Testing Times: Challenges in Disentangling Admixture Histories in Recent and Complex Demographies.

Paleogenomics has expanded our knowledge of human evolutionary history. Since the 2020s, the study of ancient DNA has increased its focus on reconstructing the recent past. However, the accuracy of paleogenomic methods in answering questions of historical and archaeological importance amidst the increased demographic complexity and decreased genetic differentiation within the historical period remains an open question. We used two simulation approaches to evaluate the limitations and behavior of commonly used methods, qpAdm and the f3-statistic, on admixture inference. The first is based on branch-length data simulated from four simple demographic models of varying complexities and configurations. The second, an analysis of Eurasian history composed of 59 populations using whole-genome data modified with ancient DNA conditions such as SNP ascertainment, data missingness, and pseudo-haploidization. We show that under conditions resembling historical populations, qpAdm can identify a small candidate set of true sources and populations closely related to them. However, in typical ancient DNA conditions, qpAdm is unable to further distinguish between them, limiting its utility for resolving fine-scaled hypotheses. Notably, we find that complex gene-flow histories generally lead to improvements in the performance of qpAdm and observe no bias in the estimation of admixture weights. We offer a heuristic for admixture inference that incorporates admixture weight estimate and P-values of qpAdm models, and f3-statistics to enhance the power to distinguish between multiple plausible candidates. Finally, we highlight the future potential of qpAdm through whole-genome branch-length f2-statistics, demonstrating the improved demographic inference that could be achieved with advancements in f-statistic estimations.

Role of meteorological parameters with the spread of Covid-19 in Pakistan: application of autoregressive distributed lag approach.

This research focuses on the impacts of different meteorological parameters (temperature, humidity, rainfall, and evapotranspiration) on the transmission of Covid-19 in the administrative regions and provinces of Pakistan, i.e., Azad Jammu and Kashmir, Gilgit Baltistan, Khyber Pakhtunkhwa, Islamabad, Punjab, Sindh, and Balochistan from June 10, 2020, to August 31, 2021. This study analyzes the relation between Covid-19-confirmed cases and the meteorological parameters with the help of the autoregressive distributed lag model. In this research, additional tools (t-statistics, f-statistics, and time series analysis) are used for the motive of examining the linear relationship, the productivity of the model, and for the significant association between dependent and independent variables, lnccc and lnevp, lnhum, lnrain, lntemp, respectively. Values of t-statistics and f-statistics reveal that variables have a connection and individual significance for the model exist. Time series display that the Covid-19 spread increased from June 10, 2020, to August 31, 2021, in Pakistan. Temperature positively influenced the Covid-19-confirmed cases in all provinces of Pakistan in the long run. Evapotranspiration and rainfall influenced positively, while specific humidity influenced negatively on the confirmed Covid-19 cases in Azad Jammu Kashmir, Khyber Pakhtunkhwa, and Punjab. Specific humidity had a positive impact, while evapotranspiration and rainfall had the negative impact on the Covid-19-confirmed cases in Sindh and Balochistan. Evapotranspiration and specific humidity influenced positively, while rainfall influenced the Covid-19-confirmed cases negatively in Gilgit Baltistan. Evapotranspiration influenced positively, while specific humidity and rainfall influenced negatively on the Covid-19-confirmed cases in Islamabad. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-023-04997-4.

On the limits of fitting complex models of population history to f-statistics.

Our understanding of population history in deep time has been assisted by fitting admixture graphs (AGs) to data: models that specify the ordering of population splits and mixtures, which along with the amount of genetic drift and the proportions of mixture, is the only information needed to predict the patterns of allele frequency correlation among populations. The space of possible AGs relating populations is vast, and thus most published studies have identified fitting AGs through a manual process driven by prior hypotheses, leaving the majority of alternative models unexplored. Here, we develop a method for systematically searching the space of all AGs that can incorporate non-genetic information in the form of topology constraints. We implement this findGraphs tool within a software package, ADMIXTOOLS 2, which is a reimplementation of the ADMIXTOOLS software with new features and large performance gains. We apply this methodology to identify alternative models to AGs that played key roles in eight publications and find that in nearly all cases many alternative models fit nominally or significantly better than the published one. Our results suggest that strong claims about population history from AGs should only be made when all well-fitting and temporally plausible models share common topological features. Our re-evaluation of published data also provides insight into the population histories of humans, dogs, and horses, identifying features that are stable across the models we explored, as well as scenarios of populations relationships that differ in important ways from models that have been highlighted in the literature.

Estimation of microsatellite-based autozygosity and its correlation with pedigree inbreeding coefficient in crossbred cattle.

In countries where farming is largely subsistence, no pedigree records of farm animals are maintained at farmers' herd and scientific mating plans are not observed which leads to the accumulation of inbreeding and loss of production potential. Microsatellites have been widely used as reliable molecular markers to measure inbreeding. We attempted to correlate autozygosity estimated from microsatellite data with the inbreeding coefficient (F) calculated from pedigree data in Vrindavani crossbred cattle developed in India. The inbreeding coefficient was calculated from the pedigree of ninety-six Vrindavani cattle. Animals were further grouped into three groups viz. acceptable/low (F: 0-5%), moderate (F: 5-10%) and high (F: ≥10%), based on their inbreeding coefficients. The overall mean of the inbreeding coefficient was found to be 0.070 ± 0.007. A panel of twenty-five bovine-specific loci were chosen for the study according to ISAG/FAO. The mean FIS, FST, and FIT values were 0.0548 ± 0.025, 0.012 ± 0.001 and 0.0417 ± 0.025, respectively. There was no significant correlation between the FIS values obtained and the pedigree F values. The locus-wise individual autozygosity was estimated using the method-of-moments estimator (MME) formula for locus-specific autozygosity. The autozygosities ascribing to CSSM66 and TGLA53 were found to be significantly (p < .01 and p < .05, respectively) correlated with pedigree F values.

Exploring genetic diversity of wild and related tetraploid wheat species Triticum turgidum and Triticum timopheevii.

INTRODUCTION: The domestication bottleneck has reduced genetic diversity inwheat, necessitating the use of wild relatives in breeding programs. Wild tetraploid wheat are widely used in the breeding programs but with morphological characters, it is difficult to distinguish these, resulting in misclassification/mislabeling or duplication of accessions in the Gene bank. OBJECTIVES: The study aims to exploreGenotyping by sequencing (GBS) to characterize wild and domesticated tetraploid wheat accessions to generate a core set of accessions to be used in the breeding program. METHODS: TASSEL-GBS pipeline was used for SNP discovery, fastStructure was used to determine the population structure and PowerCore was used to generate a core sets. Nucleotide diversity matrices of Nie's and F-statistics (FST) index were used to determine the center of genetic diversity. RESULTS: We found 65 % and 47 % duplicated accessions in Triticum timopheevii and T. turgidum respectively. Genome-wide nucleotide diversity and FST scan uncovered a lower intra and higher inter-species differentiation. Distinct FST regions were identified in genomic regions belonging to domestication genes: non-brittle rachis (Btr1) and vernalization (VRN-1).Our results suggest that Israel, Jordan, Syria, and Lebanonas the hub of genetic diversity of wild emmer;Turkey, and Georgia for T. durum; and Iraq, Azerbaijan, and Armenia for theT. timopheevii. Identified core set accessions preserved more than 93 % of the available genetic diversity. Genome wide association study (GWAS) indicated the potential chromosomal segment for resistance to leaf rust in T. timopheevii. CONCLUSION: The present study explored the potential of GBS technology in data reduction while maintaining the significant genetic diversity of the species. Wild germplasm showed more differentiation than domesticated accessions, indicating the availability of sufficient diversity for crop improvement. With reduced complexity, the core set preserves the genetic diversity of the gene bank collections and will aid in a more robust characterization of wild germplasm.

Aspergillus flavus genetic structure at a turkey farm.

BACKGROUND: The ubiquitous environmental fungus Aspergillus flavus is also a life-threatening avian pathogen. OBJECTIVES: This study aimed to assess the genetic diversity and population structure of A. flavus isolated from turkey lung biopsy or environmental samples collected in a poultry farm. METHODS: A. flavus isolates were identified using both morphological and ITS sequence features. Multilocus microsatellite genotyping was performed by using a panel of six microsatellite markers. Population genetic indices were computed using FSTAT and STRUCTURE. A minimum-spanning tree (MST) and UPGMA dendrogram were drawn using BioNumerics and NTSYS-PC, respectively. RESULTS: The 63 environmental (air, surfaces, eggshells and food) A. flavus isolates clustered in 36 genotypes (genotypic diversity = 0.57), and the 19 turkey lung biopsies isolates clustered in 17 genotypes (genotypic diversity = 0.89). The genetic structure of environmental and avian A. flavus populations were clearly differentiated, according to both F-statistics and Bayesian model-based analysis' results. The Bayesian approach indicated gene flow between both A. flavus populations. The MST illustrated the genetic structure of this A. flavus population split in nine clusters, including six singletons. CONCLUSIONS: Our results highlight the distinct genetic structure of environmental and avian A. flavus populations, indicative of a genome-based adaptation of isolates involved in avian aspergillosis.

The genetic origin of Huns, Avars, and conquering Hungarians.

Huns, Avars, and conquering Hungarians were migration-period nomadic tribal confederations that arrived in three successive waves in the Carpathian Basin between the 5th and 9th centuries. Based on the historical data, each of these groups are thought to have arrived from Asia, although their exact origin and relation to other ancient and modern populations have been debated. Recently, hundreds of ancient genomes were analyzed from Central Asia, Mongolia, and China, from which we aimed to identify putative source populations for the above-mentioned groups. In this study, we have sequenced 9 Hun, 143 Avar, and 113 Hungarian conquest period samples and identified three core populations, representing immigrants from each period with no recent European ancestry. Our results reveal that this "immigrant core" of both Huns and Avars likely originated in present day Mongolia, and their origin can be traced back to Xiongnus (Asian Huns), as suggested by several historians. On the other hand, the "immigrant core" of the conquering Hungarians derived from an earlier admixture of Mansis, early Sarmatians, and descendants of late Xiongnus. We have also shown that a common "proto-Ugric" gene pool appeared in the Bronze Age from the admixture of Mezhovskaya and Nganasan people, supporting genetic and linguistic data. In addition, we detected shared Hun-related ancestry in numerous Avar and Hungarian conquest period genetic outliers, indicating a genetic link between these successive nomadic groups. Aside from the immigrant core groups, we identified that the majority of the individuals from each period were local residents harboring "native European" ancestry.

A geometric relationship of F2, F3 and F4-statistics with principal component analysis.

Principal component analysis (PCA) and F-statistics sensu Patterson are two of the most widely used population genetic tools to study human genetic variation. Here, I derive explicit connections between the two approaches and show that these two methods are closely related. F-statistics have a simple geometrical interpretation in the context of PCA, and orthogonal projections are a key concept to establish this link. I show that for any pair of populations, any population that is admixed as determined by an F3-statistic will lie inside a circle on a PCA plot. Furthermore, the F4-statistic is closely related to an angle measurement, and will be zero if the differences between pairs of populations intersect at a right angle in PCA space. I illustrate my results on two examples, one of Western Eurasian, and one of global human diversity. In both examples, I find that the first few PCs are sufficient to approximate most F-statistics, and that PCA plots are effective at predicting F-statistics. Thus, while F-statistics are commonly understood in terms of discrete populations, the geometric perspective illustrates that they can be viewed in a framework of populations that vary in a more continuous manner. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

f-Statistics estimation and admixture graph construction with Pool-Seq or allele count data using the R package poolfstat.

By capturing various patterns of the structuring of genetic variation across populations, f -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D ) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F 4 -ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analysed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

Genetic diversity and the origins of parthenogenesis in the teiid lizard Aspidoscelis laredoensis.

Unisexual vertebrates typically form through hybridization events between sexual species in which reproductive mode transitions occur in the hybrid offspring. This evolutionary history is thought to have important consequences for the ecology of unisexual lineages and their interactions with congeners in natural communities. However, these consequences have proven challenging to study owing to uncertainty about patterns of population genetic diversity in unisexual lineages. Of particular interest is resolving the contribution of historical hybridization events versus post formational mutation to patterns of genetic diversity in nature. Here we use restriction site associated DNA genotyping to evaluate genetic diversity and demographic history in Aspidoscelis laredoensis, a diploid unisexual lizard species from the vicinity of the Rio Grande River in southern Texas and northern Mexico. The sexual progenitor species from which one or more lineages are derived also occur in the Rio Grande Valley region, although patterns of distribution across individual sites are quite variable. Results from population genetic and phylogenetic analyses resolved the major axes of genetic variation in this species and highlight how these match predictions based on historical patterns of hybridization. We also found discordance between results of demographic modelling using different statistical approaches with the genomic data. We discuss these insights within the context of the ecological and evolutionary mechanisms that generate and maintain lineage diversity in unisexual species. As one of the most dynamic, intriguing, and geographically well investigated groups of whiptail lizards, these species hold substantial promise for future studies on the constraints of diversification in unisexual vertebrates.

Population structure and phylogenetic relationships in Brassica rapa L. subspecies by using isozyme markers / Estrutura populacional e relações filogenéticas em subespécies de Brassica rapa L. utilizando marcadores isoenzimáticos

Abstract The present study aimed to assess population structure and phylogenetic relationships of nine subspecies of Brassica rapa L. represented with thirty-five accessions cover a wide range of species distribution area using isozyme analysis in order to select more diverse accessions as supplementary resources that can be utilized for improvement of B. napus. Enzyme analysis resulted in detecting 14 putative polymorphic loci with 27 alleles. Mean allele frequency 0.04 (rare alleles) was observed in Cat4A and Cat4B in sub species Oleifera accession CR 2204/79 and in subspecies trilocularis accessions CR 2215/88 and CR 2244/88. The highest genetic diversity measures were observed in subspecies dichotoma, accession CR 1585/96 (the highest average of observed (H0) and expected heterozygosity (He), and number of alleles per locus (Ae)). These observations make this accession valuable genetic resource to be included in breeding programs for the improvement of oilseed B. napus. The average fixation index (F) is significantly higher than zero for the analysis accessions indicating a significant deficiency of heteozygosity. The divergence among subspecies indicated very great genetic differentiation (FST = 0.8972) which means that about 90% of genetic diversity is distributed among subspecies, while 10% of the diversity is distributed within subspecies. This coincides with low value of gene flow (Nm = 0.0287). B. rapa ssp. oleifera (turnip rape) and B. rapa ssp. trilocularis (sarson) were grouped under one cluster which coincides with the morphological classification.

Resumo O presente estudo teve como objetivo avaliar a estrutura populacional e as relações filogenéticas de nove subespécies de Brassica rapa L. representadas com 35 acessos, cobrindo uma ampla gama de áreas de distribuição de espécies usando análise isoenzimática, a fim de selecionar acessos mais diversos como recursos suplementares que podem ser utilizados para melhoria de B. napus. A análise enzimática resultou na detecção de 14 loci polimórficos putativos com 27 alelos. A frequência média de 0,04 alelo (alelos raros) foi observada em Cat4A e Cat4B, nas subespécies Oleifera CR 2204/79 e nas subespécies trilocularis CR 2215/88 e CR 2244/88. As maiores medidas de diversidade genética foram observadas na subespécie dicotômica CR 1585/96 (a média mais alta observada (H0) e heterozigosidade esperada (He) e número de alelos por locus (Ae). Essas observações tornam esse acesso um valioso recurso genético a ser incluído em programas de melhoramento de oleaginosas B. napus. O índice médio de fixação (F) é significativamente maior que 0 para os acessos à análise, indicando uma deficiência significativa de heterozigose. A divergência entre as subespécies indicou uma grande diferenciação genética (FST = 0,8972), o que significa que cerca de 90% da diversidade genética é distribuída entre as subespécies, enquanto 10% da diversidade é distribuída nas subespécies. Isso coincide com o baixo valor do fluxo gênico (Nm = 0,0287). B. rapa ssp. oleifera (nabo) e B. rapa ssp. trilocularis (sarson) foram agrupados conforme a classificação morfológica.

The discernible and hidden effects of clonality on the genotypic and genetic states of populations: Improving our estimation of clonal rates.

Partial clonality is widespread across the tree of life, but most population genetic models are designed for exclusively clonal or sexual organisms. This gap hampers our understanding of the influence of clonality on evolutionary trajectories and the interpretation of population genetic data. We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto ß) and genetic (FIS and linkage disequilibrium) indices, and tested predictions of c from population genetic data through supervised machine learning. Two complementary behaviours emerged from the probability distributions of genotypic and genetic indices with increasing c. While the impact of c on R and Pareto ß was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c > 0.95). Consequently, genotypic indices allowed reliable estimates of c, while genetic descriptors led to poorer performances when c < 0.95. These results provide clear baseline expectations for genotypic and genetic diversity and dynamics under partial clonality. Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness, should be reappraised. We propose future avenues to derive realistic confidence intervals for c and show that, although still approximate, a supervised learning method would greatly improve the estimation of c from population genetic data.

A Geometrical Framework for f-Statistics.

A detailed derivation of the f-statistics formalism is made from a geometrical framework. It is shown that the f-statistics appear when a genetic distance matrix is constrained to describe a four population phylogenetic tree. The choice of genetic metric is crucial and plays an outstanding role as regards the tree-like-ness criterion. The case of lack of treeness is interpreted in the formalism as the presence of population admixture. In this respect, four formulas are given to estimate the admixture proportions. One of them is the so-called [Formula: see text]-ratio estimate and we show that a second one is related to a known result developed in terms of the fixation index [Formula: see text]. An illustrative numerical simulation of admixture proportion estimates is included. Relationships of the formalism with coalescence times and pairwise sequence differences are also provided.

Discriminant Canonical Analysis of the Contribution of Spanish and Arabian Purebred Horses to the Genetic Diversity and Population Structure of Hispano-Arabian Horses.

Genetic diversity and population structure were analyzed using the historical and current pedigree information of the Arabian (PRá), Spanish Purebred (PRE), and Hispano-Arabian (Há) horse breeds. Genetic diversity parameters were computed and a canonical discriminant analysis was used to determine the contributions of ancestor breeds to the genetic diversity of the Há horse. Pedigree records were available for 207,100 animals born between 1884 and 2019. Nei's distances and the equivalent subpopulations number indicated the existence of a highly structured, integrated population for the Há breed, which is more closely genetically related to PRá than PRE horses. An increase in the length of the generation interval might be an effective solution to reduce the increase in inbreeding found in the studied breeds (8.44%, 8.50%, and 2.89%, for PRá, PRE, and Há, respectively). Wright's fixation statistics indicated slight interherd inbreeding. Pedigree completeness suggested genetic parameters were highly reliable. High GCI levels found for number of founders and non-founders and their relationship to the evolution of inbreeding permit controlling potential deleterious negative effects from excessively frequent mating between interrelated individuals. For instance, the use of individuals presenting high GCI may balance founders' gene contributions and consequently preserve genetic diversity levels (current genetic diversity loss in PRá, PRE, and Há is 6%, 7%, and 4%, respectively).

Detecting Selection from Linked Sites Using an F-Model.

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

Applying f4 -statistics and admixture graphs: Theory and examples.

A popular approach to learning about admixture from population genetic data is by computing the allele-sharing summary statistics known as f-statistics. Compared to some methods in population genetics, f-statistics are relatively simple, but interpreting them can still be complicated at times. In addition, f-statistics can be used to build admixture graphs (multi-population trees allowing for admixture events), which provide more explicit and thorough modelling capabilities but are correspondingly more complex to work with. Here, I discuss some of these issues to provide users of these tools with a basic guide for protocols and procedures. My focus is on the kinds of conclusions that can or cannot be drawn from the results of f4 -statistics and admixture graphs, illustrated with real-world examples involving human populations.

Analysis of genetic characteristics of pig breeds using information on single nucleotide polymorphisms.

OBJECTIVE: This study was undertaken to investigate the genetic characteristics of Berkshire (BS), Landrace (LR), and Yorkshire (YS) pig breeds raised in the Great Grandparents pig farms using the single nucleotide polymorphisms (SNP) information. METHODS: A total of 25,921 common SNP genotype markers in three pig breeds were used to estimate the expected heterozygosity (HE), polymorphism information content, F-statistics (FST), linkage disequilibrium (LD) and effective population size (Ne). RESULTS: The chromosome-wise distribution of FST in BS, LR, and YS populations were within the range of 0-0.36, and the average FST value was estimated to be 0.07±0.06. This result indicated some level of genetic segregation. An average LD (r2) for the BS, LR, and YS breeds was estimated to be approximately 0.41. This study also found an average Ne of 19.9 (BS), 31.4 (LR), and 34.1 (YS) over the last 5th generations. The effective population size for the BS, LR, and YS breeds decreased at a consistent rate from 50th to 10th generations ago. With a relatively faster Ne decline rate in the past 10th generations, there exists possible evidence for intensive selection practices in pigs in the recent past. CONCLUSION: To develop customized chips for the genomic selection of various breeds, it is important to select and utilize SNP based on the genetic characteristics of each breed. Since the improvement efficiency of breed pigs increases sharply by the population size, it is important to increase test units for the improvement and it is desirable to establish the pig improvement network system to expand the unit of breed pig improvement through the genetic connection among breed pig farms.

Population genetic structure of the parasitic nematode Spirocerca lupi in South Africa.

Spirocerca lupi is a parasitic nematode of canids and occurs in most tropical and subtropical regions around the world. While its life cycle is well known, insight is lacking about its mating structure within-hosts, genetic variability and long-distance dispersal ability. These characteristics contribute significantly to the dynamics and spread of potential resistance genes, which impacts on the control of S. lupi. To evaluate the population structure and infer potential mating behaviour of S. lupi, we genotyped 130 samples at nine microsatellite loci from three geographical locations in South Africa, between 600 and 1000 km apart. These loci identified unique individuals with high levels of polymorphism suggesting that these are not newly established S. lupi populations in South Africa and that effective population sizes must be large. Population genetic analyses showed that populations are not very distinct, that worms within dogs are more similar to each other than random worms from each population, and that mating is at random within dogs. We can thus infer that the parasite is frequently transported over great distances. Even so, two genetically distinct populations could be identified. Relatedness of worms within dogs were significantly higher than between dogs and together with F-statistics suggests some non-random transmission of parasites between hosts. While mating is random within a host, parasites from a host are more likely to be related and hence an increase in homozygosity is seen. The implications of this genetic structure on parasite control are considered.

Effects of sampling close relatives on some elementary population genetics analyses.

Many molecular ecology analyses assume the genotyped individuals are sampled at random from a population and thus are representative of the population. Realistically, however, a sample may contain excessive close relatives (ECR) because, for example, localized juveniles are drawn from fecund species. Our knowledge is limited about how ECR affect the routinely conducted elementary genetics analyses, and how ECR are best dealt with to yield unbiased and accurate parameter estimates. This study quantifies the effects of ECR on some popular population genetics analyses of marker data, including the estimation of allele frequencies, F-statistics, expected heterozygosity (He ), effective and observed numbers of alleles, and the tests of Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE). It also investigates several strategies for handling ECR to mitigate their impact and to yield accurate parameter estimates. My analytical work, assisted by simulations, shows that ECR have large and global effects on all of the above marker analyses. The naïve approach of simply ignoring ECR could yield low-precision and often biased parameter estimates, and could cause too many false rejections of HWE and LE. The bold approach, which simply identifies and removes ECR, and the cautious approach, which estimates target parameters (e.g., He ) by accounting for ECR and using naïve allele frequency estimates, eliminate the bias and the false HWE and LE rejections, but could reduce estimation precision substantially. The likelihood approach, which accounts for ECR in estimating allele frequencies and thus target parameters relying on allele frequencies, usually yields unbiased and the most accurate parameter estimates. Which of the four approaches is the most effective and efficient may depend on the particular marker analysis to be conducted. The results are discussed in the context of using marker data for understanding population properties and marker properties.

A Mixed Model Approach to Genome-Wide Association Studies for Selection Signatures, with Application to Mice Bred for Voluntary Exercise Behavior.

Selection experiments and experimental evolution provide unique opportunities to study the genetics of adaptation because the target and intensity of selection are known relatively precisely. In contrast to natural selection, where populations are never strictly "replicated," experimental evolution routinely includes replicate lines so that selection signatures-genomic regions showing excessive differentiation between treatments-can be separated from possible founder effects, genetic drift, and multiple adaptive solutions. We developed a mouse model with four lines within a high running (HR) selection treatment and four nonselected controls (C). At generation 61, we sampled 10 mice of each line and used the Mega Mouse Universal Genotyping Array to obtain single nucleotide polymorphism (SNP) data for 25,318 SNPs for each individual. Using an advanced mixed model procedure developed in this study, we identified 152 markers that were significantly different in frequency between the two selection treatments. They occurred on all chromosomes except 1, 2, 8, 13, and 19, and showed a variety of patterns in terms of fixation (or the lack thereof) in the four HR and four C lines. Importantly, none were fixed for alternative alleles between the two selection treatments. The current state-of-the-art regularized F test applied after pooling DNA samples for each line failed to detect any markers. We conclude that when SNP or sequence data are available from individuals, the mixed model methodology is recommended for selection signature detection. As sequencing at the individual level becomes increasingly feasible, the new methodology may be routinely applied for detection of selection.