Differentiating salmon populations at broad and fine geographical scales with microsatellites and single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are appealing genetic markers due to several beneficial attributes, but uncertainty remains about how many of these bi-allelic markers are necessary to have sufficient power to differentiate populations, a task now generally accomplished with highly polymorphic microsatellite markers. In this study, we tested the utility of 37 SNPs and 13 microsatellites for differentiating 29 broadly distributed populations of Chinook salmon ( n  = 2783). Information content of all loci was determined by In and     , and the top 12 markers ranked by In were microsatellites, but the 6 highest, and 7 of the top 10     ranked markers, were SNPs. The mean ratio of random SNPs to random microsatellites ranged from 3.9 to 4.1, but this ratio was consistently reduced when only the most informative loci were included. Individual assignment test accuracy was higher for microsatellites (73.1%) than SNPs (66.6%), and pooling all 50 markers provided the highest accuracy (83.2%). When marker types were combined, as few as 15 of the top ranked loci provided higher assignment accuracy than either microsatellites or SNPs alone. Neighbour-joining dendrograms revealed similar clustering patterns and pairwise tests of population differentiation had nearly identical results with each suite of markers. Statistical tests and simulations indicated that closely related populations were better differentiated by microsatellites than SNPs. Our results indicate that both types of markers are likely to be useful in population genetics studies and that, in some cases, a combination of SNPs and microsatellites may be the most effective suite of loci.     

Comparison of SNPs and microsatellites for assessing the genetic structure of chicken populations

Many studies in human genetics compare informativeness of single‐nucleotide polymorphisms (SNPs) and microsatellites (single sequence repeats; SSR) in genome scans, but it is difficult to transfer the results directly to livestock because of different population structures. The aim of this study was to determine the number of SNPs needed to obtain the same differentiation power as with a given standard set of microsatellites. Eight chicken breeds were genotyped for 29 SSRs and 9216 SNPs. After filtering, only 2931 SNPs remained. The differentiation power was evaluated using two methods: partitioning of the Euclidean distance matrix based on a principal component analysis (PCA) and a Bayesian model‐based clustering approach. Generally, with PCA‐based partitioning, 70 SNPs provide a comparable resolution to 29 SSRs. In model‐based clustering, the similarity coefficient showed significantly higher values between repeated runs for SNPs compared to SSRs. For the membership coefficients, reflecting the proportion to which a fraction segment of the genome belongs to the ith cluster, the highest values were obtained for 29 SSRs and 100 SNPs respectively. With a low number of loci (29 SSRs or ≤100 SNPs), neither marker types could detect the admixture in the Gödöllö Nhx population. Using more than 250 SNPs allowed a more detailed insight into the genetic architecture. Thus, the admixed population could be detected. It is concluded that breed differentiation studies will substantially gain power even with moderate numbers of SNPs.     

Analysis of genetic population structure in Acacia caven (Leguminosae,Mimosoideae), comparing one exploratory and two Bayesian-model-based methods

Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other.     

随机SNP在全基因组关联研究人群分层分析中的应用

在复杂疾病的全基因组关联研究中,人群分层现象会增加结果的假阳性率,因此考虑人群遗传结构、控制人群分层是很有必要的。而在人群分层研究中,使用随机选择的SNP的效果还有待进一步探讨。文章利用HapMap Phase2人群中无关个体的Affymetrix SNP 6.0芯片分型数据,在全基因组上随机均匀选择不同数量的SNP,同时利用f值和Fisher精确检验方法筛选祖先信息标记（Ancestry Informative Markers,AIMs）。然后利用HapMap Phase3中的无关个体的数据,以F-statistics和STRUCTURE分析两种方法评估所选出的不同SNP组合对人群的区分效果。研究发现,随机均匀分布于全基因组的SNP可用于识别人群内部存在的遗传结构。文章进一步提示,在全基因组关联研究中,当没有针对特定人群的AIMs时,可在全基因组上随机选择3000以上均匀分布的SNP来控制人群分层。     

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.     

Use of autosomal loci for clustering individuals and populations of East Asian origin

We studied the genetic relationships among East Asian populations based on allele frequency differences to clarify the relative similarities of East Asian populations with a specific focus on the relationships among the Koreans, the Japanese, and the Chinese populations known to be genetically similar. The goal is to find markers appropriate for differentiating among the specific populations. In this study, no prior data existed for Koreans and the markers were selected to differentiate Chinese and Japanese. We typed, using AB TaqMan assays, single-nucleotide polymorphisms (SNPs) at 43 highly selected mostly independent diallelic sites, on 386 individuals from eight East Asian populations (Han Chinese from San Francisco, Han Chinese from Taiwan, Hakka, Koreans, Japanese, Ami, Atayal, and Cambodians) and one Siberian population (Yakut). We inferred group membership of individuals using a model-based clustering method implemented by the STRUCTURE program and population clustering by using computer programs DISTANCE, NEIGHBOR, LSSEARCH, and DRAWTREE, respectively, calculating genetic distances among populations, calculating neighbor-joining and least-squares trees, and drawing the calculated trees. On average 52% of individuals in the three Chinese groups were assigned into one cluster, and, respectively, 78 and 69% of Koreans and Japanese into a different cluster. Koreans differentiated from the Chinese groups and clustered with the Japanese in the principal component analysis (PCA) and in the best least-squares tree. The majority of Koreans were difficult to distinguish from the Japanese. This study shows that a relatively few highly selected markers can, within limits, differentiate between closely related populations.Electronic Supplementary Material Supplementary material is available for this article at     

Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers

Information about the genetic diversity and population structure in elite breeding material is of fundamental importance for the improvement of crops. The objectives of our study were to (a) examine the population structure and the genetic diversity in elite maize germplasm based on simple sequence repeat (SSR) markers, (b) compare these results with those obtained from single nucleotide polymorphism (SNP) markers, and (c) compare the coancestry coefficient calculated from pedigree records with genetic distance estimates calculated from SSR and SNP markers. Our study was based on 1,537 elite maize inbred lines genotyped with 359 SSR and 8,244 SNP markers. The average number of alleles per locus, of group specific alleles, and the gene diversity (D) were higher for SSRs than for SNPs. Modified Roger’s distance (MRD) estimates and membership probabilities of the STRUCTURE matrices were higher for SSR than for SNP markers but the germplasm organization in four heterotic pools was consistent with STRUCTURE results based on SSRs and SNPs. MRD estimates calculated for the two marker systems were highly correlated (0.87). Our results suggested that the same conclusions regarding the structure and the diversity of heterotic pools could be drawn from both markers types. Furthermore, although our results suggested that the ratio of the number of SSRs and SNPs required to obtain MRD or D estimates with similar precision is not constant across the various precision levels, we propose that between 7 and 11 times more SNPs than SSRs should be used for analyzing population structure and genetic diversity.     

Estimating genome-wide heterozygosity: effects of demographic history and marker type

Heterozygosity–fitness correlations (HFCs) are often used to link individual genetic variation to differences in fitness. However, most studies examining HFCs find weak or no correlations. Here, we derive broad theoretical predictions about how many loci are needed to adequately measure genomic heterozygosity assuming different levels of identity disequilibrium (ID), a proxy for inbreeding. We then evaluate the expected ability to detect HFCs using an empirical data set of 200 microsatellites and 412 single nucleotide polymorphisms (SNPs) genotyped in two populations of bighorn sheep (Ovis canadensis), with different demographic histories. In both populations, heterozygosity was significantly correlated across marker types, although the strength of the correlation was weaker in a native population compared with one founded via translocation and later supplemented with additional individuals. Despite being bi-allelic, SNPs had similar correlations to genome-wide heterozygosity as microsatellites in both populations. For both marker types, this association became stronger and less variable as more markers were considered. Both populations had significant levels of ID; however, estimates were an order of magnitude lower in the native population. As with heterozygosity, SNPs performed similarly to microsatellites, and precision and accuracy of the estimates of ID increased as more loci were considered. Although dependent on the demographic history of the population considered, these results illustrate that genome-wide heterozygosity, and therefore HFCs, are best measured by a large number of markers, a feat now more realistically accomplished with SNPs than microsatellites.     

Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites

Although growing numbers of single nucleotide polymorphisms (SNPs) and microsatellites (short tandem repeat polymorphisms or STRPs) are used to infer population structure, their relative properties in this context remain poorly understood. SNPs and STRPs mutate differently, suggesting multi-locus genotypes at these loci might differ in ability to detect population structure. Here, we use coalescent simulations to measure the power of sets of SNPs and STRPs to identify population structure. To maximize the applicability of our results to empirical studies, we focus on the popular STRUCTURE analysis and evaluate the role of several biological and practical factors in the detection of population structure. We find that: (1) fewer unlinked STRPs than SNPs are needed to detect structure at recent divergence times <0.3 N_e generations; (2) accurate estimation of the number of populations requires many fewer STRPs than SNPs; (3) for both marker types, declines in power due to modest gene flow (N_em=1.0) are largely negated by increasing marker number; (4) variation in the STRP mutational model affects power modestly; (5) SNP haplotypes (θ=1, no recombination) provide power comparable with STRP loci (θ=10); (6) ascertainment schemes that select highly variable STRP or SNP loci increase power to detect structure, though ascertained data may not be suitable to other inference; and (7) when samples are drawn from an admixed population and one of its parent populations, the reduction in power to detect two populations is greater for STRPs than SNPs. These results should assist the design of multi-locus studies to detect population structure in nature.     

A comparison of biallelic markers and microsatellites for the estimation of population and conservation genetic parameters in Atlantic salmon (Salmo salar)

Biallelic markers such as single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms have become increasingly popular markers for various population genetics applications. However, the effort required to develop biallelic markers in nonmodel organisms is still substantial. In this study, we compared the estimation of various population genetic parameters (genetic divergence and structuring, isolation-by-distance, genetic diversity) using a limited number of biallelic markers (in total 7 loci) to those estimated with 14 microsatellite loci in 21 Atlantic salmon (Salmo salar) populations from northern Europe. Pairwise FST values were significantly correlated between biallelic loci and microsatellite datasets, as was overall heterozygosity when both anadromous and nonanadromous populations were analyzed together. However, when the anadromous and nonanadromous samples were analyzed separately, only genetic divergence correlations remained significant. Biallelic markers alone were not sufficient for reliable neighbor-joining clustering of populations but gave highly similar isolation-by-distance signals when compared with microsatellites. Finally, although several population prioritization measures for conservation exhibited significant correlation between different marker types, the specific populations highlighted as being most valuable for conservation purposes varied depending on the marker type and conservation criteria applied. This study demonstrates that a relatively small set of biallelic markers can be sufficient for obtaining concordant results in most of the analyses compared with microsatellites, although estimates of genetic distance are generally more concordant than estimates of genetic diversity. This suggests that a relatively small number of biallelic markers can provide useful information for various population genetic applications. However, we emphasize that the use of much higher number of loci is preferable, especially when the genetic differences between populations are subtle or individual multilocus genotype-based analyses are to be performed.     

Comparing RADseq and microsatellites for estimating genetic diversity and relatedness — Implications for brown trout conservation

The conservation and management of endangered species requires information on their genetic diversity, relatedness and population structure. The main genetic markers applied for these questions are microsatellites and single nucleotide polymorphisms (SNPs), the latter of which remain the more resource demanding approach in most cases. Here, we compare the performance of two approaches, SNPs obtained by restriction‐site‐associated DNA sequencing (RADseq) and 16 DNA microsatellite loci, for estimating genetic diversity, relatedness and genetic differentiation of three, small, geographically close wild brown trout (Salmo trutta) populations and a regionally used hatchery strain. The genetic differentiation, quantified as F_ST, was similar when measured using 16 microsatellites and 4,876 SNPs. Based on both marker types, each brown trout population represented a distinct gene pool with a low level of interbreeding. Analysis of SNPs identified half‐ and full‐siblings with a higher probability than the analysis based on microsatellites, and SNPs outperformed microsatellites in estimating individual‐level multilocus heterozygosity. Overall, the results indicated that moderately polymorphic microsatellites and SNPs from RADseq agreed on estimates of population genetic structure in moderately diverged, small populations, but RADseq outperformed microsatellites for applications that required individual‐level genotype information, such as quantifying relatedness and individual‐level heterozygosity. The results can be applied to other small populations with low or moderate levels of genetic diversity.     

Genetic admixture and population substructure in Guanacaste Costa Rica

The population of Costa Rica (CR) represents an admixture of major continental populations. An investigation of the CR population structure would provide an important foundation for mapping genetic variants underlying common diseases and traits. We conducted an analysis of 1,301 women from the Guanacaste region of CR using 27,904 single nucleotide polymorphisms (SNPs) genotyped on a custom Illumina InfiniumII iSelect chip. The program STRUCTURE was used to compare the CR Guanacaste sample with four continental reference samples, including HapMap Europeans (CEU), East Asians (JPT+CHB), West African Yoruba (YRI), as well as Native Americans (NA) from the Illumina iControl database. Our results show that the CR Guanacaste sample comprises a three-way admixture estimated to be 43% European, 38% Native American and 15% West African. An estimated 4% residual Asian ancestry may be within the error range. Results from principal components analysis reveal a correlation between genetic and geographic distance. The magnitude of linkage disequilibrium (LD) measured by the number of tagging SNPs required to cover the same region in the genome in the CR Guanacaste sample appeared to be weaker than that observed in CEU, JPT+CHB and NA reference samples but stronger than that of the HapMap YRI sample. Based on the clustering pattern observed in both STRUCTURE and principal components analysis, two subpopulations were identified that differ by approximately 20% in LD block size averaged over all LD blocks identified by Haploview. We also show in a simulated association study conducted within the two subpopulations, that the failure to account for population stratification (PS) could lead to a noticeable inflation in the false positive rate. However, we further demonstrate that existing PS adjustment approaches can reduce the inflation to an acceptable level for gene discovery.     

A parsimony estimator of the number of populations from a STRUCTURE‐like analysis

Population genetics model based Bayesian methods have been proposed and widely applied to making unsupervised inference of population structure from a sample of multilocus genotypes. Usually they provide good estimates of the ancestry (or population membership) of sampled individuals by clustering them probabilistically or proportionally into (anonymous) populations. However, they have difficulties in accurately estimating the number of populations (K) represented by the sampled individuals. This study proposed a new ad hoc estimator of K, calculable from the output of a population clustering program such as STRUCTURE or ADMIXTURE. The new criterion, called parsimony index (PI), aims to identify the number of populations (K) which yields consistently the minimal admixture estimates of sampled individuals. Extensive simulated and empirical data were used to compare the accuracy of PI and two popular K estimators based on Pr[X|K] (i.e., the probability of genotype data X given K) and ΔK (i.e., the rate of change of the probability of data as a function of K) calculated from STRUCTURE outputs, and the accuracy of PI and the cross‐validation method calculated from ADMIXTURE outputs. It was shown that PI was more accurate than the other methods consistently in various population structure (e.g., hierarchical island model, different extents of differentiation) and sampling (e.g., unbalanced sample sizes, different marker information contents) scenarios. The ΔK method was more accurate than the Pr[X|K] method only for hierarchically structured or highly inbred populations, and the opposite was true in the other scenarios. The PI method was implemented in a computer program, KFinder, which can be run on all major computer platforms.     

缢蛏5个群体遗传多样性和遗传分化的SNP分析

利用多态性SNP标记,采用SNaPShot方法分析了缢蛏(Sinonovacula constricta)5个群体(山东东营群体DY、浙江乐清群体YQ、福建云霄群体YX、广东湛江群体ZJ及广西钦州群体QZ)的遗传多样性和遗传结构。结果表明, DY、YQ、YX、ZJ和QZ的平均有效等位基因数(Ne)分别是1.754、1.555、1.558、1.533和1.519;平均香农指数(I)为0.605、0.501、0.502、0.489和0.471;平均观测杂合度(Ho)为0.317、0.282、0.282、0.265和0.285;平均期望杂合度(He)为0.423、0.336、0.338、0.325和0.313;平均最小等位基因频率(MAF)为0.336、0.241、0.238、0.234和0.229;缢蛏5个群体具有较高遗传多样性。STRUCTURE分析显示所有的缢蛏个体可以划分为5个聚类簇, 5个群体在每个聚类簇中占比为0.075—0.397,均未聚集到单个聚类簇中;AMOVA分析结果显示,群体间遗传分化系数(Fst)值为–0.0061—0.0829;群体两两之间遗传距离为0.0023—0...     

Outlier analyses to test for local adaptation to breeding grounds in a migratory arctic seabird

Investigating the extent (or the existence) of local adaptation is crucial to understanding how populations adapt. When experiments or fitness measurements are difficult or impossible to perform in natural populations, genomic techniques allow us to investigate local adaptation through the comparison of allele frequencies and outlier loci along environmental clines. The thick‐billed murre (Uria lomvia) is a highly philopatric colonial arctic seabird that occupies a significant environmental gradient, shows marked phenotypic differences among colonies, and has large effective population sizes. To test whether thick‐billed murres from five colonies along the eastern Canadian Arctic coast show genomic signatures of local adaptation to their breeding grounds, we analyzed geographic variation in genome‐wide markers mapped to a newly assembled thick‐billed murre reference genome. We used outlier analyses to detect loci putatively under selection, and clustering analyses to investigate patterns of differentiation based on 2220 genomewide single nucleotide polymorphisms (SNPs) and 137 outlier SNPs. We found no evidence of population structure among colonies using all loci but found population structure based on outliers only, where birds from the two northernmost colonies (Minarets and Prince Leopold) grouped with birds from the southernmost colony (Gannet), and birds from Coats and Akpatok were distinct from all other colonies. Although results from our analyses did not support local adaptation along the latitudinal cline of breeding colonies, outlier loci grouped birds from different colonies according to their non‐breeding distributions, suggesting that outliers may be informative about adaptation and/or demographic connectivity associated with their migration patterns or nonbreeding grounds.     

An empirical comparison of SNPs and microsatellites for parentage and kinship assignment in a wild sockeye salmon (Oncorhynchus nerka) population

Because of their high variability, microsatellites are still considered the marker of choice for studies on parentage and kinship in wild populations. Nevertheless, single nucleotide polymorphisms (SNPs) are becoming increasing popular in many areas of molecular ecology, owing to their high-throughput, easy transferability between laboratories and low genotyping error. An ongoing discussion concerns the relative power of SNPs compared to microsatellites-that is, how many SNP loci are needed to replace a panel of microsatellites? Here, we evaluate the assignment power of 80 SNPs (H(E) = 0.30, 80 independent alleles) and 11 microsatellites (H(E) = 0.85, 192 independent alleles) in a wild population of about 400 sockeye salmon with two commonly used software packages (Cervus3, Colony2) and, for SNPs only, a newly developed software (SNPPIT). Assignment success was higher for SNPs than for microsatellites, especially for parent pairs, irrespective of the method used. Colony2 assigned a larger proportion of offspring to at least one parent than the other methods, although Cervus and SNPPIT detected more parent pairs. Identification of full-sib groups without parental information from relatedness measures was possible using both marker systems, although explicit reconstruction of such groups in Colony2 was impossible for SNPs because of computation time. Our results confirm the applicability of SNPs for parentage analyses and refute the predictability of assignment success from the number of independent alleles.     

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious “populations”. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure

One of the primary goals of population genetics is to succinctly describe genetic relationships among populations, and the computer program STRUCTURE is one of the most frequently used tools for doing so. The mathematical model used by STRUCTURE was designed to sort individuals into Hardy–Weinberg populations, but the program is also frequently used to group individuals from a large number of populations into a small number of clusters that are supposed to represent the main genetic divisions within species. In this study, I used computer simulations to examine how well STRUCTURE accomplishes this latter task. Simulations of populations that had a simple hierarchical history of fragmentation showed that when there were relatively long divergence times within evolutionary lineages, the clusters created by STRUCTURE were frequently not consistent with the evolutionary history of the populations. These difficulties can be attributed to forcing STRUCTURE to place individuals into too few clusters. Simulations also showed that the clusters produced by STRUCTURE can be strongly influenced by variation in sample size. In some circumstances, STRUCTURE simply put all of the individuals from the largest sample in the same cluster. A reanalysis of human population structure suggests that the problems I identified with STRUCTURE in simulations may have obscured relationships among human populations—particularly genetic similarity between Europeans and some African populations.     

Population structure of chum salmon and selection on the markers collected for stock identification

Genetic stock identification (GSI) is a major management tool of Pacific salmon (Oncorhynchus Spp.) that has provided rich genetic baseline data of allozymes, microsatellites, and single‐nucleotide polymorphisms (SNPs) across the Pacific Rim. Here, we analyzed published data sets for adult chum salmon (Oncorhynchus keta), namely 10 microsatellites, 53 SNPs, and a mitochondrial DNA locus (mtDNA3, control region, and NADH‐3 combined) in samples from 495 locations in the same distribution range (n = 61,813). TreeMix analysis of the microsatellite loci identified the greatest convergence toward Japanese/Korean populations and suggested two admixture events from Japan/Korea to Russia and the Alaskan Peninsula. The SNPs had been purposively collected from rapidly evolving genes to increase the power of GSI. The largest expected heterozygosity was observed in Japanese/Korean populations for microsatellites, whereas it was largest in Western Alaskan populations for SNPs, reflecting the SNP discovery process. A regression of SNP population structures on those of microsatellites indicated the selection of the SNP loci according to deviations from the predicted structures. Specifically, we matched the sampling locations of the SNPs with those of the microsatellites and performed regression analyses of SNP allele frequencies on a 2‐dimensional scaling (MDS) of matched locations obtained from microsatellite pairwise F _ST values. The MDS first axis indicated a latitudinal cline in American and Russian populations, whereas the second axis showed differentiation of Japanese/Korean populations. The top five outlier SNPs included mtDNA3, U502241 (unknown), GnRH373, ras1362, and TCP178, which were identified by principal component analysis. We summarized the functions of 53 nuclear genes surrounding SNPs and the mtDNA3 locus by referring to a gene database system and propose how they may influence the fitness of chum salmon.     

Sequence-tagged microsatellite sites as markers in chicken reference and resource populations

Two chicken genomic libraries were screened for the presence of poly(TG/AC) microsatellite tracts. The number of positive clones was low, confirming the low frequency of such micro-satellites in the chicken genome relative to mammalian genomes. Polymorphism of 29 microsatellite tracts, comprising 11 from the library screening and 18 obtained from GenBank, was examined in the East Lansing and Compton reference families, in a resource population formed by a cross between a single White Rock broiler and inbred Leghorn females, and in a panel of birds from five layer stocks. Twenty microsatellites, primarily of the poly(TG/AC) type, were polymorphic in at least one of the populations. Thirteen of the microsatellites were polymorphic in the East Lansing reference family and 13 were also polymorphic in the resource population, confirming that the genetic distance between White Rock and White Leghorn is about as great as between Jungle fowl and White Leghorn. Only six microsatellites were polymorphic in the Compton reference family, formed by a cross between two White Leghorn strains. Twelve of the microsatellites were mapped in the East Lansing and/or Compton reference families. These were well dispersed among the various linkage groups and did not show any indications of terminal clustering.