Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences . Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents . This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays . Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical  Genetic association studies have become an effective research tool for identifying genetic loci related to complex phenotypes and diseases . A fundaAlgorithmic methods are exemplified by cluster analysis and principal component analysis (PCA). In genetic datasets, PCA is performed to identify systematic variation amongst individuals’ genotypes by a transformation of genotype data into a smaller group of uncorrelated variables, called principal components (PCs), usually with the constraint that each PC successively captures less variation in the original data. PCA of genotypic data yields a series of scores per individual, corresponding to the values of these PCs. Top PCs typically reflect population structure, allowing inferences of genetic ancestry. PCA has demonstrated its utility for elucidating genetic ancestry from seemingly unrelated samples , correctThe application of PCA in genetic analysis is not without challenges. Care must be taken to ensure that PCs are unbiased and reflect variation in ancestry and not some other form of systematic variation. Rather than capturing population structure, some PCs may reflect linkage disequilibrium (LD) structure. If such PCs are included as covariates in genome-wide association studies (GWASs), the power of these studies is reduced ,8,9,10. Model-based approaches, such as those embodied by the programs STRUCTURE , fastSTRThe two methods appear to have little in common at the surface due to underlying analytical differences. One involves the explicit definition of a model, while the other does not. A link between the approaches has been investigated, and strategies for identifying admixture proportions from PCs of PCA have been proposed ,17,18,19One strategy for mitigating concerns of population structure in GWASs is to employ a family-based design ,23,24,25In this paper, we examine genetic ancestry estimates between pairs of family members across the spectrum of genetic relatedness. We leverage data from the 1000 Genomes Project (1000 G)  and the An overview of the analytical strategies employed in this study is shown in raw individuals = 12,779, Nraw variants = 905,422), Affymetrix Axiom-NTR [raw individuals = 3606, Nraw variants = 642,716), or Illumina GSA-NTR [raw individuals = 14,553, Nraw variants = 669,322) platforms. Nearly all genotyping was performed at the Avera Institute for Human Genetics , with a proportion of those on AFFY6 genotyped at Rutgers University Genomics Center . All genotyping was performed according to the respective manufacturer’s protocol.All individuals in the study are participants of the Netherlands Twin Register (NTR) ,30. The xiom-NTR  . Sample and SNP quality control was conducted on each dataset separately using Plink v1.9 . A HarmoOf the total available sample, 751 individuals were genotyped on at least two of the platforms, 35 of which were genotyped on all three genotyping arrays .Unrelated individuals in the 1000 G (N = 2487) and GoNL (N = 498) reference panels were determined using HapMap3 SNPs. After the alignment of alleles between the reference panels, SNPs present in both datasets were identified . A final reference set was created with the overlapping markers and subsequent exclusion of SNPs with a call rate of less than 98% .The largest set of unrelated NTR participants was determined with the KING v2.2.0 software  with optp < 0.001. SNPs were pruned for linkage disequilibrium (LD) by removing each SNP with an R2 value greater than 0.5 with any other SNP within a 250-SNP sliding window (advanced by one SNP each iteration) using Plink v1.9 [The generation and selection of the SNPs for PCA and ADMIXTURE from each of the three platform and Harmonized datasets were determined with the unrelated individual datasets. Autosomal SNPs were selected and filtered to exclude those with a call rate of less than 95%, a minor allele frequency (MAF) of <0.01, and a Hardy–Weinberg Equilibrium (HWE) of ink v1.9 . Long-raink v1.9 , resultiIn total, four analysis datasets were generated (three genotyping platforms and the Harmonized set). The selected SNPs of each dataset were then merged with the final reference dataset and filtered to exclude SNPs with a call rate of less than 98% .For each dataset, 10 PCs were calculated with the SMARTPCA software , using 12 > 0.5 were excluded using a 250 SNP window, advanced by one SNP each iteration). The resulting SNP set  was analyzed with the cross-validation procedure. The cross-validation method partitions all the observed genotypes into K roughly equally sized folds. The procedure masks all genotypes for each fold in turn. For each fold, the resulting masked dataset is used to calculate estimates of P and Q, the population allele frequencies, and the ancestry proportions, respectively. We varied K from 3 to 27. Prediction errors, reported as the standard error of the cross-validation error estimates, were used to select the model with the lowest error. The optimal model was K = 9, corresponding to the number of distinct ancestral populations  of the nine ancestral populations using the learned clusters and ancestry proportions from the K = 9 model of the merged 1000 G and GoNL reference dataset. For each study participant, ancestry proportions of each of the nine demes were calculated (Q1–Q9). The resulting ancestry proportions were compared between genetic relatedness groups and genotyping platforms using descriptive statistics, correlations, and Euclidean distances.Quantitative evaluation of the genetic ancestry measures was performed on Euclidean distances calculated between pairs of family members based on the relatedness group. For each ancestry estimation method, Euclidean distances were used to quantify differences of the multidimensional data  between pairs of individuals with a singular metric. Within each pair of individuals, differences in the PCs or ADMIXTURE ancestry proportions were squared and then summed over all method-specific values. The Euclidean distance was calculated by taking the square root of the summed squared differences (see Formula (1)). In this manner, smaller Euclidean distances represent more similar pairs across all PCs or ADMIXTURE ancestry proportions, whereas larger Euclidean distances indicate a greater dissimilarity across the corresponding values.x,yd = Euclidean distance of J between two individuals. x,y = two individuals, representing a pair within a family. J = PCs 1–10 or Q 1–9.Formula (1). Formula for calculating Euclidean distances between pairs of individuals for ten PCs or nine ancestry proportions.We employed a non-parametric Kruskal–Wallis permutation test  to assess if there was a difference in ancestry estimates across the four relatedness groups  for each estimation method (PCA and ADMIXTURE). A Kruskal–Wallis test was employed due to the unequal sample sizes of the relatedness groups and the non-normal distributions of the Euclidean distances, particularly in the MZ twin group. A post-hoc Dunn test was used to determine which familial group(s) differed. Using the MZ twin group , we again implemented the Kruskal–Wallis permutation scheme to evaluate if there were ancestry estimation differences due to the genotyping platform  for each estimation method. A post-hoc Dunn test was used to determine which platform(s) differed. We used the same statistical strategy to evaluate differences in ancestry estimates from individuals genotyped on two platforms for both estimation methods. Here, the analysis was based on three groups .Visualization of the projected PCs 1–10 can be found in To examine the relationship of PCs across datasets representing distinct genotyping arrays, we calculated correlations of PCs 1–10 within and between datasets using the results of array-mimicked reference populations as the input. Correlations of PCs 1–10 within each genotyping platform show no correlation, reflecting the inherent statistical property of PCs in that they are uncorrelated . The corWe next assessed the differences in PCs between the MZ twin and DZ twin/sibling pairs. Since siblings have the same parents, it was expected that the differences in PCs between siblings would be near zero. Since MZ twins arise from the same fertilized egg, the expectation for their PC differences is zero, with non-random values reflecting measurement errors or post-splitting/somatic mutations . The resp < 0.001 and Dunn test p < 0.001 for all familial group comparisons). Across all datasets, Euclidean distances were inversely related to the genetic relatedness between the pairs, as expected . Significant differences were observed between platforms produced by different manufacturers (Affymetrix vs. Illumina) and compared to the Harmonized dataset . There were no significant differences between Euclidean distances in the MZ twins genotyped on AFFY6 and AXIOM (Dunn test p = 0.497).Using the MZ twin group as a genetic control to assess platform effects on genetic ancestry estimates, we observed statistically significant differences across genotyping platforms . The smallest distance values were obtained for individuals with genotypic data from the AFFY6 and AXIOM platforms. Larger Euclidean distances were observed for individuals with data from each of the array manufacturers: Affymetrix (either AFFY6 or AXIOM) and Illumina (ILLGSA). Because the platform SNPs on which the PCs are based are not identical, the observed differences can be attributed to input SNP variation.For individuals with genetic data from multiple genotyping platforms  is illustrated in To estimate individual ancestry in the NTR samples, we projected them onto the population structure  derived from the 1000 G and GoNL reference datasets by specifying K = 9. Descriptive statistics of the ancestral population proportions (Q1–Q9) from the three platform and Harmonized datasets of the NTR participants are shown in The stacked bar charts in As observed in the stacked bar charts, the ancestral fractions of all the NTR individuals are nearly indistinguishable from each other for across platforms. This finding highlights a relatively similar population composition of individuals genotyped on each platform. Comparatively, PCs can reveal more fine-grained differences between the same individuals, such as North–South or East–West clines. Within each platform dataset, a small number of genetically diverse and admixed individuals are shown on the left side of each figure. These individuals show stark variation in the ancestry proportions relative to the bulk of the NTR sample population, indicating more heterogenous ancestry and deviation from the majority Northern and Western Europe origin as captured by AP4. Similar admixture and population heterogeneity patterns among the NTR samples were observed in the PCs .Correlations of ancestry proportions within and between genotyping platforms for all the NTR participants are shown in We compared the estimates between the MZ and between DZ twins/sibling pairs to examine the ancestry proportions in more detail. The results are shown in p < 0.001 and Dunn test p < 0.001 for all familial group comparisons).Consistent with the evaluation of PCs, we calculated Euclidean distances over the nine ancestry proportions within family pairs according to Formula (1). Comparable to the Euclidean distances of PCs, the distances in ancestry proportions increased as the degree of relatedness between the pairs of individuals became less (p < 0.001). The individual platform comparisons all yielded statistically significant differences (Dunn test p < 0.001), except for the comparison between the AFFY6 and Harmonized platforms.Again, using the MZ twins as a genetic control group, we compared Euclidean distance measures as a function of the genotyping platform within this group. Overall, we observed statistically significant differences , statistically significant differences existed for all group comparisons . Like the Euclidean distances of PCs of individuals genotyped on multiple arrays, the smallest distances were observed for those with genetic data obtained from the Affymetrix platforms  minima or maxima, respectively. The NTR individuals with values greater than or equal to the reference minimum or less than or equal to the reference maximum were considered inliers. Outliers were determined for each PC and each value of Q. The total number of outliers across all PCs and values of Q was determined by identifying unique individuals.p = 0.005; PCA Spearman’s rho 0.04, p = 0.122). Overall, the Euclidean distances calculated from the PCs were smaller in magnitude than those derived from ADMIXTURE ancestry proportions. Though the ADMIXTURE comparison yielded a statistically significant correlation, it was a very weak positive relationship. Thus, the sibling offspring of more diverse parents show slightly more variation in ancestry to each other compared to the sibling offspring with parents of similar genetic ancestry. However, this weak relationship appears to depend on the method used for calculating ancestry estimates. Regardless of the method, the near-zero relationship indicates that the sibling offspring of more diverse parents are slightly more dissimilar than the progeny of parents of similar genetic ancestry.Using the calculated PCs and ADMIXTURE ancestry proportions, we also assessed if the sibling offspring (non-MZ) of diverse parents were more dissimilar to each other than those with parents of similar ancestry . EuclideWe examined ancestry estimates as a function of the genotyping array and genetic relatedness within nuclear families and evaluated estimates of genetic ancestry obtained from PCA and ADMIXTURE in twin pairs and their family members based on whole-genome SNP data from three genotyping platforms and a harmonized dataset. Utilizing reference data from 1000 G and GoNL as global population surrogates, we demonstrated that PCs across genotyping arrays are not the same despite identical analytical strategies. We ascribe this finding to differences in platform SNPs. Across familial groups, the Euclidean distance measures of PCs were inversely related to the degree of genetic similarity between individuals. The greater the genetic relatedness between two individuals, the smaller the Euclidean distances of their respective PCs. Given that the twin/sibling offspring have the same parents and possess a genetic profile derived from the same pool of segregating alleles, this finding is in line with theoretical expectations. However, statistically significant differences were observed between all familial groups, even between the DZ twin/siblings and parent/offspring pairs, which would be expected to be ~50% similar. These differences might also be explained by the fact that within pairs of a familial group, there are variable numbers of ancestry outliers. This intrinsic difference would result in greater variation in the Euclidean distance measures.We used ADMIXTURE, a model-based ancestry estimation method, to detect the optimal number of ancestral populations in the global reference data. Ancestral population 4 was the major ancestry fraction of the NTR participants, representing Northern and Western Europe and the Netherlands. The differences in ancestry proportions of the nine populations were near zero between the MZ twins and DZ twins/siblings. Consistent with the evaluation of PCs, the Euclidean distance measures of ancestry proportions were inversely proportional to the amount of allele sharing between family members.Given the uniqueness of the NTR data, we evaluated estimates of genetic ancestry from genotypic data from the independently genotyped MZ twins. Importantly, within-platform differences of the MZ twins were non-zero. Although measurement error may play a role, differences within the MZ twin pairs cannot simply be ascribed to measurement error alone. One DNA sequencing study showed in a 40- and a 100-year-old MZ twin pair that somatic mosaicism leads to differences within pairs , and morWe examined ancestry estimates of individuals genotyped on multiple arrays and observed the largest Euclidean distances between the Affymetrix (AFFY6/AXIOM) and Illumina (ILLGSA) platforms. These variations can be attributed to array manufacturer differences, including array design, chemistry, and platform-specific genotype calling algorithms. The bar plots of the Euclidean distances for the MZ twins, who should be genetically identical, also exhibit these differences. It is crucial to consider the unequal sample sizes per platform, the variability in the composition of genotyped individuals, and the limited number of samples with genotypes from multiple arrays, which impose limitations on the study’s findings. Regardless of the ancestry estimation method and familial group, the Euclidean distances for individuals genotyped on the ILLGSA platform are smaller than the other platform datasets. This finding is consistent with the fact that the Illumina GSA-NTR array includes an improved backbone for capturing population variation and was further customized to include additional content to aid in population-specific GWASs . PerhapsThe accuracy of ancestry inference methods depends on various factors, including the distribution of human genetic variation across geographic regions, the types and number of genetic markers used, the sampled population, the choice of reference populations, and the statistical methods for interpreting variation patterns . AncestrWhen investigating genetic ancestry, a variety of statistical tests have been recommended for selecting the number of PCs  or anceFor many years, PCA has been widely used in genetic association studies to address population stratification confounding. However, recent scrutiny has raised concerns about potential biases associated with this technique in population genetic research  and GWASOverall, we show genetic ancestry inference methods can provide reliable estimates of individual genetic ancestry across the genetic relatedness spectrum from genetic data from various genotyping arrays. The consistency of the estimates is contingent upon the inclusion of necessary proxies of global population diversity and proper analytical execution. Genetic relatedness can confound individual ancestry estimates in the absence of appropriate reference population samples . Several


Horticultural crops play a vital role in human diets, providing essential nutrients and contributing to food security, economic stability, and cultural value. However, horticultural production is facing numerous challenges due to climate change and other environmental stresses in the past few decades. These challenges include increased temperatures, reduced precipitation, soil salinity, extreme weather events, and emerging pests and diseases. Therefore, genetic resources are vital to developing more resilient horticultural varieties that adapt to changing environmental conditions.Integrating new OMICS and phenotyping approaches in horticultural genetic resources conservation and utilization has revolutionized our understanding of plant diversity and breeding. Genomic approaches enable rapid analysis of DNA sequences, identifying genetic variations, and exploring gene functions and metabolic pathways. Phenomics approaches measure plant traits at a large scale, providing valuable data on genotype-environment interactions. These tools help breeders identify superior horticultural varieties with desired traits, such as improved yield and resilience. In addition, genome editing technologies like CRISPR-Cas9 offer precise modifications to the plant genome, accelerating breeding and introducing beneficial traits.Recent biotechnological advancements, such as next-generation sequencing and genomics-based approaches, have enabled the selection of potential parents from germplasm collections. These methods also include Omics approaches, like transcriptomics, proteomics, and metabolomics, to create new cultivars with higher adaptability to environmental conditions.Breeding resilient cultivars depends on genetic resources and cutting-edge technologies. Next-generation sequencing allows for cost-effective analysis of DNA sequences to identify significant variations and genes linked to desirable traits. High-throughput phenotyping platforms provide a comprehensive understanding of phenotypic variations and their interactions with the environment, improving breeders’ knowledge of plant morphology, physiology, and biochemical composition. Advancements in genotyping platforms facilitate the identification of genetic variations linked to important traits, supporting genomics-based approaches like GWAS, MAS, and GS. Multi-omics approaches, such as transcriptomics, metabolomics, and proteomics, expand our understanding of complex biological processes underlying horticultural traits. Integrating horticultural genetic resources with next-generation genomics and phenomics technologies enables breeders to develop new cultivars with improved adaptability, productivity, and quality, ensuring resilience in the face of climate change.Genetic engineering techniques have led to the development of GMOs by transferring desirable genes between plant species to improve traits like pest resistance, disease resistance, herbicide resistance, and nutritional quality. However, the use of GMOs is controversial due to safety and environmental concerns. With CRISPR-Cas9 gene editing, precise modifications can be made to the plant genome to introduce beneficial traits and remove undesirable ones.Recent research and developments in using genetic resources for horticultural crops have focused on various aspects of breeding for resilience, conservation of genetic resources, and molecular tools for selection.Ahmad et al. demonstrated a considerable diversity in phenological and biochemical characteristics of date palm cultivars and a correlation among several elements of the studied germplasm, which can be exploited in future breeding programs.For example, Huded et al.). In addition, the identification of cold-tolerant Iranian olive varieties has provided a potential solution for establishing olive groves under cold climate conditions . In another report, for genetic polymorphisms evaluation, researchers applied the K-seq protocol in both ornamental species, including Ranunculus asiaticus L. and Anemone coronaria L. Finally, 11,542 SNPs were chosen for the identification of genetic variability of eighteen commercial varieties of R. asiaticus, while 1,752 SNPs for identification genetic variability in six cultivars of A. coronaria .Furthermore, studies have focused on the identifying genes associated with desirable traits, such as disease resistance and abiotic stress tolerance. For example, the identification of the NAC25 gene in coffee has revealed its independent evolution in coffee species, providing valuable insights into the genetic diversity of this crop (Epigenetic modifications are changes in gene expression that are not caused by changes in the DNA sequence but are induced by environmental factors, contributing to the development of desirable traits. Biotechnological advancements, such as next-generation sequencing and genomics-based approaches, have enabled the selection of potential parents from germplasm collections. High-throughput genotyping platforms and mapping techniques facilitate the identification of genes associated with desirable traits. However, challenges to effective utilization of genetic resources include limited access for small-scale farmers and breeders in developing countries and commercialization leading to monopolization by a few companies, limiting their availability to others.Therefore, efforts are being made to address these challenges and promote the effective utilization of genetic resources. These efforts include the development of international agreements, such as the International Treaty on Plant Genetic Resources for Food and Agriculture, which aims to ensure the conservation and sustainable use of plant genetic resources. In addition, there are initiatives to promote the sharing of genetic resources among countries and institutions, such as the Global Crop Diversity Trust and the CGIAR System Organization.MS-H: Writing – original draft, Writing – review & editing, MA: Writing – original draft, Writing – review & editing, RK: Writing – original draft, Writing – review & editing. MF: Conceptualization, Writing – original draft, Writing – review & editing.


Posidonia australis distribution to its historical distribution during the Last Glacial Maximum (LGM). Spatial genetic structure was estimated for 44 sampled meadows from across the geographical range of the species using nine microsatellite loci. Historical and contemporary distributions were similar, with the exception of the Bass Strait. Genetic clustering was consistent with the three currently recognized biogeographic provinces and largely consistent with the finer‐scale IMCRA bioregions. Discrepancies were found within the Flindersian province and southwest IMCRA bioregion, while two regions of admixture coincided with transitional IMCRA bioregions. Clonal diversity was highly variable but positively associated with latitude. Genetic differentiation among meadows was significantly associated with oceanographic distance. Our approach suggests how shared seascape drivers have influenced the capacity of P. australis to effectively track sea level changes associated with natural climate cycles over millennia, and in particular, the recolonization of meadows across the Continental Shelf following the LGM. Genetic structure associated with IMCRA bioregions reflects the presence of stable biogeographic barriers, such as oceanic upwellings. This study highlights the importance of biogeography to infer the role of historical drivers in shaping extant diversity and structure.Historical and contemporary processes drive spatial patterns of genetic diversity. These include climate‐driven range shifts and gene flow mediated by biogeographical influences on dispersal. Assessments that integrate these drivers are uncommon, but critical for testing biogeographic hypotheses. Here, we characterize intraspecific genetic diversity and spatial structure across the entire distribution of a temperate seagrass to test marine biogeographic concepts for southern Australia. Predictive modeling was used to contrast the current  Posidonia australis. The combined approach shows how historical and contemporary seascape drivers have influenced spatial genetic structure in P. australis which can effectively track sea level changes associated with natural climate cycles.We combined predictive mapping with population genetic data to test two biogeographic hypotheses in a widespread temperate seagrass,  TeasingP. australis sheath remains from seafloor sedimentary cores in western and central‐southern Australia, estimated at up to 3500 years old . The passive continental shelf margins surrounding the Australian continent evolved through a series of seafloor‐spreading episodes 120–55 mya that created habitat for coastal marine plants , Maugean (Bass Strait), and Peronian (east coast)  How is genetic diversity spatially structured among meadows sampled from across the 5300 km range of this species; (2) Does this spatial genetic structure correspond to the broad‐ and fine‐scale biogeographical models for the temperate Australian continental shelf; and (3) Were historical meadows widespread or restricted in isolated refugia during the LGM?We modeled the historical and contemporary distributions of 22.1P. australis meadows were sampled following methods described by Sinclair et al. , using methods previously described /(N−1)) was estimated for each meadow, where G = number of multilocus genotypes (MLGs) and N = number of samples , observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (F) using genalex 6.503 , allelic diversity (Na), and expected heterozygosity (He).Clonal richness , assigned individuals to them, and identified admixed or migrant individuals. The analysis was performed for K = 1–44. A total of 10 independent runs were made for each value of K with 10,000 “burn‐in” and 100,000 replicates, using an admixture model with allele frequencies correlated across populations and without sampling locations specified as priors. Calculation of ΔK was used to infer the most likely number of clusters for 10 replicate runs for each K  was implemented in  Wright,  and D  among a priori recognized biogeographical provinces , IMCRA inshore coastal provincial bioregions (29–38), and three “soft” seascape features as barriers to dispersal  was performed in FST  relationships were assessed through the association between genetic differentiation (FST and D) and oceanographic distance (km) across the whole species range and by coastlines: latitudinal eastern , western , and longitudinal southern  using Mantel tests (9999 iterations). Oceanographic distance was calculated using the “gdistance” package in R (3.4.4), which estimated the least‐cost (shortest) distance via water between GPS locations of all sampled meadows.Overall and pairwise population differentiation was estimated using  Wright,  and D , of which 1153 reliable records were mapped using ArcGIS, after duplicates and points with errors in geo‐positioning were removed  and GeoScience Australia (https://www.ga.gov.au/data‐pubs) online data portals, at a spatial resolution of 5 arcmin. Highly correlated variables were removed to avoid excessive autocorrelation among predictors and reduce model overfitting based on variance inflation factor (VIF) values, which was calculated for each variable using the “vifstep” function in the “usdm” R package , were removed. Identical, commonly occurring tri‐allelic genotypes were reduced to the same diploid genotype, so as not to alter the total number of MLGs . All meadows contained a set of unique MLGs, with the exception of northern range edge meadows on the west and east coasts. One MLG was shared between two meadows within the western gulf of Shark Bay  and seven MLGs were shared among northern meadows on the east coast . Clonal diversity within meadows varied from a single MLG to all samples having unique MLGs  and based on MLGs only . However, the relationship was not linear, with meadows close to Hardy–Weinberg equilibrium once clonal diversity was above ~0.20.Patterns of allelic diversity in Western Australian meadows were mostly consistent with a stepwise mutation model (SMM). There were large gaps in the presence of alleles (by locus) in southern and eastern Australian meadows relative to the west coast meadows. Private alleles were almost exclusively observed in western and southern Australian meadows. There was a highly significant relationship between clonal diversity R, Na, He) down the west  and east coasts  : west coast , western‐south coast , central‐southern coast , Bass Strait , and east coast meadows .A plot of the absolute second‐order rate of change of the likelihood distribution from the STRUCTURE analysis of the complete data showed a large peak at  Figures , S2 was p < .001; Table p < .001; Table At a higher level, genetic clustering was largely consistent with the three a priori marine biogeographic provinces . There was significant pairwise differentiation among all meadows  and highly clonal northern range edge meadows on the east coast which are 190 km apart . There was a highly significant IBD relationship between pairwise oceanographic distance and genetic differentiation across the species range (n = 44 meadows: Mantel tests R2 = 0.554 (FST), 0.805 (D)), with latitude along the western (n = 12: R2 = 0.646 (FST), 0.787 (D)) and eastern (n = 12: R2 = 0.377 (FST), 0.605 (D)) coastlines, and longitude across southern Australia (n = 20: R2 = 0.456 (FST), 0.708 (D))  shows potential for widespread fringing meadows, restricted to the edge of the contemporary continental shelf. The shallow continental shelf habitat available to benthic species including seagrass was less than the present day, with steep shelf edges reducing the total area to fringing meadows across much of the range. Consequently, the spatial extent of the modeled current P. australis distribution is about 110,000 km2, while the extent of the modeled historical distribution during the LGM was around 80,000 km2. The historical range showed greater northward range extensions on west and east coasts than the current distribution. The historical distribution has a very similar longitudinal range to the contemporary distribution, with the exception of what is currently the Bass Strait, which was entirely above sea level at the LGM. The presence of a large, hypothesized paleomeadow on the edge of the Otway Shelf was supported , are located in a region recognized as the origin of diversification for Australian Posidonia, and coincide with high species diversity and endemism , with common seascape features influencing distributions and genetic structure. Two notable historical differences included range extensions into lower latitudes and the Bassian Landbridge  and the kelp Ecklonia radiata , an observation also noted in seadragons Phyllopteryx taeniolatus  is shared among many marine invertebrate taxa, and sequence divergence estimates pre‐date the LGM  contained genetic signatures of admixture and/or restricted gene flow associated with “soft” biogeographic or oceanographic barriers to dispersal, geomorphic features and/or largely reflected a naturally fragmented distribution. Admixture coincided with the Great Australian Bight (IMCRA 32) and southeast transitions (IMCRA 37). The high levels of genetic diversity and admixture between western and central southern clusters support a more continuous distribution for P. australis across southern Australia, as our distribution models suggest. The Great Australian Bight is not widely reported as a temperate marine barrier . The western Bass Strait transition (IMCRA 34) contains a seasonal upwelling, Bonney Upwelling  was reported in species with low and high dispersal capabilities ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; visualization ; writing – original draft ; writing – review and editing . Renae K. Hovey: Conceptualization ; formal analysis ; methodology ; visualization ; writing – review and editing . Siegfried L. Krauss: Formal analysis ; funding acquisition ; writing – review and editing . Janet M. Anthony: Data curation ; methodology ; writing – review and editing . Michelle Waycott: Funding acquisition ; writing – review and editing . Gary A. Kendrick: Conceptualization ; funding acquisition ; investigation ; project administration ; writing – review and editing .https://datadryad.org/stash/share/PpQGkzGdMRNsQEqqAnLaOKvYRjzjWX4‐DIQb5qjgmdU.This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at Figure S1.Click here for additional data file.Table S1.Click here for additional data file.Table S2.Click here for additional data file.


Rare disease patients often endure prolonged diagnostic odysseys and may still remain undiagnosed for years. Selecting the appropriate genetic tests is crucial to lead to timely diagnosis. Phenotypic features offer great potential for aiding genomic diagnosis in rare disease cases. We see great promise in effective integration of phenotypic information into genetic test selection workflow. In this study, we present a phenotype-driven molecular genetic test recommendation (Phen2Test) for pediatric rare disease diagnosis. Phen2Test was constructed using frequency matrix of phecodes and demographic data from the EHR before ordering genetic tests, with the objective to streamline the selection of molecular genetic tests  for clinicians with minimum genetic training expertise. We developed and evaluated binary classifiers based on 1,005 individuals referred to genetic counselors for potential genetic evaluation. In the evaluation using the gold standard cohort, the model achieved strong performance with an AUROC of 0.82 and an AUPRC of 0.92. Furthermore, we tested the model on another silver standard cohort , achieving an overall AUROC of 0.72 and an AUPRC of 0.671. Phen2Test was adjusted to align with current clinical guidelines, showing superior performance with more recent data, demonstrating its potential for use within a learning healthcare system as a genomic medicine intervention that adapts to guideline updates. This study showcases the practical utility of phenotypic features in recommending molecular genetic tests with performance comparable to clinical geneticists. Phen2Test could assist clinicians with limited genetic training and knowledge to order appropriate genetic tests. The formal definitions of rare diseases differ slightly across countries as conditions that are more prevalent in one region may be rarer in other areas5. Individuals with rare diseases often endure a long diagnostic odyssey6, meeting with various disease specialists, undergoing numerous medical tests and procedures, and sometimes receiving misdiagnoses or inconclusive results along the way. Diagnostic delay can inevitably result in tremendous anxiety and financial burdens for patients and their family, missed treatment opportunities, compounding with disease progression7. Misdiagnosis can lead to inappropriate care that can be costly and associated with side effects. There are several well-established factors associated with delays in diagnoses. Most patients have limited access to experts with in-depth knowledge in recognizing and managing rare diseases9. Additionally, rare diseases marked by heterogenous symptoms further confuses which subspecialists to consult10. Consequently, patients receive multiple unnecessary referrals that often involve various specialists and diagnostic tests11.Rare diseases each are uncommon conditions affecting fewer than 200,000 people in the United States12, with approximately 80% of rare diseases having a genetic basis13. Consequently, genetic testing is often regarded as the gold standard for yielding a definitive diagnosis. Currently, three main categories of genetic testing are adopted by most healthcare systems and available in genetic testing laboratories: i.e., cytogenetic, biochemical, and molecular testing14. Cytogenetic testing  entails the examination of chromosomes for abnormalities, whereas biochemical testing focuses on the analysis of enzymes or metabolites14.A significant of portion of rare disease (50% - 75%) present initially in children15. It offers high resolution and accuracy in identifying genetic abnormalities16. With molecular genetic testing, two options are commonly offered: whole-exome sequencing (WES)/whole-genome sequencing (WGS) or gene panels17. While WES/WGS can provide more comprehensive genome-level testing, these tests typically come with much higher costs and longer turnaround19. Gene panels can be a viable alternative to reduce expenses and enhance effectiveness for certain clinical indications. Gene panels are most effective when the disease-causing genes are known and included in the panels19. Either excessive or inadequate utilization of genomic tests (i.e. WES/WGS) can diminish the benefits for patients and impact the cost-effectiveness of testing22. Thus, an appropriate selection of molecular genetic tests is critical to obtain early, accurate genetic diagnoses, alleviating burden on patients, health care providers and society.Molecular testing has emerged as a central approach for diagnosing genetic disease by sequencing DNA for mutations in one or more genes23. A systematic approach or comprehensive guideline regarding the selection of genetic tests for rare disease diagnosis during clinical practice is underdeveloped24. A large disparity is often perceived in adopting and ordering genomic tests, strongly associated with physicians’ preferences and level of knowledge and understanding about available tests24. While utilizing comprehensive phenotype information for variant interpretation for WES/WGS analysis is well-established28, methods for selecting optimal diagnostic genetic testing based on patients’ phenotypic features is not well established19. Electronic Health Records (EHRs) are home to rich and nuanced clinical phenotypic data and promise to enable phenotype-driven analytical models27. Pipelines have been developed to extract phenotypic features from the EHR, thereby enriching the understanding of the relationship between genetic diseases and phenotypes29. Large consortiums such as eMERGE have leveraged the EHR phenotypes and their linked biobank genetic data to conduct genome-wide association studies30. Previous findings32 have suggested a significant difference in phenotypic features between control groups and individuals with Mendelian diseases, highlighting the potential of using phenotypes in facilitating the identification of patients with genetic diseases. Morley et al. demonstrated the utility of phenotypic features extracted from structured EHR billing codes in identifying individuals suitable for genetic testing33. Building upon their work, this study aims to develop a clinician-facing, phenotype-driven molecular genetic test recommendation (Phen2Test) for pediatric rare disease diagnosis. There is a great need for such a tool since most clinicians do not have knowledge of genetic tests. Our tool will aid such clinicians in selecting the appropriate molecular genetic test orders.The current decision regarding the selection of WES/WGS and gene panels is mostly based on clinicians’ assessment and judgementThe study developed binary classification models by leveraging phenotypic features extracted from the electronic health records (EHR). The model’s output is either gene panel (negative class) or WES/WGS (positive class). An overview of the study design is depicted in 36 compared to chromosomal microarray (CMA)37. The study was approved by Columbia University Intuitional Review Board.The initial dataset consisted of 1,005 rare disease patients who visited Columbia University Irving Medical Center (CUIMC) for potential genetic evaluation between 2012 and 2023. Given the scope of this study, individuals with genetic tests ordered for non-diagnostic purposes , or recurrence risk prediction in a family with an established mutation were also excluded. We further narrowed the inclusion criteria to only focus on the pediatric setting by excluding individuals whose age at the first appointment date was 19 years or older. The test order status of the patients’ genetic tests was recorded manually into a research database (REDCap) and categorized into three groups: patients who received (1) whole-exome sequencing/whole-genome sequencing (WES/WGS) only, (2) gene panel-based variant detection (gene panels) only, or (3) gene panels first, followed by WES/WGS due to negative panel results. In this study, we considered the third group as WES/WGS instead since they ultimately received genomic sequencing. The clinical decisions regarding genetic test ordering were made through a comprehensive assessment that considered various factors. The microarray-based tests was not considered in this cohort because WES/WGS was perceived to offer significantly greater clinical and diagnostics utility in testing children with suspected genetic diseases38, WES/WGS can be considered as a first-tier test for patients with congenital anomalies, developmental delay, intellectual disability, neurological developmental disability 40, or seizures42. Genetic test labels were updated to WES/WGS if any of the above conditions were entailed in the summary. It is important to note that the phenotype summary was manually recorded in the research database, separate from the original EHR data (used for model training), and was not directly utilized as features for model training. Labels before and after adjustment were utilized to train the models separately, and performances were documented and compared.Genetic testing recommendations and guidelines are dynamic and are constantly evolving. Given that we collected patient data from 2012–2023, it is important to note that decisions documented at the time of patient visits may not reflect the optimal testing choice today given the latest guidelines. We therefore manually adjusted the labels for optimal test orders based on the phenotype summaries and the latest literature for best genetic tests for the corresponding phenotypes. These phenotype summaries were provided in the initial curated genetic dataset, where clinicians documented key phenotype indicators by reviewing patients’ previous records. We then harmonized these findings in alignment with the updated guidelines. According to current recommendation by American College of Medical Genetics and Genomics (ACMG)In this study, we extracted phenotypic features from both structured and unstructured data. Since the recommendation system is intended for use during the test ordering process, we exclusively focused on individual’s EHR data before the index date, established as their respective test date. In cases when the test date was unavailable, we used the genetic appointment visit date as the index date, assuming negligible gaps between the two, thus allowing for their interchangeable use. A list of features is outlined in 44, and have been used to train classification models for various clinical genetic tasks33. Afterwards, we implemented two data aggregation approaches of the identified phecodes per individual, such as (1) the frequency of each identified phecode (Freq_phecodes), and (2) sum of unique phecodes (Sum_phecodes). To avoid the redundancy introduced during the concept mapping, we opted to count the phecodes only once if two or more identical phecodes were observed on the same date for a given individual. Besides phecodes, Human Phenotype Ontology (HPO)45 is another standard reference for describing phenotypic abnormalities and has been adopted by various rare disease organizations, clinical labs, biomedical resources, and clinical software utilities46. We constructed an additional feature set by mapping OMOP condition concepts to HPO terms followed by the categorization of HPO terms according to their underlying Phenotypic abnormality . The counts of each HPO-based organ systems of phenotypic abnormality (n=23) were subsequently utilized as input features (Freq_HPO). Demographics characteristics  as well as different types of patient care  at the index date were also extracted and incorporated into the modeling process.We extracted the patients’ medical conditions from the Observational Medical Outcomes Partnership (OMOP) database, which uses a standardized database schema and set of terminology mappings for integrating diverse healthcare data sources from different EHR systems for research and analysis. The obtained conditions were first converted from standard OMOP concept to ICD10 CM and mapped to the corresponding phecodes (version 1.2). Phecodes are primarily designed for phenome-wide association studies (PheWAS)47), only including phenotypes observed in the individuals. Following a similar data aggregation approach described above, we calculated the frequency of each identified phecode based on extracted results (Freq_phecodes_notes) and the sum of uniquely identified phecodes (Sum_phecodes_notes). In addition, we derived the cumulative count of clinical notes documented prior to the index date (Num_notes), where these combined factors  were leveraged as a proxy to reflect the severity of a patient’s condition.We applied regular expressions to identify additional phenotypic features from clinical narratives that matched with the phecodes system. Additionally, we employed a trained language model developed by Aken et al. for negation and scope detection  and evaluated the impact of feature reduction on classification performance. The process went through five iterations for different sets of features, where performance was recorded at each iteration and averaged in the end. The evaluation metrics  were calculated using a threshold of 0.5, along with the area under the ROC curve (AUROC) and precision-recall curve (AUPRC). The combination achieving the highest averaged AUPRC was considered as the optimal model and was used for further analysis.We generated multiple feature sets, incorporating combinations of the previously mentioned attributes, and trained them using three different classifiers: Logistic Regression, Random Forest, and XGBoost. A nested cross-validation was performed for hyperparameter optimization and model evaluation, involving a three-inner fold cross validation on 80% of the entire dataset and the remaining 20% was reserved for testing. This partitioning was performed while maintaining the consistent distribution of prediction labels (types of genetic test). The models with the optimized hyperparameters achieving the highest F1-score on validation set were then selected and evaluated on the testing set. We also evaluated different strategies in mitigating class imbalance issue, including class weight adjustment, upsampling using SMOTE20. Factors like genetic expertise, education, and institutional policies also impact test choice9. The main objective of this study was to construct a recommendation system based solely on EHR-documented phenotypes/conditions to provide decision support purely from a clinical perspective. Therefore, to mimic a test environment where only clinical factors are considered in the choice of genetic test, a genetic counselor independently chart-reviewed a group of randomly selected individuals (n=30) from the test set.Genetic test decisions often consider economic and financial factors, patient preferences, insurance, and current technology limitationsThe objective was to verify whether the initial test recommendations were solely based upon the reported phenotypes/conditions and were consistent with current practice guidelines. Any observed misalignments were captured and corrected. We performed 200 iterations of bootstrapping on the 30 reevaluated labels, sampling with replacement, and for each iteration, we computed performance metrics AUROC and AUPRC according to the reevaluated labels. Performance was documented for each iteration and averaged at the end of the run. The 95% confidence interval was constructed using the calculated mean and standard error, with a critical value of 1.96, presenting a more reliable representation of the model’s performance.49. We first identified a “genetic” cohort from the structured database as individuals who had visited the genetic clinics, undergone genetic tests, or had genetic related measurement analysis  between 2012 and 2023. Next, we excluded patients whose age at the date of genetic appointment/measurement was 19 years or older. Afterwards, we implemented a two-step process to identify the types of genetics tests recommended for each patient: 1) identified clinical notes that contained the keywords such as “genetic”, “letter”, “visit”, “progress note” in their titles, 2) further identified notes that contained keyword “panel”, “exome”, “genomic”, “WES”, or “WGS”. Some consideration was taken to improve the identification of the “panel” group. This involved excluding cases related to biochemical testing, viral panel testing, routine blood panel, or for screening purposes. A complete list of keywords/regular expressions is provided in Supplemental Table 2. The label extraction algorithm was evaluated on our initial testing cohort where annotations were available, achieving an accuracy of 89% in identifying labels (after adjustment). While recognizing the possibility of discrepancies between actual ordering and labels extracted from notes, this phenotype-derived cohort served as a reliable substitution when the manual label annotation was not feasible for the large scale of data. Finally, visit dates associated with the notes when the keywords were identified were used as the index dates. For individuals who had both WES/WGS and gene panel tests documented, we considered the former as the label, with the index date being the earliest date when the testing recommendation was made. To reflect the evolving clinical practice trend, the eligible individuals  were divided into four calendar year ranges according to their index dates: 2012–2014, and 2015–2018, 2019–2021 and 2021–2023. The same feature extraction procedure described above that yielded the best performance in the held-out testing dataset was implemented to extract features. The trained model was then employed to examine the influence of temporal variability on model performance.We further analyzed our model on a larger cohort in the OMOP database using a phenotype-derived approach50, where each feature importance was calculated as the mean decrease in Gini impurity specifically referring to normalized total impurity reduction values for nodes where splitting was based on that feature51. The correlation between the types of phenotypic abnormalities and labels  was quantified using ordinary least squares (OLS) regression technique, from which we derived the corresponding odd ratios (ORs) and p-values. Moreover, the phecodes were clustered into 17 groups based on PheWAS categorization (version 1.2) to gain a system-level perspective on how phenotype abnormalities in various organ systems influenced the test recommendations. This phecode-based finding was further juxtaposed with an alternative HPO ontology to identify any discrepancies related to the choice of the standard vocabulary for feature representation.Regarding the feature importance, we quantified the significance of phenotypes in the main prediction model using Gini impurity criterionThe demographic characteristics of the initial manually curated cohort and the large phenotyping-based cohort extracted from the OMOP database are provided in Supplemental Table 3.After experimenting with different feature sets and sampling strategies, the model achieving the highest average AUPRC  was Random Forest built on features including phecodes aggregated by frequency derived from structured data, demographics characteristics and the number of notes. It applied class weight adjustments to address class imbalance and did not perform feature reduction. The ROC curve (average AUROC 0.822) and precision-recall curve are depicted in Clinicians reviewed randomly sampled cases (n=30) from the test cohort and assigned recommended genetic test labels based on phenotypes only. Out of the 30 instances, 3 testing labels (10%) were different from the newly annotated ones. Among those, two (2/3) of the altered cases were switched from gene panels to WES, with primary indicator diseases being retinal diseases and congenital myasthenic syndromes (CMS), and sensorineural hearing loss (SNHL). One case was altered from WES to panel, with the primary disease indicated as obesity. The reannotated labels were regarded as the most ideal genetic tests based solely on phenotypes, which would be evaluated alongside our model predictions. The 95% confidence interval for performance metrices was computed through performing 200 iterations of bootstrapping. As compared with the previous results (label adjusted for guidelines) shown in 52.We performed additional validation of the trained model on the larger, silver standard cohort  derived from the OMOP database, where about 46.9% of patients were recommended to receive WES/WGS. Across all calendar years, we perceived an overall AUROC of 0.718 and AUPRC of 0.671. Compared with the curated genetic testing cohort, both performance metrics exhibited a reduction of more than 10% on the EHR phenotyping-based cohort. As our model was trained based on the labels assigned according to the current guideline, we observed a discernible trend of performance improvement as the time approached more recent dates, with the AUROC increasing from 0.684 in 2012 to 0.76 in 2020 . The droSupplemental Table 4 and Supplemental Table 5 for the complete list).The feature importance of phenotypic abnormalities under both HPO ontologies and phecodes were computed using Gini impurity criteria. The top 15 important features were visualized in 33. Although not shown in our results, by adopting a similar approach and applying it to our cohort, we achieved nearly identical performance with an AUROC and AUPRC of 0.97 and 0.97, respectively. With molecular genetic testing becoming the gold standard for genetic diagnosis, our study further advances the genetic test ordering process by providing additional recommendations to ordering physicians for selection of appropriate molecular genetic/genomic tests. Thus, our findings enhance a future workflow towards practically using the phenotypic features to more systematically diagnose rare genetic disorders with decision support to providers. Universal decision support could help decrease disparities to obtaining a genetic diagnosis.Individuals with rare diseases often encounter prolonged diagnostic delays, highlighting a need to optimize the genetic test ordering process to ensure timely diagnosis and effective disease management. A previous study has showed that shared phenotypes among patient groups can be leveraged to identify individuals likely to benefit from CMA testing with relatively high accuracy54 have explored the extraction of clinical phenotypes from clinical narratives for genetic data analysis, our study did not exhibit any noticeable performance improvement when using phenotypes extracted from clinical notes. Additionally, we also explored the use of phenotypes based on another popular ontology, HPO, by extracting HPO terms from the notes, as described in a previous study29, and we found no statistical improvement when using either high-level HPO phenotypic abnormality or fine-grained HPO terms. While this lack of improvement may be due to the simplicity of our phenotype extraction algorithm , another possible explanation is that the information necessary for predicting genetic test ordering is already effectively summarized in high-level structured data. For example, we showed phenotypes involved with nervous system, growth abnormality, limb abnormality, genitourinary system abnormality can contribute to the decision to order WGS/W, and these systemic abnormalities can be readily inferred from the ICD codes. Furthermore, our recommendation aligned with established clinical guidelines, such as recommending WES/WGS for conditions like congenital anomalies, developmental delay, intellectual disability, neurological developmental disability, all of which are likely to be clearly summarized within the structure data.The model achieved its highest performance when utilizing features derived from structured billing codes (converted to Phecodes) along with basic demographic information. While various studies55. Recent studies56 have explored leveraging existing ontologies along with pre-trained transformer models to extract phenotypes from text. Given the recent advancement of large language models (LLM) and its potential in diverse NLP tasks with few-shots of training57, researchers have58 demonstrated the utilization of a GPT-based model to enhance the detection of phenotype terms from clinical notes. However, we noticed many phenotypes cannot be directly represented by HPO vocabulary, and given the capability of LLM in comprehending narratives, it prompts a new question - do we still need to extract those intermediate-level concepts to help with downstream predictions? While further research is required to explore the potential use of a fine-tuned LLM for directly conducting these prediction tasks, it is important to note the fine-tuning or training process may involve sensitive data, and there is a risk that the model could inadvertently retain information from the training context and disclose it in a subsequent prediction task59.Extracting comprehensive lists of phenotypes from narrative notes remains a challenging task, often demanding substantial manual annotation by domain experts to construct suitable extraction algorithms60, a time when both genomic testing and supporting evidence were limited61. As more evidence emerged and clinical guidelines evolved, an increasing number of physicians began to incorporate WES/WGS into their diagnostic assessments. Our model, specifically trained and adjusted for the latest clinical guidelines, effectively reflected this temporal trend – exhibiting improved performance in more recent cohorts. This underscores its potential as a component of a learning health system62, capable of direct training using the EHR data, facilitating the dynamic updating of clinical guidelines, and consequently offering evidence-based decision support to healthcare providers. Moreover, while WES/WGS has been adopted for newborn screening63, the limited phenotypes present at birth has posed challenges for the WES/WGS data analysis at prenatal or neonatal stages64. It has been showcased by many publications66 the benefits of re-analyzing sequencing data in the light of evolving knowledge and understanding of the molecular aspects of medical conditions. The determination of appropriate timing for re-analyzing genomic data based on individuals’ conditions is unclear67. Phen2Test is primarily designed for the selection of genomic/genetic tests based on the latest EHR data, which can also enable the identification of the “index date” – determining when the WES/WGS data (re)analysis is needed for children by analyzing their most up-to-date phenotypes documented in the EHR. In other words, our model is applicable to the selection of virtual panel and WES/WGS driven by phenotypic conditions.WES/WGS were initially introduced into the clinical settings in 201268. While the relatively simple billing-codes based feature engineering pipeline used in our study might help enhance adaptability and retraining capabilities across various systems, how to address this resource imbalance remains a significant and pressing concern.Several concerns need to be addressed before deploying this system into a routine clinical workflow. First, we observed a higher ratio of WES/WGS in both our initial curated cohort and larger phenotyping-based cohort. This may be attributed to the fact that our study was conducted within a high-resource healthcare institution  where physicians are more likely to be well-informed about and inclined to order advanced, cutting-edge tests. Hence, it is essential to acknowledge that the system’s generalizability may be limited when applied to lower-resource healthcare systems, as the training data might not accurately represent the true distribution within the intended cohort. Unfortunately, this challenge is a pervasive limitation encountered in many machine learning or AI-based approaches, where models are often trained within high-resource academic centers but are increasingly sought after in low-resource clinical settings, such as rural pediatric clinics20. While Phen2Test does not account for those external factors, the question arises hypothetically whether clinicians should always choose WES/WGS if these constraints were eliminated. Despite the fact that WES/WGS has higher yield in detecting pathogenic variants, they also are associated with incidental findings that many non-geneticists do not feel comfortable managing69. Additionally, a previous study70 has shown that clinical WES/WGS may not fully capture all exons, detect mosaicism, or detect small intragenic deletions/duplications in clinically implicated genes. This suggests that a well-covered disease focused gene panel could be a more suitable choice when the phenotypes clearly indicate an underlying genetic disease. On the other hand, payer barriers have limited the clinical use of WES for patients with suspected genetic diseases72, with reimbursement sometimes denied73. The outcomes generated by Phen2Test can provide more objective decisions and gather more data to assess the medical impact of performing specific genetic tests.Furthermore, many other factors such as financial costs and resource availability can also be relevant in the real-world decision-making processThis retrospective study develops and validates an effective phenotype-driven approach to identifying suitable molecular genetic tests for diagnosing pediatric rare disorders. The approach can potentially assist clinicians with minimum genetic tests knowledge in ordering relevant tests. More field studies are warranted to test the effectiveness of this approach prospectively in clinical settings and the generalizability to different EHR systems.


Protection of genetic diversity is important for the sustainable development of the animal industry. Due to the unique environment in Hainan Province, the local pig populations have excellent heat resistance and adaptability. Moreover, the meat quality of these pigs is excellent and very popular with the market. Analysis of the genetic composition of the population may provide valuable information for conservation and utilization of the breed. Therefore, in our study, we aimed to assess the genetic diversity and population structure of three Chinese indigenous pig populations in Hainan Province, and further identified candidate selection signatures based on whole-genome sequencing data. Our analysis shows that native Chinese pig populations are more diverse than commercial pigs. In addition, analysis of selection signatures revealed some candidate genes related to certain traits, such as stress resistance.STF) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity , adaptability (APBA1), reproduction , meat quality , and heat tolerance . Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources , the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index ( Domestication has led to the development of many new breeds of the common pig. In China, abundant pig populations have been formed, which can provide valuable germplasm resources for promoting the sustainable development of the pig industry around the world. Hainan Province, located in the most southern part of China and surrounded by the sea on all sides, has a tropical island climate characterized by warm and abundant rainfall throughout the year .The uniqHowever, decreases in annual lean meat production, poor growth rates and intense competition from exotic commercial pig breeds has led to a decline in the indigenous pig population. In recent years, although some measures and efforts have been implemented by the government to promote indigenous pig breeding, such as establishing national and provincial conservation farms for WZS, TC, DA and LG, we are still unsure if these conservation efforts have effectively protected the valuable genes expressed within the population. To date, the effect of this conservation effort has not been investigated based on molecular markers. Additionally, the population structure of these indigenous pig breeds has suffered tremendous shocks from African swine fever since 2018 . TherefoGenetic diversity is the fundamental foundation for genetics and breeding research, and it can often represent the potential of a population to adapt to change . UnderstDomestication may also leave ‘imprints’ in the sequence of the genome associated with distinct phenotypic characteristics. These imprints mainly manifest in the reduction of genetic diversity and increase in the degree of linkage disequilibrium . In receThe objective of this present study is to investigate the genetic diversity and structure of three local pig populations in China, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), based on molecular markers. Furthermore, we looked to identify potential candidate genes associated with high-quality economic characteristics known to the species . Our results will add to the growing knowledge on the genetic status and evolutionary history of these three indigenous pig populations and provide valuable insight into the genetic mechanisms of some phenotypic characters of growing interest in the changing climate such as environmental heat adaptation.A total of 30, 18 and 30 ear tissue samples of Wuzhishan (WZS), Tunchang (TC) and Dingan (DA) were collected from randomly chosen individuals . The exthttps://www.ncbi.nlm.nih.gov/sra) (The DNBSEQ platform (150 bp paired-end) was used for sequencing. The average sequenced depth was more than 15×. In addition, data from 13 individuals of Landrace (LR) and 12 individuals of Large White (LW) were acquired from the SRA database (gov/sra) . These dgov/sra) .https://www.ncbi.nlm.nih.gov/assembly/GCF_000003025.6/) (accessed on 1 December 2022) using the “MEM” algorithm of the BWA (v0.7.17) [SOAPnuke (v2.1.0) was carried out for quality control (QC) of the reads , includiv0.7.17)  alignmenv0.7.17)  for the v0.7.17)  was usedO) and expected heterozygosity (HE) [pi values were calculated to evaluate nucleotide polymorphism. The genetic diversity parameters were statistically analyzed using violin plots generated with the ggplot2 package in R. The degree of linkage disequilibrium (LD) decay was measured by taking the squared correlation (2r) of pairwise SNPs using PopLDdecay with default parameters [Genetic diversity refers to the range of alleles and genotypes that exist within the studied population . Variousity (HE) , and pi rameters .STF) is a statistical test used to measure the degree of population differentiation between groups [STF, resulting in a 5 × 5 STF matrix that was visualized by heatmap. To obtain these results, 100 kb sliding windows were used with a 10 kb step size during the STF calculation.The F-statistic (n groups . In thisMEGA software (v7.0)  was carrSTF values between the indigenous pig populations and two exotic breeds were calculated using VCFtools. The analysis was performed across genomes using 100 kb windows and 10 kb steps. To empirically identify candidate regions undergoing positive selection, we have set the top 1% of STF as the threshold to ensure data quality. To identify candidate genes in these regions, Ensembl database  (accessed on 21 March 2023) was carried out [The ried out .STF sliding windows with the pig QTL database to identify genes that may be associated with economically important traits.DAVID is a bioinformatics database which integrates biological data and analysis tools. The enrichment analysis of the candidate genes obtained in this study was performed by using DAVID with GO and KEGG pathway analyses . AdditioA total of 28,384,282 SNPs were identified in these five pig populations. In the analysis, a total of 27,134,233 SNPs were identified from three types of indigenous pigs. ANNOVAR software was used to annotate the variation information, and a chart displaying the annotation information was plotted . NotablyIn order to estimate the genetic diversity of all pigs, five indices have been carried out . We obseO) and expected heterozygosity (HE) were used to estimate the genetic diversity of every tested pig population. The LR and WZS pig populations were found to have the lowest HO and HE values among all populations, while the LW population had the highest HO (0.39) and HE (0.35). Across all indigenous pig populations, the HO values ranged from 0.29 to 0.34, while the HE values ranged from 0.31 to 0.32.Heterozygosity is a commonly used parameter to measure genetic diversity. In this study, both observed heterozygosity . By screening candidate genes using a 1% STF value for statistics , we found a total of 359 genes (p < 0.05)  with commercial pig breeds (LR and LW) at the population level. We compared evidence of selection using 59 genes , shown i < 0.05) . Among t < 0.05) A–D.A number of cellular components of metabolism were found to be upregulated in the GO analysis, including nucleus activity, myosin phosphatase activity, adaptive immune response, etc. The most significant GO term was associated with energy . In our KEGG pathway analysis, we discovered numerous signaling pathways that are potentially related to immune and metabolic processes. These findings may be associated with the excellent disease resistance and adaptability observed in Chinese indigenous pigs. As we delved further into our analysis, we compared the results with those of the Pig QTLdb database . Through this comparison, a number of QTLs related to meat quality and health were identified , which mE, HO, pi, LD and STF, showed a significant level of genetic diversity in indigenous pigs. The significant artificial selection undergone by Western pig breeds over the past few decades has led to decreased genetic diversity in commercial pigs. These results are consistent with recent research. Wang et al. (2015), for example, reported that Chinese indigenous pigs exhibit greater genetic variability than commercial pigs [Chinese native pigs are ancient compared to commercial pig breeds. Unlike Western pigs, Chinese pigs have not been subjected to the same level of intense selection pressure throughout history. As a consequence, Chinese pigs exhibit higher levels of genetic diversity compared to commercial pig breeds. In this study, a comprehensive analysis of the genetic diversity of indigenous pigs was conducted by investigating various factors that affect it. The results of the genetic diversity parameters used in this study, such as MAF, Hial pigs . OverallO and HE between indigenous pigs and commercial pig breeds in this study, it was found that commercial pig breeds exhibit a higher value of HE. This inconsistency may be the reason of the SNP detection bias or the relatively small effective population size of indigenous pigs, coupled with limited sample representativeness, which has also been observed in many similar studies [O values of 0.14 and 0.15, respectively) [In comparing the H studies ,31,32,33ctively) , our resSTF is the common measure to estimate population genetic differentiation [STF values between commercial pigs and native Chinese pigs, potentially due to geographic isolation. Therefore, in order to further investigate the differences between WZS and Hainan pigs, it is necessary to conduct further analysis of the population structure of Hainan local pigs.Pairwise Wright’s ntiation . Based oSTF values calculated between the two regions; this suggests that there are significant differences between local pigs and commercial pigs, possibly due to long-term genetic divergence and varying degrees of selection, as well as geographic isolation. It is worth noting that when K = 3 in structural analysis, TC and DA exhibit relatively similar characteristics, while WZS belongs to another group. Geographic isolation may also be responsible. The WZS pigs originated near the distant Wuzhishan City, whereas Tunchang City and Ding’an City are adjacent to each other—promoting crossbreeding between the TC and DA pigs. The mountainous terrain may have caused the WZS pig population to become isolated and independent from other populations. The distance between Tunchang and Ding’an is only 70 km. Considering that populations located in close geographic proximity are more likely to share common ancestors and mate with each other, locals believe that the DA pig may be the ancestor of the TC pig, and both populations are black pigs and have a similar appearance. This observation suggests that a closer genetic relationship between the two populations is possible, a hypothesis that has been supported by previous genetic studies. Based on these findings, it is clear that a more detailed analysis of the genetic and phenotypic traits of TC and DA is required in order to fully understand their evolutionary history and potential for economic development.We performed genetic population structure analysis on five pig populations, and the patterns of the sub-clustering performed consistently among the analyses of NJ tree, PCA and STRUCTURE. The NJ tree displayed a clear bifurcation, similarly evident in the PCA plot. When K = 2 in the STRUCTURE analysis, we observed a marked division between local and commercial pigs in Hainan, consistent with the We analyzed three KEGG pathways, two of which are associated with intestinal immunity, while the other is linked to cholesterol metabolism. The pathways associated with intestinal immunity could involve genes or proteins related to inflammatory response, immune regulation and intestinal mucosal barrier, all of which play crucial roles in the production and immune response of IgA in the intestinal immune system.Furthermore, the enrichment of the ssc04979, cholesterol metabolism pathway, may indicate that cholesterol metabolism plays a critical role in the physiological processes of indigenous pigs. This pathway involves multiple genes and molecules, including cholesterol synthesis and transport, bile acid synthesis and lipoprotein metabolism. The enrichment analysis of this pathway enables us to better comprehend the regulatory mechanism of cholesterol metabolism in Chinese indigenous pigs, providing us with new ideas and targets for improving the quality of pork and enhancing breeding efficiency.IL-2, IL-21 and ZFYVE16. Prior studies have reported IL-2 as a growth factor that can drive the amplification of activated T cell populations in humans. IL-21 is one of the primary immune modulators that regulates various immune responses by impacting different immune cells. T cells, as immune cells, play a crucial role in protecting the body against pathogens [We also identified several immune-related genes, such as athogens ,36.IL-13 belongs to the Th9 cytokine family and has significant immune regulatory activity that affects various immune cells. IL-17A promotes the production of corresponding myeloid cells by recruiting pathogenic T cells and plays an initiating role in autoimmunity [ZFYVE16 may upregulate the proliferation of B lymphocytes through TGF-β signal transduction [Additionally, immunity . Moreovesduction . The comAPBA1. Previous research has linked APBA1 to cognitive function, and its absence has been associated with the onset of neurobehavioral disorders [As there are survival differences between commercial pigs and local pigs, we have identified genes that may be related to adaptability, such as isorders ,40. ThisFGF2 gene mediates FGF2/FGFR signal transduction, which can promote in vitro maturation of cumulus-oocyte complexes. RNF17 is a component of mammalian germ cells and is critical for sperm development [ADAD1 is a protein necessary for male fertility that contains the testis-specific adenosine deaminase domain. Additionally, HIPK4 is essential for mouse sperm development [We have also identified several genes related to reproduction. The elopment ,42. ADADelopment ,44. It iABCA1, ADIG, TLE4 and IRX5, have been found to be associated with lipid metabolism and fat deposition. Intramuscular fat (IMF) is considered a major factor that affects meat quality. Often, genes related to lipid metabolism, such as ABCA1, are downregulated in commercial meats, such as Wenchang chicken [TLE4 may regulate the quiescence of muscle stem cells and influence skeletal muscle differentiation. IRX5 promotes the generation of adipose tissue in hMSCs by inhibiting glycolysis, which may further explain the delicious taste associated with the indigenous pigs [Moreover, some candidate genes, such as  chicken . The sel chicken ,47. TLE4ous pigs ,49. FurtVPS13A, which encodes the protein Chorein, may play a significant role in heat stress resistance. Moreover, Schmidt et al. (2013) identified VPS13A as a critical regulator of secretion and blood platelet aggregation during heat stress conditions. This emphasizes the importance of comprehending the molecular mechanisms that underlie the response to heat stress in different biological scenarios at both cellular and organismal levels [HSPA4), a member of the HSP110 family, can control apoptosis of inflammatory cells and immune reactions in the gut [Hsp110 in primary cardiomyocytes in vitro was sensitive to heat stress, and Hsp110 participated in the potential acquisition of heat tolerance after heat stress [It is worth noting that Asadollahi et al. (2022) suggested that l levels ,51. In a the gut . In Liu t stress . TherefoSeveral QTLs have been identified that are linked to meat quality traits, including drip loss, loin muscle area, muscle moisture percentage and shoulder subcutaneous fat thickness. Chinese indigenous pigs are renowned for their exceptional meat quality, and the overlapping of QTLs with potential selection regions may provide an explanation for the genetic differences observed between Chinese indigenous pig populations and commercial pig breeds. This information could potentially aid in the development of breeding programs that aim to improve the meat quality of commercial pig breeds.In summary, these findings are of great significance for understanding the unique characteristics of Chinese local pig populations, which will be beneficial for our understanding of Chinese local pigs as a whole. Moreover, the insights gained from this study provide a foundation for proposing a reliable and sustainable strategy for the conservation and improvement of indigenous pigs. This information can be utilized to optimize breeding programs, enhance genetic diversity and promote the preservation of local pig populations, ultimately contributing to the overall biodiversity of livestock species.Knowledge of the genetic diversity and population structure for one population may provide valuable information for its effective conservation and utilization. We found that the indigenous pigs tested in this study are much more genetically diverse than commercial pigs. Furthermore, we identified pathways and genes related to meat quality, immunity and adaptability in the indigenous pigs—valuable genetic resources that must be conserved for years to come. Overall, we conclude that conservation of indigenous pigs has been effective to date, but their population protection plan still needs further optimization to avoid inbreeding depression and to maintain sufficient genetic diversity.