Interpreting the flock algorithm from a statistical perspective

We show that the algorithm in the program flock (Duchesne & Turgeon 2009) can be interpreted as an estimation procedure based on a model essentially identical to the structure (Pritchard et al. 2000) model with no admixture and without correlated allele frequency priors. Rather than using MCMC, the flock algorithm searches for the maximum a posteriori estimate of this structure model via a simulated annealing algorithm with a rapid cooling schedule (namely, the exponent on the objective function →∞). We demonstrate the similarities between the two programs in a two‐step approach. First, to enable rapid batch processing of many simulated data sets, we modified the source code of structure to use the flock algorithm, producing the program flockture . With simulated data, we confirmed that results obtained with flock and flockture are very similar (though flockture is some 200 times faster). Second, we simulated multiple large data sets under varying levels of population differentiation for both microsatellite and SNP genotypes. We analysed them with flockture and structure and assessed each program on its ability to cluster individuals to their correct subpopulation. We show that flockture yields results similar to structure albeit with greater variability from run to run. flockture did perform better than structure when genotypes were composed of SNPs and differentiation was moderate ( $F_{\mathrm{ST}}=$ 0.022–0.032). When differentiation was low, structure outperformed flockture for both marker types. On large data sets like those we simulated, it appears that flock 's reliance on inference rules regarding its ‘plateau record’ is not helpful. Interpreting flock 's algorithm as a special case of the model in structure should aid in understanding the program's output and behaviour.     

RAD genotyping reveals fine‐scale genetic structuring and provides powerful population assignment in a widely distributed marine species,the American lobster (Homarus americanus)

Deciphering genetic structure and inferring connectivity in marine species have been challenging due to weak genetic differentiation and limited resolution offered by traditional genotypic methods. The main goal of this study was to assess how a population genomics framework could help delineate the genetic structure of the American lobster (Homarus americanus) throughout much of the species’ range and increase the assignment success of individuals to their location of origin. We genotyped 10 156 filtered SNPs using RAD sequencing to delineate genetic structure and perform population assignment for 586 American lobsters collected in 17 locations distributed across a large portion of the species’ natural distribution range. Our results revealed the existence of a hierarchical genetic structure, first separating lobsters from the northern and southern part of the range (F_CT = 0.0011; P‐value = 0.0002) and then revealing a total of 11 genetically distinguishable populations (mean F_ST = 0.00185; CI: 0.0007–0.0021, P‐value < 0.0002), providing strong evidence for weak, albeit fine‐scale population structuring within each region. A resampling procedure showed that assignment success was highest with a subset of 3000 SNPs having the highest F_ST. Applying Anderson's (Molecular Ecology Resources, 2010, 10, 701) method to avoid ‘high‐grading bias’, 94.2% and 80.8% of individuals were correctly assigned to their region and location of origin, respectively. Lastly, we showed that assignment success was positively associated with sample size. These results demonstrate that using a large number of SNPs improves fine‐scale population structure delineation and population assignment success in a context of weak genetic structure. We discuss the implications of these findings for the conservation and management of highly connected marine species, particularly regarding the geographic scale of demographic independence.     

Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation

Traditional methods for characterizing genetic differentiation among populations rely on a priori grouping of individuals. Bayesian clustering methods avoid this limitation by using linkage and Hardy–Weinberg disequilibrium to decompose a sample of individuals into genetically distinct groups. There are several software programs available for Bayesian clustering analyses, all of which describe a decrease in the ability to detect distinct clusters as levels of genetic differentiation among populations decrease. However, no study has yet compared the performance of such methods at low levels of population differentiation, which may be common in species where populations have experienced recent separation or high levels of gene flow. We used simulated data to evaluate the performance of three Bayesian clustering software programs, PARTITION, STRUCTURE, and BAPS, at levels of population differentiation below F _ST=0.1. PARTITION was unable to correctly identify the number of subpopulations until levels of F _ST reached around 0.09. Both STRUCTURE and BAPS performed very well at low levels of population differentiation, and were able to correctly identify the number of subpopulations at F _ST around 0.03. The average proportion of an individual’s genome assigned to its true population of origin increased with increasing F _ST for both programs, reaching over 92% at an F _ST of 0.05. The average number of misassignments (assignments to the incorrect subpopulation) continued to decrease as F _ST increased, and when F _ST was 0.05, fewer than 3% of individuals were misassigned using either program. Both STRUCTURE and BAPS worked extremely well for inferring the number of clusters when clusters were not well-differentiated (F _ST=0.02–0.03), but our results suggest that F _ST must be at least 0.05 to reach an assignment accuracy of greater than 97%.     

Evidence of divergent selection for drought and cold tolerance at landscape and local scales in Abies alba Mill. in the French Mediterranean Alps

Understanding local adaptation in forest trees is currently a key research and societal priority. Geographically and ecologically marginal populations provide ideal case studies, because environmental stress along with reduced gene flow can facilitate the establishment of locally adapted populations. We sampled European silver fir (Abies alba Mill.) trees in the French Mediterranean Alps, along the margin of its distribution range, from pairs of high‐ and low‐elevation plots on four different mountains situated along a 170‐km east–west transect. The analysis of 267 SNP loci from 175 candidate genes suggested a neutral pattern of east–west isolation by distance among mountain sites. F_ST outlier tests revealed 16 SNPs that showed patterns of divergent selection. Plot climate was characterized using both in situ measurements and gridded data that revealed marked differences between and within mountains with different trends depending on the season. Association between allelic frequencies and bioclimatic variables revealed eight genes that contained candidate SNPs, of which two were also detected using F_ST outlier methods. All SNPs were associated with winter drought, and one of them showed strong evidence of selection with respect to elevation. Q_ST–F_ST tests for fitness‐related traits measured in a common garden suggested adaptive divergence for the date of bud flush and for growth rate. Overall, our results suggest a complex adaptive picture for A. alba in the southern French Alps where, during the east‐to‐west Holocene recolonization, locally advantageous genetic variants established at both the landscape and local scales.     

Population genetic inferences using immune gene SNPs mirror patterns inferred by microsatellites

Single nucleotide polymorphisms (SNPs) are replacing microsatellites for population genetic analyses, but it is not apparent how many SNPs are needed or how well SNPs correlate with microsatellites. We used data from the gopher tortoise, Gopherus polyphemus—a species with small populations, to compare SNPs and microsatellites to estimate population genetic parameters. Specifically, we compared one SNP data set (16 tortoises from four populations sequenced at 17 901 SNPs) to two microsatellite data sets, a full data set of 101 tortoises and a partial data set of 16 tortoises previously genotyped at 10 microsatellites. For the full microsatellite data set, observed heterozygosity, expected heterozygosity and F_ST were correlated between SNPs and microsatellites; however, allelic richness was not. The same was true for the partial microsatellite data set, except that allelic richness, but not observed heterozygosity, was correlated. The number of clusters estimated by structure differed for each data set (SNPs = 2; partial microsatellite = 3; full microsatellite = 4). Principle component analyses (PCA) showed four clusters for all data sets. More than 800 SNPs were needed to correlate with allelic richness, observed heterozygosity and expected heterozygosity, but only 100 were needed for F_ST. The number of SNPs typically obtained from next‐generation sequencing (NGS) far exceeds the number needed to correlate with microsatellite parameter estimates. Our study illustrates that diversity, F_ST and PCA results from microsatellites can mirror those obtained with SNPs. These results may be generally applicable to small populations, a defining feature of endangered and threatened species, because theory predicts that genetic drift will tend to outweigh selection in small populations.     

Generalization of the QST framework in hierarchically structured populations: Impacts of inbreeding and dominance

Q_ST is a differentiation parameter based on the decomposition of the genetic variance of a trait. In the case of additive inheritance and absence of selection, it is analogous to the genic differentiation measured on individual loci, F_ST. Thus, Q_ST?F_ST comparison is used to infer selection: selective divergence when Q_ST > F_ST, or convergence when Q_ST < F_ST. The definition of Q‐statistics was extended to two‐level hierarchical population structures with Hardy–Weinberg equilibrium. Here, we generalize the Q‐statistics framework to any hierarchical population structure. First, we developed the analytical definition of hierarchical Q‐statistics for populations not at Hardy–Weinberg equilibrium. We show that the Q‐statistics values obtained with the Hardy–Weinberg definition are lower than their corresponding F‐statistics when F_IS > 0 (higher when F_IS < 0). Then, we used an island model simulation approach to investigate the impact of inbreeding and dominance on the Q_ST?F_ST framework in a hierarchical population structure. We show that, while differentiation at the lower hierarchical level (Q_SR) is a monotonic function of migration, differentiation at the upper level (Q_RT) is not. In the case of additive inheritance, we show that inbreeding inflates the variance of Q_RT, which can increase the frequency of Q_RT > F_RT cases. We also show that dominance drastically reduces Q‐statistics below F‐statistics for any level of the hierarchy. Therefore, high values of Q‐statistics are good indicators of selection, but low values are not in the case of dominance.     

On the Apportionment of Population Structure

Measures of population differentiation, such as F_ST, are traditionally derived from the partition of diversity within and between populations. However, the emergence of population clusters from multilocus analysis is a function of genetic structure (departures from panmixia) rather than of diversity. If the populations are close to panmixia, slight differences between the mean pairwise distance within and between populations (low F_ST) can manifest as strong separation between the populations, thus population clusters are often evident even when the vast majority of diversity is partitioned within populations rather than between them. For any given F_ST value, clusters can be tighter (more panmictic) or looser (more stratified), and in this respect higher F_ST does not always imply stronger differentiation. In this study we propose a measure for the partition of structure, denoted E_ST, which is more consistent with results from clustering schemes. Crucially, our measure is based on a statistic of the data that is a good measure of internal structure, mimicking the information extracted by unsupervised clustering or dimensionality reduction schemes. To assess the utility of our metric, we ranked various human (HGDP) population pairs based on F_ST and E_ST and found substantial differences in ranking order. E_ST ranking seems more consistent with population clustering and classification and possibly with geographic distance between populations. Thus, E_ST may at times outperform F_ST in identifying evolutionary significant differentiation.     

Characterization of 19 single nucleotide polymorphism markers for coho salmon

We report 39 single nucleotide polymorphisms (SNPs) observed in 23 nuclear DNA sequences in coho salmon Oncorhynchus kisutch. High‐throughput genotyping assays based on the 5′‐nuclease reaction were developed for 17 of these nuclear SNPs and for two previously published mitochondrial DNA SNPs. Minor allele frequency differences (Δq) among collections were between 5.2% and 51.2%, resulting in per locus F_ST estimates of 0.00–0.24 with an average of 0.09.     

Prediction of breed composition in an admixed cattle population

Swiss Fleckvieh was established in 1970 as a composite of Simmental (SI) and Red Holstein Friesian (RHF) cattle. Breed composition is currently reported based on pedigree information. Information on a large number of molecular markers potentially provides more accurate information. For the analysis, we used Illumina BovineSNP50 Genotyping Beadchip data for 90 pure SI, 100 pure RHF and 305 admixed bulls. The scope of the study was to compare the performance of hidden Markov models, as implemented in structure software, with methods conventionally used in genomic selection [BayesB, partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO) variable selection)] for predicting breed composition. We checked the performance of algorithms for a set of 40 492 single nucleotide polymorphisms (SNPs), subsets of evenly distributed SNPs and subsets with different allele frequencies in the pure populations, using F_ST as an indicator. Key results are correlations of admixture levels estimated with the various algorithms with admixture based on pedigree information. For the full set, PLSR, BayesB and structure performed in a very similar manner (correlations of 0.97), whereas the correlation of LASSO and pedigree admixture was lower (0.93). With decreasing number of SNPs, correlations decreased substantially only for 5% or 1% of all SNPs. With SNPs chosen according to F_ST, results were similar to results obtained with the full set. Only when using 96 and 48 SNPs with the highest F_ST, correlations dropped to 0.92 and 0.90 respectively. Reducing the number of pure animals in training sets to 50, 20 and 10 each did not cause a drop in the correlation with pedigree admixture.     

Contrasting patterns of morphological and neutral genetic divergence among geographically proximate populations of sockeye salmon Oncorhynchus nerka in Lake Aleknagik,Alaska

Levels of differentiation in morphological traits (age at maturity, body length at age, egg mass and body depth) and spawning time were examined in sockeye salmon Oncorhynchus nerka from three geographically proximate but physically distinct creeks in Lake Aleknagik, Alaska. Happy Creek fish had significantly greater values for most measured morphological traits, and Eagle Creek fish spawned significantly later than fish in the other creeks. Phenotypic differentiation between creeks, measured as P_ST, was then compared with microsatellite marker differentiation between creeks, measured as F_ST. No correlations were apparent between P_ST and F_ST values, and P_ST values were generally significantly larger than zero (P_ST= 0·0018–0·31) whereas F_ST values were not (F_ST=?0·0004 to 0·0016). The insignificant pair‐wise F_ST values between creek samples indicated that gene flow occurs between creeks, assuming the creek populations have reached migration–drift equilibrium. However, the strong homing behaviour of sockeye salmon precludes a scenario in which fish from the three creeks constitute a single population that segregates by body size. Rather, significant phenotypic differentiation suggests that strong divergent selection occurs on the phenotypic traits despite the homogenizing effects of gene flow.     

Pervasive selection or is it…? why are FST outliers sometimes so frequent?

It is now common for population geneticists to estimate F_ST for a large number of loci across the genome, before testing for selected loci as being outliers to the F_ST distribution. One surprising result of such F_ST scans is the often high proportion (>1% and sometimes >10%) of outliers detected, and this is often interpreted as evidence for pervasive local adaptation. In this issue of Molecular Ecolog, Fourcade et al. ( 2013 ) observe that a particularly high rate of F_ST outliers has often been found in river organisms, such as fishes or damselflies, despite there being no obvious reason why selection should affect a larger proportion of the genomes of these organisms. Using computer simulations, Fourcade et al. ( 2013 ) show that the strong correlation in co‐ancestry produced in long one‐dimensional landscapes (such as rivers, valleys, peninsulas, oceanic ridges or coastlines) greatly increases the neutral variance in F_ST, especially when the landscape is further reticulated into fractal networks. As a consequence, outlier tests have a high rate of false positives, unless this correlation can be taken into account. Fourcade et al.'s study highlights an extreme case of the general problem, first noticed by Robertson ( 1975a , b ) and Nei & Maruyama ( 1975 ), that correlated co‐ancestry inflates the neutral variance in F_ST when compared to its expectation under an island model of population structure. Similar warnings about the validity of outlier tests have appeared regularly since then but have not been widely cited in the recent genomics literature. We further emphasize that F_ST outliers can arise in many different ways and that outlier tests are not designed for situations where the genetic architecture of local adaptation involves many loci.     

The empirical Bayes estimators of fine‐scale population structure in high gene flow species

An empirical Bayes (EB) pairwise F_ST estimator was previously introduced and evaluated for its performance by numerical simulation. In this study, we conducted coalescent simulations and generated genetic population structure mechanistically, and compared the performance of the EBF_ST with Nei's G_ST, Nei and Chesser's bias‐corrected G_ST (G_{ST_NC}), Weir and Cockerham's θ (θ_WC) and θ with finite sample correction (θ_{WC_F}). We also introduced EB estimators for Hedrick’ G’_ST and Jost’ D. We applied these estimators to publicly available SNP genotypes of Atlantic herring. We also examined the power to detect the environmental factors causing the population structure. Our coalescent simulations revealed that the finite sample correction of θ_WC is necessary to assess population structure using pairwise F_ST values. For microsatellite markers, EBF_ST performed the best among the present estimators regarding both bias and precision under high gene flow scenarios (). For 300 SNPs, EBF_ST had the highest precision in all cases, but the bias was negative and greater than those for G_{ST_NC} and θ_{WC_F} in all cases. G_{ST_NC} and θ_{WC_F} performed very similarly at all levels of F_ST. As the number of loci increased up to 10 000, the precision of G_{ST_NC} and θ_{WC_F} became slightly better than for EBF_ST for cases with , even though the size of the bias remained constant. The EB estimators described the fine‐scale population structure of the herring and revealed that ~56% of the genetic differentiation was caused by sea surface temperature and salinity. The R package finepop for implementing all estimators used here is available on CRAN.     

Rapid adaptive divergence between ecotypes of an aquatic isopod inferred from FST–QST analysis

Divergent natural selection is often thought to be the principal factor driving phenotypic differentiation between populations. We studied two ecotypes of the aquatic isopod Asellus aquaticus which have diverged in parallel in several Swedish lakes. In these lakes, isopods from reed belts along the shores colonized new stonewort stands in the centre of the lakes and rapid phenotypic changes in size and pigmentation followed after colonization. We investigated if selection was likely to be responsible for these observed phenotypic changes using indirect inferences of selection (F_ST–Q_ST analysis). Average Q_ST for seven quantitative traits were higher than the average F_ST between ecotypes for putatively neutral markers (AFLPs). This suggests that divergent natural selection has played an important role during this rapid diversification. In contrast, the average Q_ST between the different reed ecotype populations was not significantly different from the mean F_ST. Genetic drift could therefore not be excluded as an explanation for the minor differences between allopatric populations inhabiting the same source habitat. We complemented this traditional F_ST–Q_ST approach by comparing the F_ST distributions across all loci (n = 67–71) with the Q_ST for each of the seven traits. This analysis revealed that pigmentation traits had diverged to a greater extent and at higher evolutionary rates than size‐related morphological traits. In conclusion, this extended and detailed type of F_ST–Q_ST analysis provides a powerful method to infer adaptive phenotypic divergence between populations. However, indirect inferences about the operation of divergent selection should be analyzed on a per‐trait basis and complemented with detailed ecological information.     

Micro- and Macro-Geographic Scale Effect on the Molecular Imprint of Selection and Adaptation in Norway Spruce

Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based F _ST-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the F _ST-outlier methods detected together 11 F _ST-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with F _ST-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.     

Estimation and adjustment of microsatellite null alleles in nonequilibrium populations

Nonamplified (null) alleles are a common feature of microsatellite genotyping and can bias estimates of allele and genotype frequencies, thereby hindering population genetic analyses. The frequency of microsatellite null alleles in diploid populations can be estimated for populations that are in Hardy–Weinberg equilibrium. However, many microsatellite data sets are from nonequilibrium populations, often with known inbreeding coefficients (F) or fixation indices (F_IS or F_ST). Here, we propose a novel null allele estimator that can be used to estimate the null allele frequency and adjust visible allele frequencies in populations for which independent estimates of F, F_IS or F_ST are available. The algorithm is currently available as an Excel macro that can be downloaded at no cost from http://www.microchecker.hull.ac.uk/ and will be incorporated into the software micro ‐checker .     

Population genetics of the common long-armed octopus Octopus minor (Sasaki, 1920) (Cephalopoda: Octopoda) in Chinese waters based on microsatellite analysis

Octopus minor (Sasaki, 1920) is a commercially important cephalopod in Chinese waters. To provide a theoretical basis for resource protection and sustainable management, we investigated genetic structure of ten O. minor populations in Chinese waters using microsatellite DNA markers. Eight microsatellite loci revealed high allelic diversity with 11–26 alleles per locus. Observed and expected heterozygosity varied from 0.412 to 0.900 and from 0.337 to 0.845, respectively. The overall F_ST value was 0.198, indicating great genetic differentiation among populations. The F_ST value between Yilan and other populations reached more than 0.3 that may be indicative of subspecies rank. Mantel test showed significant correlations between genetic and geographic distance (R = 0.383, P = 0.004) indicating that genetic differentiation of O. minor conformed to a pattern of isolation-by-distance. Using the Neighbor-joining method, cluster analysis divided nine populations into three groups and divided ten populations into two groups wherein Yilan was distinguished from the other populations. Analysis based on F_ST, D_c values and clustering highlighted the heterogeneity of Yilan and the relative homogeneity between Yilan and Ganyu. The significant population genetic structure of O. minor is related to the combined effects of geographical barriers, current features and life history characteristics.     

Genetic Diversity and Structure of Two Prominent Zebu Cattle Breeds Adapted to the Arid Region of India Inferred from Microsatellite Polymorphism

This study aims to assess the genetic diversity and population structure of two major zebu dairy breeds (Tharparkar and Rathi) adapted to the arid region of Rajasthan state of India. Various variability estimates indicate the existence of sufficient within-breed genetic diversity. Mean estimates of F-statistics are significantly different from zero: F _IS = 0.112 ± 0.029, F _IT = 0.169 ± 0.033, F _ST = 0.065 ± 0.017. The overall positive value of F _IS (0.112) and an F _IT value (0.169) that is more than the F _ST (0.065) indicate departure from random mating. The drift-based estimates reflect a moderate yet significant level of breed differentiation between the Tharparkar and Rathi breeds. The evaluation of an exact test, showing that allele frequencies across all the loci differed significantly, supports the population differentiation. This is paralleled by the outcome of neighbor-joining clustering based on allele-sharing distance measures. The allocation of a high percentage of individuals (95.7%) to their population of origin and correspondence analysis further substantiates the existence of a cohesive genetic structure in both the breeds.     

Empirical Distributions of F ST from Large-Scale Human Polymorphism Data

Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright’s F _ST that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-F _ST may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically F _ST analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global F _ST distribution closely follows an exponential distribution. Third, although the overall F _ST distribution is similarly shaped (inverse J), F _ST distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-F _ST of these groups is linear in allele frequency. These results suggest that investigating the extremes of the F _ST distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection.     

Single nucleotide polymorphism discovery in albacore and Atlantic bluefin tuna provides insights into worldwide population structure

The optimal management of the commercially important, but mostly over‐exploited, pelagic tunas, albacore (Thunnus alalunga Bonn., 1788) and Atlantic bluefin tuna (BFT; Thunnus thynnus L., 1758), requires a better understanding of population structure than has been provided by previous molecular methods. Despite numerous studies of both species, their population structures remain controversial. This study reports the development of single nucleotide polymorphisms (SNPs) in albacore and BFT and the application of these SNPs to survey genetic variability across the geographic ranges of these tunas. A total of 616 SNPs were discovered in 35 albacore tuna by comparing sequences of 54 nuclear DNA fragments. A panel of 53 SNPs yielded F_ST values ranging from 0.0 to 0.050 between samples after genotyping 460 albacore collected throughout the distribution of this species. No significant heterogeneity was detected within oceans, but between‐ocean comparisons (Atlantic, Pacific and Indian oceans along with Mediterranean Sea) were significant. Additionally, a 17‐SNP panel was developed in Atlantic BFT by cross‐species amplification in 107 fish. This limited number of SNPs discriminated between samples from the two major spawning areas of Atlantic BFT (F_ST = 0.116). The SNP markers developed in this study can be used to genotype large numbers of fish without the need for standardizing alleles among laboratories.     

Identifying chromosomal selection‐sweep regions in facial eczema selection‐line animals using an ovine 50K‐SNP array

Facial eczema (FE) is a hepato‐mycotoxicosis found mainly in New Zealand sheep and cattle. When genetics was found to be a factor in FE susceptibility, resistant and susceptible selection lines of Romney sheep were established to enable further investigations of this disease trait. Using the Illumina OvineSNP50 BeadChip, we conducted a selection‐sweep experiment on these FE genetic lines. Two analytical methods were used to detect selection signals, namely the Peddrift test (Dodds & McEwan, 1997) and fixation index F_ST (Weir & Hill, 2002). Of 50 975 single nucleotide polymorphism (SNP) markers tested, there were three that showed highly significant allele frequency differences between the resistant and susceptible animals (Peddrift nominal P < 0.000001). These SNP loci are located on chromosomes OAR1, OAR11 and OAR12 that coincide precisely with the three highest genomic F_ST peaks. In addition, there are nine less significant Peddrift SNPs (nominal P ≤ 0.000009) on OAR6 (n = 2), OAR9 (n = 2), OAR12, OAR19 (n = 2), OAR24 and OAR26. In smoothed F_ST (five‐SNP moving average) plots, the five most prominent peaks are on OAR1, OAR6, OAR7, OAR13 and OAR19. Although these smoothed F_ST peaks do not coincide with the three most significant Peddrift SNP loci, two (on OAR6 and OAR19) overlap with the set of less significant Peddrift SNPs above. Of these 12 Peddrift SNPs and five smoothed F_ST regions, none is close to the FE candidate genes catalase and ABCG2; however, two on OAR1 and one on OAR13 fall within suggestive quantitative trait locus regions identified in a previous genome screen experiment. The present studies indicated that there are at least eight genomic regions that underwent a selection sweep in the FE lines.