Thousands Of Single Nucleotide Polymorphism Markers Research Articles

Mid‐Pleistocene events influenced the current spatial structure of genetic diversity in Scots pine (Pinus sylvestris L.)

AbstractScots pine (Pinus sylvestris L.) is one of the most important tree species of the temperate and boreal zones in Eurasia. Its wide distribution range and current patterns of genetic variation have been influenced by Quaternary climatic oscillations and the demographic processes connected to them. In order to better understand the relationship between evolutionary history and demographic factors in a widespread species with a large genome, we used the single‐nucleotide polymorphism (SNP) array to genotype thousands of SNP markers across 62 natural populations (N = 686 trees) of Scots pine in Eurasia. This provides the largest range‐wide SNPs' genetic diversity assessment of Scots pine to date. Our findings show evidence of past admixture events between genetic clusters that were retained despite the potential for effective pollen‐mediated gene flow across the species' distribution range. We also examined the contemporary population structure of the species and analyzed the range‐wide genetic diversity patterns. Phylogenetic analyses and demographic modeling suggest that the observed divergence patterns between genetic lineages likely predate the last glaciation events. Two of the most distinctive groups are represented by trees from the eastern parts of Fennoscandia and Eastern Russia, which have remained separated since the mid‐Pleistocene. The patterns of genetic variation also confirm the dual colonization of Fennoscandia and the existence of an admixture zone in Central Europe that was formed during multiple waves of postglacial recolonization. This study provides insights into the genetic relationships of Scots pine populations from Europe and Asia and offers a more comprehensive understanding of the species' history.

Using molecular markers in breeding: ornamentals catch up

Thanks to advances in next generation sequencing it is now straightforward to develop tens or even hundreds of thousands of SNP markers. Advances in genotyping technology have made it feasible to genotype progenies of crosses, panels of genotypes, or even a complete breeding program, by using arrays with tens of thousands of SNPs, or by random or targeted sequencing technologies. Recently software has been developed for dosage scoring and linkage mapping in polyploid crops. This means that advanced genetic analyses can now also be performed in many polyploid ornamentals. A DNA marker, such as a single nucleotide polymorphism (SNP), linked to a trait enables following a gene or allele during crosses and in a breeding program. Association of a SNP marker to a trait or a component of a trait may be done through QTL analysis in segregating populations, by genome-wide association analysis (GWAS) in a set of accessions, or through an analysis across a pedigree. In these analyses, a dense linkage map is a very important tool, to delineate and possibly narrow down the QTL interval and to filter away false positive SNPs. New developments in linkage mapping include paying attention to even marker coverage, and new ways to use markers to infer haplotypes. The latter is especially important in polyploids, in populations with multiple parents, or in wide panels used for association studies. In such cases multiple functional alleles may segregate simultaneously, that cannot all be tagged uniquely by single biallelic SNP markers.

Development and Application of Single‐Nucleotide Polymorphism (SNP) Genetic Markers for Conservation Monitoring of Burbot Populations

AbstractThe transboundary (Idaho, USA; and British Columbia, Canada) population of Burbot Lota lota native to the Kootenai River basin once provided a popular sport and commercial fishery and has been culturally significant to the Kootenai Tribe of Idaho for millennia. However, the population has experienced significant declines over the last 30 years, due primarily to habitat loss and alteration caused by water storage and diversion. By the late 1990s, the population was considered functionally extinct, with estimates of fewer than 50 Burbot in the wild and little to no recruitment, prompting an ongoing international recovery effort. As part of these recovery efforts, managers have been actively developing a hatchery supplementation program to rebuild the population and support future tribal subsistence harvest and recreational fisheries. Although supplementation breeding programs have the potential to rapidly rebuild depleted natural populations, careful genetic management is critical. To monitor genetic diversity and potential inbreeding in the broodstock and to provide parentage‐based tagging of supplementation offspring, we developed a set (N = 96) of highly variable single‐nucleotide polymorphism (SNP) genetic markers. The subset of 96 SNP markers was developed from a larger suite of 6,517 SNPs that were discovered by using restriction site‐associated DNA sequencing. This cost‐efficient technology allows for the rapid discovery of thousands of SNP markers in species that have not been extensively studied previously or for which there are little existing DNA sequence data. We demonstrated high accuracy (>99%) of our SNP set for parentage and individual identification through simulated and empirical tests. The SNP marker set provides a powerful new tool for managing broodstock and for monitoring and genetically tagging Burbot to track the growth, survival, and movement of released individuals.

De novo SNP discovery and strong genetic structuring between upstream and downstream populations of Paratya australiensis Kemp, 1917 (Decapoda: Caridea: Atyidae)

The Australian glass shrimp, Paratya australiensis Kemp, 1917 (Atyidae) has been used in population genetics studies in freshwater ecosystems. There are nevertheless no genome wide markers and no reference genome developed for this species. We used the next generation sequencing (NGS) technique, restriction site associated DNA sequencing (RAD-seq) to genotype thousands of single nucleotide polymorphism (SNP) markers across the genome of P. australiensis. A total of 47 individuals from Booloumba Creek in the Mary River catchment, Conondale Range, Queensland, Australia were sequenced using a double digest RAD-seq approach. De novo assembly produced a total of 476,055 tags in the catalog. Due to missing data, a few individuals were filtered out and 2,330 loci were genotyped from 39 individuals. Upstream genetic diversity (Hexp = 0.2258, average across loci) was slightly lower than downstream diversity (Hexp = 0.2422, average across loci). PCA and admixture analysis showed clear divergence between the two populations, whereas a Bayesian tree indicated that only a single mitochondrial DNA (mtDNA) lineage was present in the stream. Due to long-term isolation and very little dispersal, the populations have diverged significantly at nuclear loci, although divergence was minimal at the mtDNA COI fragment analysed. This study facilitated evaluation of new markers in this species, as well as highlighting genetic structuring and diversity between populations of a stream using a large data set (thousands of SNP markers) compared to few markers used in previous studies.

An SNP-based saturated genetic map and QTL analysis of fruit-related traits in Zucchini using Genotyping-by-sequencing

BackgroundCucurbita pepo is a cucurbit with growing economic importance worldwide. Zucchini morphotype is the most important within this highly variable species. Recently, transcriptome and Simple Sequence Repeat (SSR)- and Single Nucleotide Polymorphism (SNP)-based medium density maps have been reported, however further genomic tools are needed for efficient molecular breeding in the species. Our objective is to combine currently available complete transcriptomes and the Zucchini genome sequence with high throughput genotyping methods, mapping population development and extensive phenotyping to facilitate the advance of genomic research in this species.ResultsWe report the Genotyping-by-sequencing analysis of a RIL population developed from the inter subspecific cross Zucchini x Scallop (ssp. pepo x ssp. ovifera). Several thousands of SNP markers were identified and genotyped, followed by the construction of a high-density linkage map based on 7,718 SNPs (average of 386 markers/linkage group) covering 2,817.6 cM of the whole genome, which is a great improvement with respect to previous maps. A QTL analysis was performed using phenotypic data obtained from the RIL population from three environments. In total, 48 consistent QTLs for vine, flowering and fruit quality traits were detected on the basis of a multiple-environment analysis, distributed in 33 independent positions in 15 LGs, and each QTL explained 1.5–62.9% of the phenotypic variance. Eight major QTLs, which could explain greater than 20% of the phenotypic variation were detected and the underlying candidate genes identified.ConclusionsHere we report the first SNP saturated map in the species, anchored to the physical map. Additionally, several consistent QTLs related to early flowering, fruit shape and length, and rind and flesh color are reported as well as candidate genes for them. This information will enhance molecular breeding in C. pepo and will assist the gene cloning underlying the studied QTLs, helping to reveal the genetic basis of the studied processes in squash.

Using Genome-Wide SNP Discovery and Genotyping to Reveal the Main Source of Population Differentiation in Nothofagus dombeyi (Mirb.) Oerst. in Chile.

Within a woody plant species, environmental heterogeneity has the potential to influence the distribution of genetic variation among populations through several evolutionary processes. In some species, a relationship between environmental characteristics and the distribution of genotypes can be detected, showing the importance of natural selection as the main source of differentiation. Nothofagus dombeyi (Mirb.) Oerst. (Nothofagaceae) is an endemic tree species occurring both in Chile and in Argentina temperate forests. Postglacial history has been studied with chloroplast DNA and evolutionary forces shaping genetic variation patterns have been analysed with isozymes but fine-scale genetic diversity studies are needed. The study of demographic and selection histories in Nothofagus dombeyi requires more informative markers such as single nucleotide polymorphisms (SNP). Genotyping-by-Sequencing tools now allow studying thousands of SNP markers at reasonable prices in nonmodel species. We investigated more than 10 K SNP loci for signatures of local adaptation and showed that interrogation of genomic resources can identify shifts in genetic diversity and putative adaptive signals in this nonmodel woody species.

Performance of a blockwise approach in variable selection using linkage disequilibrium information.

BackgroundGenome-wide association studies (GWAS) aim at finding genetic markers that are significantly associated with a phenotype of interest. Single nucleotide polymorphism (SNP) data from the entire genome are collected for many thousands of SNP markers, leading to high-dimensional regression problems where the number of predictors greatly exceeds the number of observations. Moreover, these predictors are statistically dependent, in particular due to linkage disequilibrium (LD).We propose a three-step approach that explicitly takes advantage of the grouping structure induced by LD in order to identify common variants which may have been missed by single marker analyses (SMA). In the first step, we perform a hierarchical clustering of SNPs with an adjacency constraint using LD as a similarity measure. In the second step, we apply a model selection approach to the obtained hierarchy in order to define LD blocks. Finally, we perform Group Lasso regression on the inferred LD blocks. We investigate the efficiency of this approach compared to state-of-the art regression methods: haplotype association tests, SMA, and Lasso and Elastic-Net regressions.ResultsOur results on simulated data show that the proposed method performs better than state-of-the-art approaches as soon as the number of causal SNPs within an LD block exceeds 2. Our results on semi-simulated data and a previously published HIV data set illustrate the relevance of the proposed method and its robustness to a real LD structure. The method is implemented in the R package BALD (Blockwise Approach using Linkage Disequilibrium), available from http://www.math-evry.cnrs.fr/publications/logiciels.ConclusionsOur results show that the proposed method is efficient not only at the level of LD blocks by inferring well the underlying block structure but also at the level of individual SNPs. Thus, this study demonstrates the importance of tailored integration of biological knowledge in high-dimensional genomic studies such as GWAS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0556-6) contains supplementary material, which is available to authorized users.

Genetic structure of the four wild tomato species in the Solanum peruvianum s.l. species complex.

The most diverse wild tomato species Solanum peruvianum sensu lato (s.l.) has been reclassified into four separate species: Solanum peruvianum sensu stricto (s.s.), Solanum corneliomuelleri, Solanum huaylasense, and Solanum arcanum. However, reproductive barriers among the species are incomplete and this can lead to discrepancies regarding genetic identity of germplasm. We used genotyping by sequencing (GBS) of S. peruvianum s.l., Solanum neorickii, and Solanum chmielewskii to develop tens of thousands of mapped single nucleotide polymorphisms (SNPs) to analyze genetic relationships within and among species. The data set was condensed to 14,043 SNPs with no missing data across 46 sampled plants. Origins of accessions were mapped using geographical information systems (GIS). Isolation by distance, pairwise genetic distances, and number of clusters were estimated using population genetics approaches. Isolation by distance was strongly supported, especially between interspecific pairs. Eriopersicon (S. peruvianum s.s., S. corneliomuelleri, S. huaylasense) and Arcanum (S. arcanum, S. neorickii, S. chmielewskii) species groups were genetically distinct, except for S. huaylasense which showed 50% membership proportions in each group. Solanum peruvianum and S. corneliomuelleri were not significantly differentiated from each other. Many thousands of SNP markers were identified that could potentially be used to distinguish pairs of species, including S. peruvianum versus S. corneliomuelleri, if they are verified on larger numbers of samples. Diagnostic markers will be valuable for delimiting morphologically similar and interfertile species in germplasm management. Approximately 12% of the SNPs rejected a genome-wide test of selective neutrality based on differentiation among species of S. peruvianum s.l. These are candidates for more comprehensive studies of microevolutionary processes within this species complex.

Block-based Bayesian epistasis association mapping with application to WTCCC type 1 diabetes data

Interactions among multiple genes across the genome may contribute to the risks of many complex human diseases. Whole-genome single nucleotide polymorphisms (SNPs) data collected for many thousands of SNP markers from thousands of individuals under the case-control design promise to shed light on our understanding of such interactions. However, nearby SNPs are highly correlated due to linkage disequilibrium (LD) and the number of possible interactions is too large for exhaustive evaluation. We propose a novel Bayesian method for simultaneously partitioning SNPs into LD-blocks and selecting SNPs within blocks that are associated with the disease, either individually or interactively with other SNPs. When applied to homogeneous population data, the method gives posterior probabilities for LD-block boundaries, which not only result in accurate block partitions of SNPs, but also provide measures of partition uncertainty. When applied to case-control data for association mapping, the method implicitly filters out SNP associations created merely by LD with disease loci within the same blocks. Simulation study showed that this approach is more powerful in detecting multi-locus associations than other methods we tested, including one of ours. When applied to the WTCCC type 1 diabetes data, the method identified many previously known T1D associated genes, including PTPN22, CTLA4, MHC, and IL2RA. The method also revealed some interesting two-way associations that are undetected by single SNP methods. Most of the significant associations are located within the MHC region. Our analysis showed that the MHC SNPs form long-distance joint associations over several known recombination hotspots. By controlling the haplotypes of the MHC class II region, we identified additional associations in both MHC class I (HLA-A, HLA-B) and class III regions (BAT1). We also observed significant interactions between genes PRSS16, ZNF184 in the extended MHC region and the MHC class II genes. The proposed method can be broadly applied to the classification problem with correlated discrete covariates.

Genome-wide association studies in cancer--current and future directions

Genome-wide association studies (GWAS) have emerged as an important tool for discovering regions of the genome that harbor genetic variants that confer risk for different types of cancers. The success of GWAS in the last 3 years is due to the convergence of new technologies that can genotype hundreds of thousands of single-nucleotide polymorphism markers together with comprehensive annotation of genetic variation. This approach has provided the opportunity to scan across the genome in a sufficiently large set of cases and controls without a set of prior hypotheses in search of susceptibility alleles with low effect sizes. Generally, the susceptibility alleles discovered thus far are common, namely, with a frequency in one or more population of >10% and each allele confers a small contribution to the overall risk for the disease. For nearly all regions conclusively identified by GWAS, the per allele effect sizes estimated are <1.3. Consequently, the findings of GWAS underscore the complex nature of cancer and have focused attention on a subset of the genetic variants that comprise the genomic architecture of each type of cancer, which already can differ substantially by the number of regions associated with specific types of cancer. For instance, in prostate cancer, there could be >30 distinct regions harboring common susceptibility alleles identified by GWAS, whereas in lung cancer, a disease strongly driven by exposure to tobacco products, so far, only three regions have been conclusively established. To date, >85 regions have been conclusively associated in over a dozen different cancers, yet no more than five regions have been associated with more than one distinct cancer type. GWAS are an important discovery tool that require extensive follow-up to map each region, investigate the biological mechanism underpinning the association and eventually test the optimal markers for assessing risk for a disease or its outcome, such as in pharmacogenomics, the study of the effect of genetic variation on pharmacological interventions. The success of GWAS has opened new horizons for exploration and highlighted the complex genomic architecture of disease susceptibility.

The combined effect of SNP‐marker and phenotype attributes in genome‐wide association studies

The last decade has seen rapid improvements in high-throughput single nucleotide polymorphism (SNP) genotyping technologies that have consequently made genome-wide association studies (GWAS) possible. With tens to hundreds of thousands of SNP markers being tested simultaneously in GWAS, it is imperative to appropriately pre-process, or filter out, those SNPs that may lead to false associations. This paper explores the relationships between various SNP genotype and phenotype attributes and their effects on false associations. We show that (i) uniformly distributed ordinal data as well as binary data are more easily influenced, though not necessarily negatively, by differences in various SNP attributes compared with normally distributed data; (ii) filtering SNPs on minor allele frequency (MAF) and extent of Hardy–Weinberg equilibrium (HWE) deviation has little effect on the overall false positive rate; (iii) in some cases, filtering on MAF only serves to exclude SNPs from the analysis without reduction of the overall proportion of false associations; and (iv) HWE, MAF and heterozygosity are all dependent on minor genotype frequency, a newly proposed measure for genotype integrity.

DNA analysis by fluorescence quenching detection.

The analysis of human genetic variations such as single nucleotide polymorphisms (SNPs) has great applications in genome-wide association studies of complex genetic traits. We have developed an SNP genotyping method based on the primer extension assay with fluorescence quenching as the detection. The template-directed dye-terminator incorporation with fluorescence quenching detection (FQ-TDI) assay is based on the observation that the intensity of fluorescent dye R110- and R6G-labeled acycloterminators is universally quenched once they are incorporated onto a DNA oligonucleotide primer. By comparing the rate of fluorescence quenching of the two allelic dyes in real time, we have extended this method for allele frequency estimation of SNPs in pooled DNA samples. The kinetic FQ-TDI assay is highly accurate and reproducible both in genotyping and in allele frequency estimation. Allele frequencies estimated by the kinetic FQ-TDI assay correlated well with known allele frequencies, with an r(2) value of 0.993. Applying this strategy to large-scale studies will greatly reduce the time and cost for genotyping hundreds and thousands of SNP markers between affected and control populations.