Sequencing Of Reduced Representation Libraries Research Articles

Development and application of Single Primer Enrichment Technology (SPET) SNP assay for population genomics analysis and candidate gene discovery in lettuce.

Single primer enrichment technology (SPET) is a novel high-throughput genotyping method based on short-read sequencing of specific genomic regions harboring polymorphisms. SPET provides an efficient and reproducible method for genotyping target loci, overcoming the limits associated with other reduced representation library sequencing methods that are based on a random sampling of genomic loci. The possibility to sequence regions surrounding a target SNP allows the discovery of thousands of closely linked, novel SNPs. In this work, we report the design and application of the first SPET panel in lettuce, consisting of 41,547 probes spanning the whole genome and designed to target both coding (~96%) and intergenic (~4%) regions. A total of 81,531 SNPs were surveyed in 160 lettuce accessions originating from a total of 10 countries in Europe, America, and Asia and representing 10 horticultural types. Model ancestry population structure clearly separated the cultivated accessions (Lactuca sativa) from accessions of its presumed wild progenitor (L. serriola), revealing a total of six genetic subgroups that reflected a differentiation based on cultivar typology. Phylogenetic relationships and principal component analysis revealed a clustering of butterhead types and a general differentiation between germplasm originating from Western and Eastern Europe. To determine the potentiality of SPET for gene discovery, we performed genome-wide association analysis for main agricultural traits in L. sativa using six models (GLM naive, MLM, MLMM, CMLM, FarmCPU, and BLINK) to compare their strength and power for association detection. Robust associations were detected for seed color on chromosome 7 at 50 Mbp. Colocalization of association signals was found for outer leaf color and leaf anthocyanin content on chromosome 9 at 152 Mbp and on chromosome 5 at 86 Mbp. The association for bolting time was detected with the GLM, BLINK, and FarmCPU models on chromosome 7 at 164 Mbp. Associations were detected in chromosomal regions previously reported to harbor candidate genes for these traits, thus confirming the effectiveness of SPET for GWAS. Our findings illustrated the strength of SPET for discovering thousands of variable sites toward the dissection of the genomic diversity of germplasm collections, thus allowing a better characterization of lettuce collections.

Population Genomics of the Commercially Important Gulf of Mexico Pink Shrimp Farfantepenaeus duorarum (Burkenroad, 1939) Support Models of Juvenile Transport Around the Florida Peninsula

The Gulf of Mexico pink shrimp, Farfantepenaeus duorarum, supports large fisheries in the United States and Mexico, with nearly 7,000 tons harvested from the region in 2016. Given the commercial importance of this species, management is critical: in 1997, the southern Gulf of Mexico pink shrimp fishery was declared collapsed and mitigation strategies went into effect, with recovery efforts lasting over a decade. Fisheries management can be informed and improved through a better understanding of how factors associated with early life history impact genetic diversity and population structure in the recruited population. Farfantepenaeus duorarum are short-lived, but highly fecund, and display high variability in recruitment patterns. To date, modeling the impacts of ecological, physical, and behavioral factors on juvenile settlement has focused on recruitment of larval individuals of F. duorarum to nursery grounds in Florida Bay. Here, we articulate testable hypotheses stemming from a recent model of larval transport and evaluate support for each with a population genomics approach, generating reduced representation library sequencing data for F. duorarum collected from seven regions around the Florida Peninsula. Our research represents the first and most molecular data-rich study of population structure in F. duorarum in the Gulf and reveals evidence of a differentiated population in the Dry Tortugas. Our approach largely validates a model of larval transport, allowing us to make management-informative inferences about the impacts of spawning location and recruitment patterns on intraspecific genetic diversity. Such inferences improve our understanding of the roles of non-genetic factors in generating and maintaining genetic diversity in a commercially important penaeid shrimp species.

Ancestry informative alleles captured with reduced representation library sequencing in Theobroma cacao.

As the source of chocolate, cacao has become one of the most important crops in the world. The identification of molecular markers to understand the demographic history, genetic diversity and population structure plays a pivotal role in cacao breeding programs. Here, we report the use of a modified genotyping-by-sequencing (GBS) approach for large-scale single nucleotide polymorphism (SNP) discovery and allele ancestry mapping. We identified 12,357 bi-allelic SNPs after filtering, of which, 7,009 variants were ancestry informative. The GBS approach proved to be rapid, cost-effective, and highly informative for ancestry assignment in this species.

Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach

Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four L. salmonis populations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearby L. salmonis populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.

Germinal disc region: an appropriate source for obtaining maternal DNA from eggs

Eggs may serve as an alternative source for DNA extraction. The quality of DNA extracted from eggshell, whole egg liquid (WEL), and germinal disc region (GDR) was compared based on the spectrophotometric, electrophoretic, PCR, and reduced-representation library sequencing (RRLS) results. Although these DNAs were all invisible on the gel and cannot be measured spectrophotometrically, the GDR DNA was superior to the eggshell and WEL DNA in PCR efficiency. After the whole genome amplification (WGA) was introduced, the yield of GDR DNA was significantly increased. The obtaining DNA had overwhelming superiority over the eggshell and WEL DNA in the ratio of captured genome and the number of called SNP. The GDR DNA extraction followed by the WGA provides a method to obtain sufficient DNA from a single egg.

Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia.

BackgroundThe advent of genotyping by Next Generation Sequencing has enabled rapid discovery of thousands of single nucleotide polymorphism (SNP) markers and high throughput genotyping of large populations at an affordable cost. Genotyping by sequencing (GBS), a reduced representation library sequencing method, allows highly multiplexed sequencing of genomic subsets. This method has limitations for small organisms with low amounts of genomic DNA, such as the bluetongue virus (BTV) vectors, Culicoides midges.ResultsThis study employed the GBS method to isolate SNP markers de novo from whole genome amplified Culicoides brevitarsis genomic DNA. The individuals were collected from regions representing two different Australian patterns of BTV strain distribution: the Northern Territory (NT) and the east coast.We isolated 8145 SNPs using GBS. Phylogenetic analysis conducted using the filtered 3263 SNPs revealed the presence of a distinct C. brevitarsis sub-population in the NT and this was confirmed by analysis of mitochondrial DNA. Two loci showed a very strong signal for selection and were unique to the NT population. Bayesian analysis with STRUCTURE indicated a possible two-population cluster.ConclusionsThe results suggest that genotyping vectors with high density markers in combination with biological and environmental data is useful. However, more extensive sampling over a wider spatial and temporal range is needed. The presence of sub-structure in populations and loci under natural selection indicates the need for further investigation of the role of vectors in shaping the two Australian systems of BTV transmission. The described workflow is transferable to genotyping of small, non-model organisms, including arthropod vectors of pathogens of economic and medical importance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3124-1) contains supplementary material, which is available to authorized users.

Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms

BackgroundHigh-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.ResultsWe present an improved RRL (iRRL) protocol that maximizes the generation of homologous DNA sequences, thus achieving improved genotyping-by-sequencing efficiency. Our modifications facilitate generation of single-sample libraries, enabling individual genotype assignments instead of pooled-sample analysis. We sequenced ~1% of the orangutan genome with 41-fold median coverage in 31 wild-born individuals from two populations. SNPs and genotypes were called using three different algorithms. We obtained substantially different SNP datasets depending on the SNP caller. Genotype validations revealed that the Unified Genotyper of the Genome Analysis Toolkit and SAMtools performed significantly better than a caller from CLC Genomics Workbench (CLC). Of all conflicting genotype calls, CLC was only correct in 17% of the cases. Furthermore, conflicting genotypes between two algorithms showed a systematic bias in that one caller almost exclusively assigned heterozygotes, while the other one almost exclusively assigned homozygotes.ConclusionsOur enhanced iRRL approach greatly facilitates genotyping-by-sequencing and thus direct estimates of allele frequencies. Our direct comparison of three commonly used SNP callers emphasizes the need to question the accuracy of SNP and genotype calling, as we obtained considerably different SNP datasets depending on caller algorithms, sequencing depths and filtering criteria. These differences affected scans for signatures of natural selection, but will also exert undue influences on demographic inferences. This study presents the first effort to generate a population genomic dataset for wild-born orangutans with known population provenance.