Redefining and interpreting genomic relationships of metafounders.

Metafounders are a useful concept to characterize relationships within and across populations, and to help genetic evaluations because they help modelling the means and variances of unknown base population animals. Current definitions of metafounder relationships are sensitive to the choice of reference alleles and have not been compared to their counterparts in population genetics-namely, heterozygosities, FST coefficients, and genetic distances. We redefine the relationships across populations with an arbitrary base of a maximum heterozygosity population in Hardy-Weinberg equilibrium. Then, the relationship between or within populations is a cross-product of the form Γ b , b ' = 2 n 2 p b - 1 2 p b ' - 1 ' with p being vectors of allele frequencies at n markers in populations b and b ' . This is simply the genomic relationship of two pseudo-individuals whose genotypes are equal to twice the allele frequencies. We also show that this coding is invariant to the choice of reference alleles. In addition, standard population genetics metrics (inbreeding coefficients of various forms; FST differentiation coefficients; segregation variance; and Nei's genetic distance) can be obtained from elements of matrix Γ .

Detecting inbreeding depression in structured populations.

Measuring inbreeding and its consequences on fitness is central for many areas in biology including human genetics and the conservation of endangered species. However, there is no consensus on the best method, neither for quantification of inbreeding itself nor for the model to estimate its effect on specific traits. We simulated traits based on simulated genomes from a large pedigree and empirical whole-genome sequences of human data from populations with various sizes and structures (from the 1,000 Genomes project). We compare the ability of various inbreeding coefficients ([Formula: see text]) to quantify the strength of inbreeding depression: allele-sharing, two versions of the correlation of uniting gametes which differ in the weight they attribute to each locus and two identical-by-descent segments-based estimators. We also compare two models: the standard linear model and a linear mixed model (LMM) including a genetic relatedness matrix (GRM) as random effect to account for the nonindependence of observations. We find LMMs give better results in scenarios with population or family structure. Within the LMM, we compare three different GRMs and show that in homogeneous populations, there is little difference among the different [Formula: see text] and GRM for inbreeding depression quantification. However, as soon as a strong population or family structure is present, the strength of inbreeding depression can be most efficiently estimated only if i) the phenotypes are regressed on [Formula: see text] based on a weighted version of the correlation of uniting gametes, giving more weight to common alleles and ii) with the GRM obtained from an allele-sharing relatedness estimator.

Kernel density estimation of allele frequency including undetected alleles.

Whereas undetected species contribute to estimation of species diversity, undetected alleles have not been used to estimated genetic diversity. Although random sampling guarantees unbiased estimation of allele frequency and genetic diversity measures, using undetected alleles may provide biased but more precise estimators useful for conservation. We newly devised kernel density estimation (KDE) for allele frequency including undetected alleles and tested it in estimation of allele frequency and nucleotide diversity using population generated by coalescent simulation as well as well as real population data. Contrary to expectations, nucleotide diversity estimated by KDE had worse bias and accuracy. Allele frequency estimated by KDE was also worse except when the sample size was small. These might be due to finity of population and/or the curse of dimensionality. In conclusion, KDE of allele frequency does not contribute to genetic diversity estimation.

Applicability of SCoT markers in unraveling genetic variation and population structure among sugar beet (Beta vulgaris L.) germplasm.

BACKGROUND: Sugar beet (Beta vulgaris L.) holds significant importance as a crop globally cultivated for sugar production. The genetic diversity present in sugar beet accessions plays a crucial role in crop improvement programs. METHODS AND RESULTS: During the present study, we collected 96 sugar beet accessions from different regions and extracted DNA from their leaves. Genomic DNA was amplified using SCoT primers, and the resulting fragments were separated by gel electrophoresis. The data were analyzed using various genetic diversity indices, and constructed a population STRUCTURE, applied the unweighted pair-group method with arithmetic mean (UPGMA), and conducted Principle Coordinate Analysis (PCoA). The results revealed a high level of genetic diversity among the sugar beet accessions, with 265 bands produced by the 10 SCoT primers used. The percentage of polymorphic bands was 97.60%, indicating substantial genetic variation. The study uncovered significant genetic variation, leading to higher values for overall gene diversity (0.21), genetic distance (0.517), number of effective alleles (1.36), Shannon's information index (0.33), and polymorphism information contents (0.239). The analysis of molecular variance suggested a considerable amount of genetic variation, with 89% existing within the population. Using STRUCTURE and UPGMA analysis, the sugar beet germplasm was divided into two major populations. Structure analysis partitioned the germplasm based on the origin and domestication history of sugar beet, resulting in neighboring countries clustering together. CONCLUSION: The utilization of SCoT markers unveiled a noteworthy degree of genetic variation within the sugar beet germplasm in this study. These findings can be used in future breeding programs with the objective of enhancing both sugar beet yield and quality.

Biogeographical Ancestry Analyses Using the ForenSeqTM DNA Signature Prep Kit and Multiple Prediction Tools.

The inference of biogeographical ancestry (BGA) can assist in police investigations of serious crime cases and help to identify missing people and victims of mass disasters. In this study, we evaluated the typing performance of 56 ancestry-informative SNPs in 177 samples using the ForenSeq™ DNA Signature Prep Kit on the MiSeq FGx system. Furthermore, we compared the prediction accuracy of the tools Universal Analysis Software v1.2 (UAS), the FROG-kb, and GenoGeographer when inferring the ancestry of 503 Europeans, 22 non-Europeans, and 5 individuals with co-ancestry. The kit was highly sensitive with complete aiSNP profiles in samples with as low as 250pg input DNA. However, in line with others, we observed low read depth and occasional drop-out in some SNPs. Therefore, we suggest not using less than the recommended 1ng of input DNA. FROG-kb and GenoGeographer accurately predicted both Europeans (99.6% and 91.8% correct, respectively) and non-Europeans (95.4% and 90.9% correct, respectively). The UAS was highly accurate when predicting Europeans (96.0% correct) but performed poorer when predicting non-Europeans (40.9% correct). None of the tools were able to correctly predict individuals with co-ancestry. Our study demonstrates that the use of multiple prediction tools will increase the prediction accuracy of BGA inference in forensic casework.

Correlation-based tests for the formal comparison of polygenic scores in multiple populations.

Polygenic scores (PGS) are measures of genetic risk, derived from the results of genome wide association studies (GWAS). Previous work has proposed the coefficient of determination (R2) as an appropriate measure by which to compare PGS performance in a validation dataset. Here we propose correlation-based methods for evaluating PGS performance by adapting previous work which produced a statistical framework and robust test statistics for the comparison of multiple correlation measures in multiple populations. This flexible framework can be extended to a wider variety of hypothesis tests than currently available methods. We assess our proposed method in simulation and demonstrate its utility with two examples, assessing previously developed PGS for low-density lipoprotein cholesterol and height in multiple populations in the All of Us cohort. Finally, we provide an R package 'coranova' with both parametric and nonparametric implementations of the described methods.

Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories.

Bayesian coalescent skyline plot models are widely used to infer demographic histories. The first (non-Bayesian) coalescent skyline plot model assumed a known genealogy as data, while subsequent models and implementations jointly inferred the genealogy and demographic history from sequence data, including heterochronous samples. Overall, there exist multiple different Bayesian coalescent skyline plot models which mainly differ in two key aspects: (i) how changes in population size are modeled through independent or autocorrelated prior distributions, and (ii) how many change-points in the demographic history are used, where they occur and if the number is pre-specified or inferred. The specific impact of each of these choices on the inferred demographic history is not known because of two reasons: first, not all models are implemented in the same software, and second, each model implementation makes specific choices that the biologist cannot influence. To facilitate a detailed evaluation of Bayesian coalescent skyline plot models, we implemented all currently described models in a flexible design into the software RevBayes. Furthermore, we evaluated models and choices on an empirical dataset of horses supplemented by a small simulation study. We find that estimated demographic histories can be grouped broadly into two groups depending on how change-points in the demographic history are specified (either independent of or at coalescent events). Our simulations suggest that models using change-points at coalescent events produce spurious variation near the present, while most models using independent change-points tend to over-smooth the inferred demographic history.

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

A novel 193-plex MPS panel integrating STRs and SNPs highlights the application value of forensic genetics in individual identification and paternity testing.

Massively parallel sequencing (MPS) has emerged as a promising technology for targeting multiple genetic loci simultaneously in forensic genetics. Here, a novel 193-plex panel was designed to target 28 A-STRs, 41 Y-STRs, 21 X-STRs, 3 sex-identified loci, and 100 A-SNPs by employing a single-end 400 bp sequencing strategy on the MGISEQ-2000™ platform. In the present study, a series of validations and sequencing of 1642 population samples were performed to evaluate the overall performance of the MPS-based panel and its practicality in forensic application according to the SWGDAM guidelines. In general, the 193-plex markers in our panel showed good performance in terms of species specificity, stability, and repeatability. Compared to commercial kits, this panel achieved 100% concordance for standard gDNA and 99.87% concordance for 14,560 population genotypes. Moreover, this panel detected 100% of the loci from 0.5 ng of DNA template and all unique alleles at a 1:4 DNA mixture ratio (0.2 ng minor contributor), and the applicability of the proposed approach for tracing and degrading DNA was further supported by case samples. In addition, several forensic parameters of STRs and SNPs were calculated in a population study. High CPE and CPD values greater than 0.9999999 were clearly demonstrated and these results could be useful references for the application of this panel in individual identification and paternity testing. Overall, this 193-plex MPS panel has been shown to be a reliable, repeatable, robust, inexpensive, and powerful tool sufficient for forensic practice.

Genetic diversity and forensic statistical support for the 12 X-STR markers in the Malaysian Indian population using Qiagen Investigator® Argus X-12 QS kit.

X-chromosome short tandem repeats (X-STRs) are useful for human identification, especially in complex kinship scenarios. Since forensic statistical parameters vary among populations and the X-STRs population data for the diverse population of Peninsular Malaysia's are unavailable, this attempt for Indians (n = 201) appears forensically relevant to support the 12 X-STRs markers' evidential value for human identification in Malaysia. The Qiagen Investigator® Argus X-12 QS kit showed that DXS10135 was the most polymorphic locus with high genetic diversity, polymorphism information richness, heterozygosity, and exclusion power. Based on allele frequencies, the strength of discrimination and mean exclusion chance (MECKrüger, MECKishida, MECDesmarais, and MECDesmaraisDuo) values for the Malaysian Indians were ≥0.999997790686228. As for haplotype frequencies, the overall discrimination power and mean exclusion probability (MECKrüger, MECKishida, MECDesmarais, and MECDesmaraisDuo) were ≥0.9999984801951. The genetic distance, neighbor-joining phylogenetic tree, and principal component analysis also supported the evidential value of the 12 X-STRs markers for forensic practical caseworks in Malaysia.

FSTest: an efficient tool for cross-population fixation index estimation on variant call format files.

Fixation index (Fst) statistics provide critical insights into evolutionary processes affecting the structure of genetic variation within and among populations. Fst statistics have been widely applied in population and evolutionary genetics to identify genomic regions targeted by selection pressures. The FSTest 1.3 software was developed to estimate four Fst statistics of Hudson, Weir and Cockerham, Nei, and Wright using high-throughput genotyping or sequencing data. Here, we introduced FSTest 1.3 and compared its performance with two widely used software VCFtools 0.1.16 and PLINK 2.0. Chromosome 1 of 1000 Genomes Phase III variant data belonging to South Asian (n = 211) and African (n = 274) populations were included as an example case in this study. Different Fst estimates were calculated for each single-nucleotide polymorphism (SNP) in a pairwise comparison of South Asian against African populations, and the results of FSTest 1.3 were confirmed by VCFtools 0.1.16 and PLINK 2.0. Two different sliding window approaches, one based on a fixed number of SNPs and another based on a fixed number of base pair (bp) were conducted using FSTest 1.3 and VCFtools 0.1.16. Our results showed that regions with low coverage genotypic data could lead to an overestimation of Fst in sliding window analysis using a fixed number of bp. FSTest 1.3 could mitigate this challenge by estimating the average of consecutive SNPs along the chromosome. FSTest 1.3 allows direct analysis of VCF files with a small amount of code and can calculate Fst estimates on a desktop computer for more than a million SNPs in a few minutes. FSTest 1.3 is freely available at https://github.com/similab/FSTest.

Reconstructing complex admixture history using a hierarchical model.

Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.

Massively parallel sequencing data of 31 autosomal STR loci obtained using the Precision ID GlobalFiler NGS STR Panel v2 for 82 Japanese population samples.

Allele frequencies for 31 autosomal short tandem repeat (STR) loci (CSF1PO, D10S1248, D12ATA63, D12S391, D13S317, D14S1434, D16S539, D18S51, D19S433, D1S1656, D1S1677, D21S11, D22S1045, D2S1338, D2S1776, D2S441, D3S1358, D3S4529, D4S2408, D5S2800, D5S818, D6S1043, D6S474, D7S820, D8S1179, FGA, Penta D, Penta E, TH01, TPOX, and vWA) were obtained using Precision ID GlobalFiler NGS STR Panel v2 from 82 unrelated individuals sampled from the Japanese population. Autosomal STR alleles designated by NGS and conventional capillary electrophoresis were found to be concordant except at D2S441 allele 9.

Properties of 2-locus genealogies and linkage disequilibrium in temporally structured samples.

Archeogenetics has been revolutionary, revealing insights into demographic history and recent positive selection. However, most studies to date have ignored the nonrandom association of genetic variants at different loci (i.e. linkage disequilibrium). This may be in part because basic properties of linkage disequilibrium in samples from different times are still not well understood. Here, we derive several results for summary statistics of haplotypic variation under a model with time-stratified sampling: (1) The correlation between the number of pairwise differences observed between time-staggered samples (πΔt) in models with and without strict population continuity; (2) The product of the linkage disequilibrium coefficient, D, between ancient and modern samples, which is a measure of haplotypic similarity between modern and ancient samples; and (3) The expected switch rate in the Li and Stephens haplotype copying model. The latter has implications for genotype imputation and phasing in ancient samples with modern reference panels. Overall, these results provide a characterization of how haplotype patterns are affected by sample age, recombination rates, and population sizes. We expect these results will help guide the interpretation and analysis of haplotype data from ancient and modern samples.

Post-Austronesian migrational wave of West Polynesians to Micronesia.

This study examines Y-chromosome and mtDNA markers in the population of the island of Kiritimati in the context of geographically targeted reference populations from the Pacific. Kiritimati derives its population from the atoll islands of the Gilbert Archipelago and representsa geographicaltransitional region between Micronesia, Polynesia and Melanesia that likely played a critical role during theAustronesian expansion. The large presence(84.1%)of individuals withO-M175, O2a-M324 and O2a2b-P164 sub-haplogroups, 69.9% being O2a2b-P164, the Y-STR homogeneity within O2a2b-P164 and the very recent age of the sub-haplogroup(363-548 years ago)inKiritimati suggestthe arrival ofa genetically homogenous population to the Gilberteses followed by a population expassion.The close Y-STR haplotype affinities with profiles from the Samoa and Tonga Archipelagos point to an unprecedented massive post-Austronesian expansionexodus from West Polynesia.Contrasting the abundance of AustronesianO2a2b-P164 sub-haplogroup, the most abundantMelanesian/Papuansub-haplogroup,C-M130is present at a frequency of 13.5%. Thenetwork topology suggests that C-M130 arrived to theKiribati Archipelago from West Polynesia, specifically from West Samoa, Tonga and/or Tutuila subsequent to the Austronesian expansion about 832-1408 years ago. The haplotype affinities withinO2a2b-P164 argue for anoriginal source in Taiwan and its dispersal to West Polynesia and then to Southeast Micronesia. The present investigation provides an understanding of the genetic composition and complex migration history of an understudied region of the Pacific and provides evidence for recent dispersals towards Micronesia from West Polynesia subsequent to the initial Austronesian expansion.

Population-level deficit of homozygosity unveils CPSF3 as an intellectual disability syndrome gene.

Predicting the pathogenicity of biallelic missense variants can be challenging. Here, we use a deficit of observed homozygous carriers of missense variants, versus an expected number in a set of 153,054 chip-genotyped Icelanders, to identify potentially pathogenic genotypes. We follow three missense variants with a complete deficit of homozygosity and find that their pathogenic effect in homozygous state ranges from severe childhood disease to early embryonic lethality. One of these variants is in CPSF3, a gene not previously linked to disease. From a set of clinically sequenced Icelanders, and by sequencing archival samples targeted through the Icelandic genealogy, we find four homozygous carriers. Additionally, we find two homozygous carriers of Mexican descent of another missense variant in CPSF3. All six homozygous carriers of missense variants in CPSF3 show severe intellectual disability, seizures, microcephaly, and abnormal muscle tone. Here, we show how the absence of certain homozygous genotypes from a large population set can elucidate causes of previously unexplained recessive diseases and early miscarriage.

Indian genetic heritage in Southeast Asian populations.

The great ethnolinguistic diversity found today in mainland Southeast Asia (MSEA) reflects multiple migration waves of people in the past. Maritime trading between MSEA and India was established at the latest 300 BCE, and the formation of early states in Southeast Asia during the first millennium CE was strongly influenced by Indian culture, a cultural influence that is still prominent today. Several ancient Indian-influenced states were located in present-day Thailand, and various populations in the country are likely to be descendants of people from those states. To systematically explore Indian genetic heritage in MSEA populations, we generated genome-wide SNP data (using the Affymetrix Human Origins array) for 119 present-day individuals belonging to 10 ethnic groups from Thailand and co-analyzed them with published data using PCA, ADMIXTURE, and methods relying on f-statistics and on autosomal haplotypes. We found low levels of South Asian admixture in various MSEA populations for whom there is evidence of historical connections with the ancient Indian-influenced states but failed to find this genetic component in present-day hunter-gatherer groups and relatively isolated groups from the highlands of Northern Thailand. The results suggest that migration of Indian populations to MSEA may have been responsible for the spread of Indian culture in the region. Our results also support close genetic affinity between Kra-Dai-speaking (also known as Tai-Kadai) and Austronesian-speaking populations, which fits a linguistic hypothesis suggesting cladality of the two language families.

Assortative mating biases marker-based heritability estimators.

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% - 23% higher than corrected estimates using UK Biobank data.

Forecasting emergence of COVID-19 variants of concern.

We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstrate that fitness variation, defined as a trait for which an increase in its value is associated with an increase in net Darwinian fitness if the value of other traits are held constant, is a strong indicator of imminent transition in the viral population.

Morphologic, genetic, and biogeographic continua among subspecies hinder the conservation of threatened taxa: the case of Centaurea aspera ssp. scorpiurifolia (Asteraceae).

Subspecies are widely included as conservation units because of their potential to become new species. However, their practical recognition includes variable criteria, such as morphological, genetic, geographic and other differences. Centaurea aspera ssp. scorpiurifolia is a threatened taxon endemic to Andalusia (Spain), which coexists in most of its distribution area with similar taxa. Because of the difficulty to identify it using morphology alone, we aimed to sample all the populations cited as ssp. scorpiurifolia as exhaustively as possible, morphologically characterise them, and analyse their genetic structuring using microsatellites, to better understand difficulties when conserving subspecies. Three different Centaurea species were found which were easily identified. Within C. aspera, two genetic populations and some admixed individuals were observed, one including ssp. scorpiurifolia individuals and the other including individuals identified as subspecies aspera, stenophylla, and scorpiurifolia. A morphological continuum between these two genetic populations and a wide overlapping of their biogeographic distribution were also found. This continuum can affect the conservation of ssp. scorpiurifolia because of potential misidentifications and harmful effects of subspecific hybridization. Misidentifications could be partly overcome by using as many different traits as possible, and conservation priority should be given to populations representative of the ends of this continuum.