% of the genetic variation, respectively B. JinchuWe also calculated nucleotide diversity for wild and domestic yaks. As expected, the wild yak exhibited higher nucleotide diversity than the domestic yak C, suggesWild yaks adapted to their natural environment while domestic yaks were artificially selected for human preferences, such as high milk production. To detect the selective footprints underlying wild and domestic yaks’ differentiation, we calculated pairwise Fst and π ratio (wild vs. domestic pairs) . MoreoveWe detected 334 differentiated genes, which seem to be the target of artificial selections between wild and domestic yaks. Also, genomic differentiation regions between different domestic yaks included 614~1258 genes, covering a 36.2~76.2 Mb genome sequence . As a reAs for the differentiation between different Maiwa populations, one significant genomic signal was detected on chromosome 20, spanning 33.62–34.275 Mb, by comparing Fenzui with Quanhei and Fuluo yaks . This geGene flow and divergence time analysis were analyzed with Treemix and SMC++ software, respectively. The migration parameter was tested from 0 to 4. At migration parameter 3, standard error (SE) decreased to near 0 A,B. TherGenetic diversity is an important indicator for assessing population diversity. High genetic diversity helps species to adapt to changeable living environments. Previous studies demonstrated that, despite a long-term genetic bottleneck experienced by the wild yak, its genetic diversity was higher than that of the domestic yak . HoweverIn previous studies, wild and domestic yaks were clearly divided into distinct lineages ,19. IndeWe observed a mixed genetic structure among the three Maiwa populations, indicating extensive genetic exchange. Fst analysis revealed a low level of genetic differentiation between Maiwa populations. Moreover, a major genomic region associated with the differentiation of Fenzui with Quanhei and Luofei was identified. This region, located on chromosome 20, might be involved in the specific phenotypic differentiation between Fenzui and other Maiwa populations.Fst value was an indicator of genetic differentiation among populations. Fst showed weak (0–0.05), moderate (0.05–0.15), and significant (0.15–0.25) population differentiation. The paired populations were considered different when Fst exceeds 0.25 . In prevGRIN2B encodes Glutamate receptor ionotropic that in humans is strongly associated with neuronal development [ANK2 encodes the Ankyrin repeat, which is involved in the transient increase in excitatory synapses during postnatal development [ANKS1B encodes the CASK-interacting adaptor protein, functional in autism and neuropsychiatric diseases. Affected individuals present with a spectrum of neurodevelopmental phenotypes, including autism, attention-deficit hyperactivity disorder, and speech and motor deficits [NCAM2 encodes the neural cell adhesion molecule and is the second member of the NCAM family with a high expression in the nervous system, which controls important neuron-specific processes such as neuronal differentiation, synaptogenesis, and memory formation [OR13J1, OR5M10, and OR2W3 functionals in olfactory development were under positive selection.Genome-wide analysis revealed regions under positive selection between wild and domestic yaks by integrating Fst and π ratio analysis. Especially, biological pathways involved in brain and neuronal development were enriched. Among these genes, elopment . ANK2 enelopment . ANKS1B deficits . NCAM2 eormation . OlfactoKIT gene encodes the mast/stem cell growth factor receptor, which is a cytokine receptor expressed on the surface of haematopoietic stem cells as well as other cell types [KIT genes might be responsible for coat color differentiation within the Maiwa population.A total of six populations of domestic yak were sequenced. Among them, Fuluo, Fenuoz, and Quanhei are three conservation populations belonging to Maiwa yak. Fuluo yak has a grey coat color, while Fenzui and Quanhei yaks have a black coat color. Candidate genes responsible for their differentiation were identified. Among them, ll types . This geDivergence time between wild and domestic yaks was about 8000 yr ago, which was consistent with previously published research . DivergeIn summary, a whole-genome resequencing strategy was used in this paper to explore genetic diversity, population structure, and genetic differentiation among six populations of domestic yak, which revealed major genomic loci associated with the differentiation of the domestic yak. Furthermore, selective sweep analysis revealed various biological pathways involved in yak domestication. Divergence time analysis estimated the parting time among and within domestic and wild yaks. This study provided new insight into differentiation among domestic yaks.


Anas platyrhynchos) samples across continental and island populations to determine the result of over a century of supplementation practices. We establish that a single domestic game-farm mallard breed is the source for contemporary release programs in Eurasia and North America, as well as for established feral populations in New Zealand and Hawaii. In particular, we identify central Europe and eastern North America as epicenters of ongoing anthropogenic hybridization, and conclude that the release of game-farm mallards continues to affect the genetic integrity of wild mallards. Conversely, self-sustaining feral populations in New Zealand and Hawaii not only show strong differentiation from their original stock, but also signatures of local adaptation occurring in less than a half-century since game-farm mallard releases have ceased. We conclude that ‘wild’ is not singular, and that even feral populations are capable of responding to natural processes. Although considered paradoxical to biological conservation, understanding the capacity for wildness among feral and feral admixed populations in human landscapes is critical as such interactions increase in the Anthropocene.The translocation of individuals around the world is leading to rising incidences of anthropogenic hybridization, particularly between domestic and wild congeners. We apply a landscape genomics approach for thousands of mallard ( A genomic investigation focusing on introgressive hybridization due to anthropogenic causes in mallards reveals a single domestic game-farm breed as the source for contemporary release programs in Eurasia and North America as well as for established feral populations in New Zealand and Hawaii. Alongside changing habitats, domestic releases have become common world-wide, resulting in augmented genetic integrity of various wild populations8. In fact, in cases where releases have been common and intensive, gene flow from domestic individuals now poses a major threat to these various ecologically and economically important wild species . However, genomic data has also revised and refined our understanding where contact between domestic and wild congeners do not always result in wide-spread introgresssive hybridization24. More generally, predicting the outcomes of domestic introductions remains challenging because introgression of maladaptive traits into wild populations is additive, and negative consequences are not always immediately apparent25. With rapidly changing climatic conditions, it is becoming increasing evident that there is a growing need to understand the genetic underpinning of such events as to predict future challenges in conservation28. In fact, it is possible that while feral populations are generally unsuited for survival in wild habitats, they may have a greater adaptive potential in human-dominated areas29. Here, we employ a landscape genomics approach on global wild and feral mallard (Anas platyrhynchos) populations to understand how lineages with varying demographic histories and selection regimes  respond to natural versus human influences.An outstanding but evolving question within evolutionary and conservation biology is what are the genetic consequences of hybridization that result from human-mediated disturbances?31. Since then, domestic mallard supplementation has been practiced around the world, with the most intense release programs beginning more recently in the early 20th century32. While naturally distributed across the Holarctic, the intentional or accidental release of mallards has expanded their range to nearly world-wide outside the Poles33. As a result of these efforts, widespread introgression from established feral populations now poses a genetic threat to populations of wild mallards and other closely related waterfowl found throughout Eurasia, North America, and the South Pacific37. Among regions, annual supplementation is currently greatest in Europe and eastern North America where nearly five-million39 and at least two-hundred thousand  game-farm mallards, respectively, are annually being released. Note that game-farm mallards are captive bred to be released on shooting preserves for hunting and dog training purposes43  confirmed that the genetic constitution of today’s eastern North American mallard has been fundamentally altered through extensive gene flow with game-farm mallards43.Starting in central China, humans have been domesticating mallards since shortly after 500 BCgradient forest to reconstruct the adaptive landscape of wild and feral populations by testing for genotype-environment associations including both natural and anthropogenic indices47, and thus, reconstructing their genetic niche space47. Additionally, we determine whether the association with environmental factors is driven by many or few loci, as well as whether the same loci are consistently under selective pressures from each wild/feral population. In particular, with wild mallards being innately migratory, we predict that associations with environmental cues  will be consistent across loci for wild populations47. Furthermore, we posit that genetic diversity within feral mallard populations that have been shown to take advantage of more human-dominated habitats will be dictated by loci associated with anthropogenic indices .The [un]intentional release of domestic mallards across regions of varying habitat and ecology provides powerful experiments that can help us to understand how introduced population(s) respond under differing selective pressures, which is an underlying mechanism of local adaptation. We examine the genetic and adaptive consequences from large-scale supplementation practices of domestic mallards by coupling partial genomic sequencing of thousands of samples representing wild and feral populations throughout Eurasia, North America, and New Zealand. After establishing ancestry across wild and feral populations, we couple demographic and phylogenetic analyses to identify the sources of stock currently being released  and of two self-sustaining feral populations . Next, we use the program A total of 2951 ddRAD-seq loci ) were recovered across chromosomes, including 2751 autosomal , 193 Z-sex chromosome linked , and 7 W-sex chromosome linked (937 bp) that met our sequencing coverage and missing data criteria for 1916 samples . First, all domestic, feral Khaki Campbell’s, and all but one Eurasian wild mallard were recovered within the OW A haplogroup. The one wild mallard from China possessed a NW B haplotype, which may represent a North American migrant; however, being unique, it is also possible that this haplotype represents introgression with endemic spot-billed ducks (A. poecilorhyncha) that also possess B haplogroup variants51. Additionally, a single wild mallard from the Netherlands was recovered within the Khaki Campbell’s haplotype. Although New Zealand’s feral mallards primarily carried OW A haplotypes (91% of samples), eight samples carried six NW B haplotypes not shared with any other wild mallard  A and New World (NW) B haplogroups in our mtDNA haplotype network Fig. ;48–50. FNhap = 151) and NW (Nhap = 297) haplogroups showed high uniqueness and variability, whereas game-farm and feral populations showed the complete opposite trend with only 32 haplotypes found across 249 samples  of all OW A haplotypes recovered in North America were within the same 15 OW A haplotypes carried among sampled game-farm mallards. Similarly, while 13 OW A mtDNA haplotypes were found among New Zealand’s feral mallards, 65% of these samples carried the same major OW A haplotype  independent bi-allelic SNPs. Plotting the first two components of the PCA provided clear groupings of wild and domestic ancestry mallards, with individuals in intermediate space between them as hybrids Fig. . Additiof 7 Fig. . First, f 7 Fig. . FinallyADMIXTURE required us to evaluate increasing values of K populations up to seven, we tested whether ancestry assignments changed among admixed individuals by excluding feral populations. We analyzed known game-farm and wild mallards of Eurasia and mainland North America using a 36,637 independent bi-allelic SNP dataset and following the same protocols in obtaining assignment probabilities from ADMIXTURE as outlined in the methods for K population value of 2–4  or feral . We acknowledge that while some samples with assignment probability of 90–95% wild potentially includes late generational backcrosses, we were more concerned as to not create noise in our hybrid sample set by including individuals likely already biologically wild; the same ancestry cutoffs were used in previous studies55.Next, because the recovery of all unique genetic clusters in 2–4 Fig. . We agai2–4 Fig. ; and makBased on our set ancestry cutoff, only 16 and three North American and Eurasian wild caught samples, respectively, were identified as feral game-farm mallards Fig. . The wesTreeMix: (1) three wild mallard genetic clusters including mainland Eurasia, Greenland, and North America, (2) a single domestic game-farm mallard cluster from Eurasia, and two from North America, and (3) feral populations of Khaki Campbell’s, as well as those on Hawaii and New Zealand. An optimum TreeMix tree and >99% of the variance explained was recovered when including up-to two migration edges  across pair-wise and locus-by-locus comparisons to set an arbitrary two-standard deviation ΦST threshold of 0.31 for outlier detection . Given that we were interested in general differences across groups, we obtained an average and standard deviation , but slightly greater values from Eurasian groups  across pair-wise comparisons , except for feral Khaki Campbell’s that had nearly a third of the nucleotide diversity  = 11.19, p < 0.001) and Eurasian  = 2.94, p < 0.01), as well as feral populations of mallards in New Zealand  =  −2.6528, p < 0.01) and Hawaii  = 35.44, p < 0.0001) all performed better than their respective randomized datasets  and the vegetation index during winter, as well as the human-impact indices Table ; graphinAmong the two feral populations, GF outputs for New Zealand were based on the average temperature ranges tied to wet-dry cycles, as well as both human indices Table . Project64), but also identify fine nuclear substructuring , and that game-farm mallards from Eurasia were the ancestral eve of stocks around the world. Whereas wild mallards show a high degree of mtDNA haplotype diversity, samples collected from feral populations and regions where releases are common contain a much smaller set of unique haplotypes , but are potentially better suited to the increasingly agricultural and urban landscapes of this region . Importantly, our results contrast the general consensus among wildlife managers that game-farm mallards rarely survived long enough to leave the area they are released in, because it is evident from our findings that they survived at great enough rates to have their genetic variation readily found everywhere they were and are currently being released .Among attempts at domestication, mallards have been one of the most successfulons Fig. . Interesons Fig. . In New ape Fig. , making Despite opportunities to interact, causes for why domestic × wild mallard hybrids are the result of game-farm mallards and not alternative domestic breeds remains unknown. We posit that it is possible that phenotypic similarities between wild and game-farm mallards may not result in negative assortative mating that may exist between wild mallards and other domestic breeds like Khaki Campbell’s Fig. . Moreove80, we demonstrate how severe loss and the influx of novel genetic diversity via genetic drift  and gene flow, respectively, can cause deviations in allelic histories from their true ancestral states, resulting in distorted demographic histories81. First, we find extreme demographic changes as compared to the wild ancestral state to be among highly bottlenecked groups . Conversely, the genetic adaptive niche space of wild mallards in eastern Eurasia and western North America, as well as those from France, Spain, and Portugal are largely responding to temperature and precipitation regimes of their regions  and sizes. Regardless, the rising prevalence of feral and non-native populations is becoming a major conservation concern, including being the primary causes of biotic homogenization103, especially as more endemic species become imperiled in the Anthropocene29. Therefore, interaction of wild and domestic animals, as well as the potential establishment of feral or feral admixed populations, require continued monitoring due to the dynamic nature of environmental selective pressures, urbanization, and climate change.Going forward, conservation science will need new approaches and practices when contending with challenges presented by climate change and our growing human footprintN = 13; also see Fig. Tissue, blood, DNA, or comparable published sequences were obtained for a total of 1916 samples of wild and feral mallard populations representing their ranges in mainland North America, Eurasia, Hawaii, and New Zealand . DNA integrity was based on the presence of a high-molecular weight band as determined with gel electrophoresis using a 1% agarose gel106 and following protocols outlined in Lavretsky et al.107 . All new sequences are deposited in GenBank  across samples108. Importantly, being of Eurasian descent, all domestically-derived mallards carry OW A haplotypes, and thus, are a distinguishing marker when assessing whether game-farm mallard introgression likely occurred within a wild mallard lineage in North America109. Thus, samples were visualized and sorted as possessing OW A versus NW B haplogroups using a median-joining network carried out with the program POPART110. Note that although no comparable sequences were available for the same mallards from Hawaii, these were previously shown to possess OW A haplotypes111. Finally, we plotted proportion of samples with OW A versus NW B haplotypes by European country or North American waterfowl flyway region that generally defines migratory pathways in ArcMap v. 10.7.1 (Esri).Mallards are characterized by the old world (OW) A and new world (NW) B mtDNA haplogroups, which distinguish individuals of Eurasian or North American descent, respectivelySbfI and EcoRI restriction enzymes, and as outlined in DaCosta and Sorenson112,. Size selection followed gel electrophoresis protocols as outlined in DaCosta and Sorenson112 for samples collected before 2016, with samples collected thereafter size selected following optimized double-sided magnetic bead selection as described in Hernandez et al.113. Because samples were collected over the last decade, multiplexed libraries have been sent to various genomic facilities for 150 base-pair, single-end chemistry sequencing across Illumina platforms including, HiSeq 2000, HiSeq 2500, HiSeq 4000, HiSeq X, and Novoseq. Additional details on DNA extraction and ddRAD-seq library preparation protocols can be found in 16, PRJNA911832114, PRJNA800412115, PRJNA59191243, PRJNA84779247, PRJNA74536653, PRJNA57758120, PRJNA718623116; Sample specific accession numbers can be found in Supplementary Data ddRADparser.py script of the BU ddRAD-seq pipeline112 based on perfect barcode/index matches.We followed ddRAD-seq library protocols using https://github.com/jonmohl/PopGen; see43) to automate sequence filtering, alignment, and genotyping using a combination of Trimmomatic117, Burrows Wheeler Aligner v. 07.15 bwa;118, and Samtools v. 1.7117 , and quality per base PHRED scores of ≥30 using VCFtools v. 0.1.15120.Across 1916 samples, we used custom in-house Python scripts , we expect to find near-zero levels of sequencing depth across W-sex chromosome linked loci but near equal depth for Z-sex chromosome linked loci when compared to autosomal loci. For the heterogametic sex , we expect to recover about half the sequencing depth at both W- and Z-sex chromosome linked loci as compared to autosomal loci.Finally, sex was assigned to each sample based on differences in sequencing depth across autosomal and sex chromosome-linked lociVCFtools v. 0.1.15120 to first extract bi-allelic SNPs, and then PLINK v. 1.9122 to filter for singletons, any SNP missing ≥5% of data across samples or in linkage disequilibrium (LD) , and without using a priori assignment of individuals to populations or species. The final dataset was obtained by using PLINK v. 1.9122. Next, assignment probabilities  were estimated with the program ADMIXTURE v. 1.3125. In addition to obtaining average Q values across 100 iterations, standard errors for each analysis were based on 100 bootstrap replicates for each evaluated K population model. We evaluated whether the averaged Q score and respective standard error overlapped ≥98% population assignment that we considered to represent a genetically pure parental, whereas those individuals assigned to multiple genetic clusters determined to be as hybrids16. Detailed methods can be found in ArcMap v. 10.7.1 (Esri).Population structure was first visualized with a Principal Components Analysis (PCA) as implemented in ST), nucleotide diversity, and reconstructing phylogenetic relationships of major wild and domestic mallard lineages. To do so, we used recovered genetic clusters from the above population genetics analyses to categorize samples, demarcating those samples that represent parental versus contemporary hybrids. Once done, we estimated pair-wise population relative differentiation (ΦST) and per population nucleotide diversity across ddRAD-seq loci using the PopGenome package in the program R126. In addition to composite pair-wise population estimates using concatenated autosomal and Z-chromosome datasets, per-locus estimates of relative differentiation were also estimated across ddRAD-seq loci. A two-standard deviation threshold from the average of ΦST estimates obtained across pair-wise and locus-by-locus comparisons was used for outlier detection. Although we used a generally arbitrary cutoff, doing so still allowed us to investigate our primary interest of whether the same or different loci showed elevated estimates of relative differentiation across pair-wise comparisons128.Next, to reduce the effects of contemporary gene flow events on inferences, we excluded any hybrid identified in population structure analyses  when estimating relative differentiation  species tree, along with the direction and weight (w) of gene flow among taxa that best explains analyzed allele frequencies among groups. The optimum number of migration edges was determined by sequentially adding migration events up to 36 (-m 0–36), and then evaluating the proportion of the variance explained by each migration model. Standard errors (-se) were calculated to assess significance among recovered migration edges. In order to limit overconfidence in the tree model, migration edges were added until >99% of the variance in the tree model was explained. Finally, likelihood ratios were calculated using likelihood estimates to assess significance between possible tree models.Finally, the program 113, which uses ∂a∂i to model changes in effective population size through time based on multi-individual partial genome data. Each species’ SFS was folded and masked before being projected down to account for missing data between groups131. We then estimated confidence intervals (CI) using parameter uncertainty metrics included in ∂a∂i132. Briefly, ∂a∂i calculates uncertainty values using a Fisher Information Matrix (FIM) that provides a calculation of variance by measuring how much information can be derived from the data with respect to an unknown parameter. We maximized the number of parameters for which ∂a∂i is able to return a true estimate of uncertainty by calculating uncertainty across a range of step sizes . ∂a∂i parameters were then transformed into biologically informative values  global environmental data from several public databases, with a focus on 27 annual and seasonal environmental variables shown to have impacts on bird physiology and ecology (Table 134). Specifically, we included 19 climate variables from the worldclim version 1.4 database ; Landsat Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Net Primary Productivity (NPP) data from the USGS AppEEARS database (https://lpdaacsvc.cr.usgs.gov/appeears); and elevation data from the Global Land Cover Facility (http://www.landcover.org/).To test the importance of human disturbance on genetic diversity within wild versus feral populations, we additionally downloaded data from the Human Impact Wildlife Conservation Society135 and the Human Footprint Wildlife Conservation Society136 indices.We obtained high resolution i.e., ~1 km km2 globADMIXTURE; additionally, only winter collected samples were used for Eurasia and North America . Following the approach of Bay et al., we used a genotype-environment association analysis as implemented in the R package gradientForest (GF)46. GF analysis was originally created to detect effects of environmental predictor variables on species turnover across a landscape46, but has since been adapted for identifying and modeling changes in allele frequency138. In short, we visualized genetic-environmental associations for each of the populations138, as well as used the combined GradientForest function to determine differences in genetic niche space among the groups . This study included a total of 1916 samples, but various subsets of this total were selected to answer more specific questions (also see Further information on research design is available in the Supplementary Information-NewDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Apis mellifera) in the Eastern African Mountains harbor high frequencies of two chromosomal inversions that likely govern adaptation to this high-elevation habitat. In the Americas, honey bees are hybrids of European and African ancestries and adaptation to latitudinal variation in climate correlates with the proportion of these ancestries across the genome. It is unknown which, if either, of these forms of genetic variation governs adaptation in honey bees living at high elevations in the Americas. Here, we performed whole-genome sequencing of 29 honey bees from both high- and low-elevation populations in Colombia. Analysis of genetic ancestry indicated that both populations were predominantly of African ancestry, but the East African inversions were not detected. However, individuals in the higher elevation population had significantly higher proportions of European ancestry, likely reflecting local adaptation. Several genomic regions exhibited particularly high differentiation between highland and lowland bees, containing candidate loci for local adaptation. Genes that were highly differentiated between highland and lowland populations were enriched for functions related to reproduction and sperm competition. Furthermore, variation in levels of European ancestry across the genome was correlated between populations of honey bees in the highland population and populations at higher latitudes in South America. The results are consistent with the hypothesis that adaptation to both latitude and elevation in these hybrid honey bees are mediated by variation in ancestry at many loci across the genome.A range of different genetic architectures underpin local adaptation in nature. Honey bees ( Populations of honey bees in the Americas are hybrids of African and European ancestry in which local adaptation to climate appears to be governed by the relative proportion of these ancestries in the genome. Here, we present evidence that adaptation to high elevations in such hybrids in South America has a similar genetic basis. This indicates a polygenic basis for adaptation to both latitudinal and elevation clines.Understanding the genetic basis of local adaptation is a key goal in evolutionary genomics, which is important for the conservation of wild populations in the face of climate change . Many diEctodysplasin locus in three-spined sticklebacks appears to govern adaptation to freshwater habitats in geographically distant locations . This led to a massive biological invasion, whereby the existing honey bees were replaced by those with mainly African ancestry, commonly referred to as “Africanized” bees  and 14 from Pamplona   online. A), the first and second components  clearly separate the samples into different clusters representing the A, C, and M groups  and a phylogenetic tree using a thinned set of 372,205 SNPs . In the PCA plot A, the fiM groups . The ColWe assessed the highland and lowland Colombian samples for evidence of population substructure and the presence of related samples. A PCA plot of these samples does not indicate the presence of substructure or clusters of samples with elevated relatedness  online. C. At K = 3, the A, C, and M groups separate into different clusters while the Colombian samples appear as a mixture of the A and M ancestries, with only around 0.1% contribution from the C groups. The mean A ancestry proportion of the Colombian lowland samples is 84.7%, which is identical to samples collected across Brazil . This change in ancestry with elevation resembles the clinal ancestry changes with latitude observed in both North and South America, where the type A ancestry decreases further away from the equator (Genome-wide ancestry estimation was performed using the software ADMIXTURE . The assfigure 2s Brazil  and typi equator .STF comparisons between populations using 10-kbp windows and by estimating variation in ancestry proportions across the genome in the Colombian honey bees. We first compared the Colombian highland and lowland populations (A). We did not observe any regions of elevated STF in the vicinity of the two chromosomal inversions on chromosomes 7 and 9 that were identified in the Kenyan highland populations , indicating a similar level of average divergence.We used coalescent simulations to generate an expected background pattern of allelic divergence under neutral genetic drift. We tested for signatures of excess divergence between Colombian highland and lowland bees compared to neutrality, which could indicate adaptive divergence driven by natural selection between environments. To this end, we pruned the thinned SNP dataset  into the subset of 248,598 SNPs that were polymorphic in the Colombian bees , and generated a per-SNP FST as outliers. Above this threshold (FST > 0.255), we observe 3,164 SNPs in our empirical dataset but only 2,442 SNPs in the simulated dataset, a 30% enrichment of highly divergent SNPs compared to what would be expected from neutral evolution alone. Hence there is evidence that selection has resulted in high FST values of a subset of SNPs. These highly divergent SNPs are apportioned into 14 distinct peaks (A). The peak with the highest FST window spans 30 genes on chromosome 14 (labeled 14a). Another prominent peak (labeled 11b) overlaps a region of significantly reduced African ancestry in South American bees identified in two previous studies (We considered windows greater than the 0.998 quantile of ct peaks A. The pe studies .B). There is a highly significant correlation between FST and A ancestry difference between the lowland and highland samples . This indicates that the most divergent loci are associated with the largest drops in A ancestry in the highland population. Among FST outliers, the mean drop in A ancestry in the highland population is 0.4843 compared to a genome-wide average of 0.1538 and all of these outliers show a greater decrease in A ancestry than average.We investigated differences in the proportion of A ancestry across the genome in the highland and lowland samples B. There STF-scans comparing the two Colombian populations separately to the Kenyan population in order to identify regions that differ from the ancestral African progenitors, with the aim of uncovering the origin of differences between highland and lowland populations. The STF scan between the Colombian highland population and the Kenyan population (A) revealed several regions of the genome in which STF exceeds the 0.998 quantiles. The majority of these regions correspond to FST outliers in the previous highland-lowland comparison, indicating that they are candidates for involvement in adaptation to highlands. The two most prominent of these regions are peak 11b (14.8–15 Mbp on chromosome 11) and peak 14a (6.5–6.56 Mbp on chromosome 14) which were also identified in the highland-lowland comparison. A third prominent region, peak 11a (14.43–14.53 Mbp on chromosome 11), is most pronounced in the STF comparison between the lowland population and the Kenyan population and is not present in the highland-lowland comparison . Detailed plots of peaks 11a, 11b, and 14a in all STF comparisons are shown in STF values of all peaks and the genes within them are summarized in We next performed pulation A revealefig. 4B;  online. mTOR gene , whose product serine/threonine protein kinase is involved in multiple processes in the cell and affects the caste differentiation of female bees  and were tested for GO enrichment compared to all genes using Drosophila homologs. Among these candidates, we detected strong enrichment  for genes involved in fatty acid metabolism, meiosis, and sexual reproduction . The three candidates represent unlinked loci with high allelic divergence located on three different chromosomes , including peak 14a. Two of them have roles in sperm function such as spermatogenesis  or sperm competition .We computed gene-wide 75 genes  online aoduction  online, r = 0.011) but highly significant (P < 10−11).In the study by P < 0.001 significance cutoff; see Materials and Methods) whereas none of the four in the second class show significantly higher correlations with the highland population. This difference is statistically significant . Hence the highland population tends to show stronger genome-wide correlations in ancestry with populations at higher latitudes in Argentina, which experience relatively more similar climates despite large geographical separation. This observation is consistent with findings presented by We next wished to determine whether the proportion of A ancestries across the genome is more strongly correlated in populations inhabiting similar climates, which could suggest similar selective pressures. We measured the correlation of A ancestry probabilities across the genome between the two Colombian populations and each of the Argentinian populations separately . Among tWe compared genome variation in populations of Africanized honey bees from a highland and lowland location in Colombia in order to uncover the genetic basis of adaptation to high-elevation habitats in these populations. The main findings are 1) a significantly reduced level of African (A group) ancestry in the highland compared to the lowland population; 2) identification of genomic regions with elevated divergence between the two populations, which contain candidate genes for local adaptation to high elevations; 3) correlations in ancestry proportions across the genome between populations in similar habitats, indicating that similar sets of loci with small effects may influence local adaptation across latitudinal and elevational gradients.Previous studies indicate that honey bees have a consistently high (>80%) A group ancestry throughout low-elevation localities in the tropics . This trVarroa mites, which pose significant risks to managed colonies with European ancestries in many other parts of the world , involved in caste differentiation of female bees (MLF (LOC409877). The latter is expressed in the crystal cells of the hemolymph in Drosophila melanogaster (Samui (LOC727486), which has been shown to be important in cold defense in silkworm eggs (Eaf (LOC411433), which is involved in transcriptional regulation of Hsp70 (heat-shock protein 70) and developmental genes through the activity of RNA polymerase II in human cells  and Pamplona, Norte de Santander, Colombia (2420 m above sea level) in 2016–2017 . Each saThere is very limited movement of managed honey bee colonies in Colombia, and feral colonies are numerous. Beekeepers regularly obtain new colonies from the local landscape, and mating between feral and managed colonies occurs freely. We therefore expect the samples we collected to accurately reflect the genetic ancestry of bees in each locality. DNA was extracted from the thorax of each sample using the Qiagen blood and tissue kit by following the standard protocol. Whole genomic DNA was used to prepare sequencing libraries using the Illumina TruSeq PCR-free kit. All samples were sequenced on an Illumina HiSeqX instrument to produce 2 × 150 bp paired-end reads aiming to obtain mean read coverage >8×.A. m. scutellata for the A reference population  for 19 Kenyan worker bees of pulation , 9 East pulation , and 85 pulation .https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-, last accessed 2021-12-05). Filtering of the variants was done using GATK VariantFiltration with the recommended hard filters  with the limits QD < 2; FS > 60; MQ < 40; MQRankSum < −12.5; ReadPosRankSum < −8; SOR > 3 . Additional filters were applied with VCFtools version 0.1.16 . As the data from this individual was regarded as unreliable, it was excluded from the analyses.The 85 Iberian drones, which are haploid, were treated as diploid samples during variant calling. After the filtering steps mentioned above, 7.1 million SNPs remained and out of those, 178,000 SNPs (2.5%) were heterozygous in one or more of the drones. At sites where only one drone was heterozygous, the genotype of this individual was marked as missing. The 38,500 sites where more than one drone was heterozygous were removed from the entire dataset . An additional filter for minor allele count greater than or equal to three was also applied with VCFtools, leaving a final set of 5.4 million SNPs. One of the Iberian drones had unexpectedly high heterozygosity after diploid variant calling, with an inbreeding coefficient of We estimated the location of pericentromeric regions, characterized by low GC (guanine + cytosine) content, in the Amel_HAv3.1 genome by identifying regions with low GC content over extended chromosomal segments. GC content was calculated in 10 kbp windows based on the reference genome, except in windows where no bases were called. To identify low GC regions, a cumulative window-based score was used. Each 10 kbp window was given a score +1 if its GC content was lower than the genome-wide average, −1 if it was higher than the genome-wide average, and 0 if it was equal to the genome-wide average. The cumulative score was calculated from left to right on each chromosome, starting at zero and adding the value from each window. The 0.5 Mb region with the highest increase in the cumulative score on each chromosome was then selected as the starting point for the putative centromere. This region was then extended in both directions, by ten windows at a time, if the score increased with at least five within those ten windows. Finally, the region was extended by one window at a time if this window increased the score by 1. Extended low GC regions identified in this way showed high correspondence with those regions identified in a previous study  based onThe nucleotide diversity (π) was calculated with VCFtools, separately for each of the reference populations and the Colombian populations. The calculations were done in 50 kbp windows across the genome before computing the average of those windows. The filtering of the VCF  files described above includes a filter for minor allele count, which in this case could cause biased estimates, as rare SNPs are excluded. Therefore, a set of 7 million SNPs, including all allele counts but otherwise filtered as described above, was used instead. Pairs of haploid Iberian drone samples were randomly combined into pseudodiploid samples to conduct this analysis. We assessed the samples for evidence of relatedness using the –relatedness2 option in VCFtools .https://github.com/BGI-shenzhen/VCF2Dis). The tree was constructed in R version 4.1.2 (R core team 2021) with the package ape version 5.5  was estimated with the clustering software ADMIXTURE  on the sSTF  was greater than or equal to the 0.99 quantile of all STF-values. Genes for which at least 30% of the exons overlap the peak by at least 50% of the exon length were identified as potential targets of selection. We estimated recombination rate of the most prominent peaks by comparison to an linkage-disequilibirum-based recombination map . For this data, we estimated the population mutation rate of Watterson's theta  /2, and used T/2 in the simulation (0.0150). Using these parameters, we simulated a 906 bp long locus with the scaled population mutation rate of 4.23 (906 × 0.00467). We ran this simulation 248,598 times, exporting one biallelic SNP and sampling 28 highland and 30 lowland copies of the SNP locus per iteration. The ms command was:We performed coalescent simulations and compared patterns of divergence between simulated and empirical data using ms , taking g sites)  to 0.47%g sites) , and we stimator  to 0.058ms 58 248598 -t 4.23 -I 2 28 30 -ej 0.0150 2 1 -s 1 > highland_vs_lowland.simulated.txtFST values  (FST per gene. We tested for enriched gene ontology terms among genes with FST surpassing the 0.998 quantile in ShinyGO (Drosophila homologs for the top set as the foreground (n = 14) and all homologs in the set as background  and a False Discovery Rate cutoff of 0.05 to correct for multiple testing. ShinyGO calculates the significance of enrichment based on the hypergeometric distribution followed by false discovery rate correction.To test for shared biological functions among the highly divergent genes, we used protein-protein BLAST  in orderB2021_0) , taking B2021_0)  and the  ShinyGO , using tOut of the set of 542,000 SNPs analyzed by Calfee et al., 394,000 SNPs (72.6%) were also included in our SNP-set. Those SNPs were analyzed in a similar way as in the study by Calfee et al. in order to get ancestry probabilities that could be compared to the clines across Argentina. The software ancestry_HMM  was usedThe Pearson correlation of the A ancestry per SNP was also calculated between the Colombian highland and lowland populations and each of the Argentinian populations separately. For each of the Argentinian populations, the difference between the correlation to the Colombian highland population and the Colombian lowland population was evaluated with the method suggested by  for compevad157_Supplementary_DataClick here for additional data file.


Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently.Nmax=254,373) or European  populations, we first evaluated the trans-ethnic genetic correlation (ρg) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with By leveraging summary statistics of 37 traits from East Asian (Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.The online version contains supplementary material available at 10.1186/s12864-023-09434-x. Over the past few years, large-scale genome-wide association studies (GWASs) have convincingly detected a large number of genetic loci associated with a series of complex traits by casting single-nucleotide polymorphisms (SNPs) across the entire genome , generatIt has been revealed that many trait-associated SNPs identified in GWASs of EUR ancestry can be replicated in other ethnic groups , 6–10, iTBC1D4, which confers muscle insulin resistance and increases risk for type 2 diabetes (T2D) and is common in Greenland but rare or absent in other populations [PCSK9, APOA, APOC, and ABCA1 all play key roles in lipid genetics in both the EUR and African (AFR) Americans, yet the precise alleles within each locus differ substantially between the two populations, supporting the perspective that lipid-associated SNPs are largely shared across ancestral groups, but the allelic structure within a locus may be shaped by population history and thus exhibits considerable heterogeneity [Indeed, ancestry-relevant heterogeneity regarding varying allele frequency and linkage disequilibrium (LD) patterns is observed for some causal variants such that a significant association detected in one population is not readily identified in others , 15, 16.ulations , severalulations , 19, andulations , 21. As ogeneity , 23.Prior work has investigated the replicability of GWAS discoveries for some particular traits, displaying the similarity and diversity of associated SNPs across ancestral groups , 24, 25.To fill in this critical knowledge gap mentioned above, here we obtained GWAS summary statistics of 37 complex traits from the EAS and EUR populations to perform a complete comparison of genetic similarity and diversity across the two populations. We first evaluated the trans-ethnic genetic correlation to quantify the extent of common genetic basis to which these traits shared . Then, wWe here give a brief overview of some important statistical methods employed in our analyses and showed more methodological descriptions in the ρg) via popcorn [We first employed linkage disequilibrium score regression (LDSC) to estimate SNP-based heritability for every trait in each population . Next, t popcorn . We iden popcorn , 28.rm) of SNP effect sizes in each of the four groups using MAGIC [Relying on cFDR and ccFDR, for each trait we divided all analyzed SNPs into four incompatible groups. For every trait we estimated the marginal genetic correlation  and coefficient of variation of MAF (MAFCV) across the populations to investigate whether there exist different patterns of LD and MAF for trait-associated SNPs compared to those null ones.Finally, to further demonstrate the direction of genetic differentiation, for each trait we conducted a GRS analysis . We alsoP = 1.83 × 10− 7), there still existed an obvious trans-ethnic distinction in heritability. For example, platelet count (PLT) showed the maximal difference in estimated heritability, with We first present the estimated SNP-based heritability . However, as expected, we indeed discovered suggestive evidence that larger sample size can lead to more accurate estimate of heritability, with Pearson’s correlation=-0.266 in the EAS population and − 0.102 in the EUR population between sample size and standard error of heritability. We further used the coefficient of variation of sample sizes (NCV) to measure the difference of sample sizes, but did not observe a significant correlation between NCV and the coefficient of variation of heritability .We did not find a substantial correlation between sample size and heritability in both populations  were larger than one and thus not included in the following descriptions. All the traits exhibited positive trans-ethnic genetic correlation between the EAS and EUR populations Table , with \dH0: ρg = 0), but 61.3% (= 19/31) were less than one (H0: ρg = 1) (FDR < 0.05), indicating potential heterogeneity in genetic effects across populations. The average of P = 0.217, possibly due to limited number of binary phenotypes under analysis) smaller than that for continuous ones , implying that the imbalanced sample sizes would not significantly affect the estimate of trans-ethnic genetic correlation. However, we observed that the difference of sample sizes in a pair of traits could affect the significance of P = 0.010 in terms of the approximate normal test).We did not discover a substantial connection between NCV and f01 + f11 in Table f10 + f11 in Table To discover trait-associated SNPs shared across the EAS and EUR populations, we carried out the cFDR analysis using a set of distinct SNPs , 28, witf11 in Table Furthermore, we identified many significant SNPs shared across the EAS and EUR populations  in the EAS or EUR population (Table f11) was negatively correlated with NCV , indicating that smaller difference of sample sizes in a pair of traits  might lead to more discoveries of shared SNPs.On average, approximately 67.1% of trait-associated SNPs in the EUR population were also detected to be significant in the EAS population, but only 26.8% of trait-associated SNPs in the EAS population were replicated to be significant in the EUR population. Among these shared significant SNPs, 44.1% and 77.0% showed genome-wide significance  null SNPs; (ii) EAS-specific associated SNPs; (iii) EUR-specific associated SNPs; (iv) population-common associated SNPs. For these SNPs, it is shown that population-common associated SNPs often exhibited a maximal positive correlation in effect sizes compared to population-specific associated SNPs and null ones Fig. . For exaP = 0.053).We found that the effect size slopes of population-common associated SNPs of eight traits  were not significantly different from one (FDR > 0.05)  .We next performed a heterogeneity test using Cochran’s Q test and again focused only on population-common associated SNPs because the population-specific associated SNPs can be naturally considered to be heterogeneous . On average, approximately 20.8% of the common trait-associated SNPs, ranging from 7.1% for TL to 52.9% for AAM, showed heterogeneous genetic effect on traits between the two ancestral populations after Bonferroni’s correction for multiple comparisons Table . We hereP = 1.04 × 10− 17), for these population-specific loci in EAS (0.22 (se = 0.02) vs. 0.28 (se = 0.04), P = 1.82 × 10− 9) or EUR (0.22 (se = 0.02) vs. 0.28 (se = 0.03), P = 1.52 × 10− 12).First, we observed that, except for three lipid traits , all the remaining traits showed substantial different variations in LD for SNPs in various groups between the EAS and EUR populations (FDR < 0.05) Fig. . The aveP = 1.54 × 10− 12), for these population-specific loci in EAS (0.33 (se = 0.07) vs. 0.43 (se = 0.11), P = 7.30 × 10− 8) or EUR (0.33 (se = 0.07) vs. 0.40 (se = 0.07), P = 2.22 × 10− 8).On the other hand, we observed that, except for three traits , all the remaining traits showed substantially different MAFCV for SNPs in distinct groups between the EAS and EUR populations (FDR < 0.05) Fig. . On averFst compared with population-specific associated SNPs in EAS (0.056 (se = 0.01) vs. 0.062 (se = 0.02), P = 0.032) or EUR (0.056 (se = 0.01) vs. 0.063 (se = 0.006), P = 6.77 × 10− 4), and they also showed a lower mean Fst relative to null ones (0.056 (se = 0.01) vs. 0.060 (se = 0.001), with a marginally significant P = 0.086)  as well as between Fst and MAFCV (P = 0.602)  (FDR < 0.05) Figure . For all) Figure , indicatf10 + f11 for EAS or f01 + f11 for EUR) as an overall measurement of genetic effect on a given trait in each population. Among these, most of the traits had a substantially different GRS between the two populations  or only population-specific associated SNPs , respectively. We viewed these new GRSs to quantify a measurement of partial genetic effect because only part of associated SNPs is employed. Interestingly, 16 traits  had consistent partial genetic effect compared to the overall one. Specifically, 15 traits  showed consistent partial genetic effect in terms of GRS calculated with EAS- or EUR-specific associated SNPs, whereas only 4 traits  exhibited consistent partial genetic effect in terms of GRS calculated with shared associated SNPs. Particularly, IS and TL exhibited a completely opposite partial genetic effect in terms of the EAS/EUR-specific associated SNPs or population-common associated SNPs compared with the overall genetic impact measured with all trait-associated SNPs.We further generated the GRS computed with only population-common associated SNPs  or in part reflects the discrepancy of classification and diagnosis of diseases in distinct populations.Using cFDR , 28 we dFurthermore, we revealed that population-specific associated SNPs were often more possible to suffer from natural selection compared with population-common associations, whereas population-common associated SNPs often displayed more consistent patterns in LD and MAF across continental populations.Especially, the GRS analysis showed that population-common and population-specific associated SNPs have potentially different genetic influence on phenotypic variation and that the genetic differentiation from associated SNPs may at least partly explain the observed phenotypic variation across diverse ancestral groups. For example, the average GRS of SCZ in the EUR population was on average higher than that in the EAS population, partly contributing to the observation that SCZ was more prevalent in individuals of EUR ancestry than those of EAS ancestry . The meaDEFB1 [However, we also observed patterns that seemed to be opposite against prior findings. For example, it was shown the mean GRS of AD in EUR was higher than that in EAS, in contrast to previous observation that Asians and Pacific Islanders were seven-fold more likely than whites to be diagnosed with AD because stronger Th17/Th22 polarization and mutations in immune-related genes such as DEFB1 –39. ThesLike the findings obtained from prior studies that however often focused only on a single trait , 40, 41,As revealed in our study, while some of associated SNPs vary substantially across populations, common associated SNPs in the EAS and EUR populations nevertheless often show much more similar effect sizes and effect directions; therefore, at least part of trait-associated SNP mapping results discovered in one population can be transferred to the other populations . It needOn the other hand, despite highly shared genetic architecture, we indeed found evidence of heterogeneity at trait-associated variants, which meanwhile challenges in assessing the transferability of risk variants across different ethnic populations based on associations discovered in EUR GWASs , 45. ForThe genetic difference may also underlie the well-known trans-ethnic dissimilarities in prevalence or characteristics of the traits –52. In oOur findings regarding the degree of common or diverse genetic components of the traits across ancestral groups have important implication in practice. For example, theoretically, genetic correlation offers the maximal boundary of trans-ethnic genetic prediction power , 12; howIn addition, it is helpful for aggregating multiple study cohorts across ethnicities to conduct trans-ethnic GWAS analysis –60, deveThe present study is not without limitation. First, as mentioned before, the studies in EAS are in generally underpowered due to much smaller sample size compared to that in EUR and hence our results may be affected by power issues. The small sample size may also lead to unstable effect estimation for these SNPs. Moreover, as shown before, we cannot completely rule out the possibility that the imbalanced sample sizes can also partly interpret the observed trans-ethnic genetic difference in traits. For example, we found that sample size was significantly positively correlated with the number of trait-associated loci in both populations, and that the estimated trans-ethnic genetic correlation would become less significantly different from one when the difference of sample sizes in a pair of traits reduced. Thus, the external validation of our results with larger sample size especially for EAS GWASs is warranted.Second, besides the difference in sample sizes, other discrepancies in study designs such as phenotypic definition, statistical methods, and covariate adjustment can be also contributable to the observed trans-ethnic genetic similarity and diversity. Examining and quantifying the relative contributions are imperative for understanding genetic heterogeneity across populations. However, compared to the difference in sample sizes which are already reported in summary statistics, the design discrepancies in GWAS are difficult to handle with only summary statistics. It needs large-scale individual-level data of phenotypes and genotypes, and is thus challenged by privacy concerns when sharing data .Third, individuals of EAS and EUR ancestries are to a great extent genetically similar, whereas more major genetic differences are expected to be found between AFR and non-AFR populations . TherefoFourth, our analysis only considered common SNPs (MAF > 0.01) and ignored rare variants, which usually have a recent origin compared with common ones from ancient origin. Theoretically, rare risk variants might be more likely to be population-specific and could possibly carry a greater risk effect, which probably leads us to underestimate the genetic heterogeneity across populations.− 8).Fifth, to our knowledge, this is the first time that the cFDR method has been employed in detecting trans-ethnic genetic overlap for a large range of complex traits. However, it implements association mapping at a fixed FDR level rather than the standard error measure such as family-wise error rate (FWER) or type I error rate which is more widely-used in a typical GWAS. FDR is more liberal compared to FWER; thus, we can discover a much larger number of trait-associated SNPs. Although the cFDR method has been well-established under the context of pleiotropy-informed association mapping in ancestry-matched populations , 28 and Our work provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.We obtained summary statistics  of 37 complex traits (10 binary and 27 continuous) analyzed on EAS-only or EUR-only individuals Table -S7. ThesFor each analyzed trait, we carried out stringent quality control in both populations by following previous work , 30, 69:N = 504) or EUR (N = 503) individuals in the 1000 Genomes Project if missing in the original summary statistics data; the threshold value of 0.01 for MAF was selected as it was widely used in summary statistics-based studies [Here, MAF was calculated with genotypes of EAS  with a 10 Mb window on EAS or EUR individuals in the 1000 Genomes Project. Then, the LD score of SNP was regressed on the square of Z-statistic of the analyzed trait, with the regression slope offering an unbiased estimate for heritability. Besides quality control procedures described above, we here further removed SNPs located within the major histocompatibility complex region (chr6: 28.5 ~ 33.5 Mb) because of its complicated structure which was often difficult to estimate accurately from an external reference panel [We first employed LDSC to estimate SNP-based heritability for each trait separately in the two populations . The LDSce panel , 70. Relce panel .ρg) via popcorn [ρg is defined as the correlation between SNP effect sizes of the trait in various ancestral groups to measure the extent to which the same SNP exhibits the same or similar impact on phenotypic variation [https://github.com/brielin/popcorn, which was calculated with genotypes of EAS or EUR individuals in the 1000 Genomes Project between the focal SNP and all the flanking ones within a 10 Mb window. To obtain an unbiased estimate of trans-ethnic genetic correlation, we implement an unbounded estimation algorithm in popcorn, which likely leads to an estimate less than − 1 or greater than 1.To evaluate genetic similarity and diversity for these traits across populations, we calculate the global trans-ethnic genetic correlation , where p1 and p2 are the observed P values of a particular SNP for the trait in the two populations, respectively; and In our application context, the null hypothesis of FDR is no association between a SNP and the trait under investigation in one population. Following the definition and principle of FDR, cFDR can be logically described as the likelihood that a SNP is null for the trait in one population based on posterior probability, given that the observed ocal FDR .2|1 is defined in a similar way. Therefore, ccFDR for identifying shared SNPs is simply expressed as ccFDR1|2=max, which is defined as the probability that a particular SNP has a false positive association with the trait in the two populations given the observed P values. Finally, SNPs with ccFDR less than a given significance threshold can be prioritized to be population-common SNPs. Although the traditional meta-analysis is also often applied in trans-ethic association studies [As the principal and conditional positions for the trait in cFDR described above are exchangeable between the populations, cFDR studies –81; hower2 = 0.1) in PLINK with genotypes of EAS or EUR individuals separately in the 1000 Genomes Project as the reference panel for LD calculation, and then combined the two sets of SNPs available from both populations  [Z-statistics were ultimately transformed into two-sided P values for the cFDR analysis based on normal approximation.Because cFDR and ccFDR are constructed for relatively independent SNPs, to generate uncorrelated SNPs, following prior work  we applins Table . In addi> 10− 4) , 83. Thi> 10− 4) ; therefoP-value method  not associated with the trait in neither population ; (ii) only associated with the trait in the EAS population but not in the EUR population ; (iii) only associated with the trait in the EUR population but not in the EAS population ; (iv) associated with the trait in both the two populations .P values of null SNPs (not associated with the trait in neither populations) follow the uniform distribution and P values of non-null SNPs (associated with the trait at least in one population) follow the Beta distribution.To measure the degree of genetic components shared by the same trait across the populations, we further applied the four-group model  which exrm) of SNP effect sizes in each of the four groups using a recently proposed method called MAGIC [For every trait we calculated the marginal genetic correlation  to evaluate such an allele frequency diversity across populations under natural selection [Fst of each SNP in the four groups for every trait with genotypes available from EAS and EUR individuals in the 1000 Genomes Project using PLINK.We further evaluated whether an identified trait-associated SNP had been under natural selection. If this was the case, then a substantial between-population differentiation would be observed in the allele frequency , 88. To election , 88, 89,Fst in the four groups, we carried out the Kruskal test for each trait, with the resulting P values being FDR corrected to account for multiple testing using the Benjamini-Hochberg procedure. We also performed a paired test to compare the average of LDCV, MAFCV, and Fst across the traits while simply ignoring the uncertainty of the estimated average in each group.To examine LDCV, MAFCV, and J was the number of SNPs used and G represented the genotype  available from the EAS or EUR individuals in the 1000 Genomes Project. For binary phenotypes, Z-statistic [To further demonstrate the direction of genetic differentiation, for each trait we conducted a GRS analysis . The caltatistic ; that isJ = f10 or f01), population-common loci (J = f11) and population-associated loci (J = f10 + f11 or f01 + f11). For the convenience of description, we referred to the first two GRSs as partial GRS, while the third one as overall GRS.For each pair of traits under analysis, three types of GRS were generated using three distinct sets of SNPs Table , includise1 and se2, respectively), we studied the influence of sample sizes of traits on the estimated heritability and its standard error.We finally attempted to investigate whether the sample size difference could influence our findings with regards to genetic similarity and diversity of traits between the EAS and EUR populations. First, after obtaining the heritability estimate and its standard error for a given trait in the two populations (denoted by Second, to assess the impact of sample size on the trans-ethnic diversity of estimated heritability, we employed NCV to measure the difference of sample sizes, and examined its relation with the coefficient of variation of heritability, the estimated slope of effect sizes for population-common SNPs and the proportion of population-common SNPs with heterogeneous effects.u=P value of u using the standard normal distribution as the null distribution, with the issue of multiple testing corrected via by the Benjamini-Hochberg method. By doing this, we explicitly took the difference of sample sizes in traits into account by modeling its standard error when comparing heritability.Third, we implemented an approximation normal test to examine the significance of the difference in the estimated heritability between the two populations by calculating Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2