Thousands Of Single Nucleotide Polymorphisms Research Articles

Concordance rate in cattle and sheep between genotypes differing in Illumina GenCall quality score.

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

Multivariate linear mixed model enhanced the power of identifying genome-wide association to poplar tree heights in a randomized complete block design.

With the advances in high-throughput sequencing technologies, it is not difficult to extract tens of thousands of single-nucleotide polymorphisms (SNPs) across many individuals in a fast and cheap way, making it possible to perform genome-wide association studies (GWAS) of quantitative traits in outbred forest trees. It is very valuable to apply traditional breeding experiments in GWAS for identifying genome variants associated with ecologically and economically important traits in Populus. Here, we reported a GWAS of tree height measured at multiple time points from a randomized complete block design (RCBD), which was established with clones from an F1 hybrid population of Populus deltoides and Populus simonii. A total of 22,670 SNPs across 172 clones in the RCBD were obtained with restriction site-associated DNA sequencing (RADseq) technology. The multivariate mixed linear model was applied by incorporating the pedigree relationship matrix of individuals to test the association of each SNP to the tree heights over 8 time points. Consequently, 41 SNPs were identified significantly associated with the tree height under the P-value threshold determined by Bonferroni correction at the significant level of 0.01. These SNPs were distributed on all but two chromosomes (Chr02 and Chr18) and explained the phenotypic variance ranged from 0.26% to 2.64%, amounting to 63.68% in total. Comparison with previous mapping studies for poplar height as well as the candidate genes of these detected SNPs were also investigated. We therefore showed that the application of multivariate linear mixed model to the longitudinal phenotypic data from the traditional breeding experimental design facilitated to identify far more genome-wide variants for tree height in poplar. The significant SNPs identified in this study would enhance understanding of molecular mechanism for growth traits and would accelerate marker-assisted breeding programs in Populus.

There and Back Again: Reticulate Evolution in Ceratopteris

Examples of reticulate evolution are known from throughout the tree of life, but are particularly common in ferns due to their unique reproductive biology and few prezygotic reproductive barriers. However, untangling the complex evolution history of these groups can be challenging. Often, several different types of data are needed to understand the full story of reticulate evolution; for example, chloroplast markers trace maternal inheritance, while nuclear markers complete the picture by suggesting paternal inheritance. Next generation sequencing can provide thousands of nuclear loci, which are informative for estimating reticulate evolutionary histories. The model fern genus Ceratopteris is known to have cryptic allotetraploid taxa, and hybridization is common between many species in the genus. To better understand the patterns of hybridization and reticulate evolution in the genus, we constructed a split network analysis using thousands of single nucleotide polymorphisms from samples collected throughout the pan-tropical range of Ceratopteris. Our split network organizes taxa based on genomic similarity, revealing potential introgression between lineages. Combining this analysis with Patterson's D to measure gene flow, as well as contributions by previous authors, we show extensive hybridization and reticulate evolution in Old World Ceratopteris, and also provide evidence for natural hybridization events involving the model species C. richardii.

Selection on a small genomic region underpins differentiation in multiple color traits between two warbler species.

Speciation is one of the most important processes in biology, yet the study of the genomic changes underlying this process is in its infancy. North American warbler species Setophaga townsendi and Setophaga occidentalis hybridize in a stable hybrid zone, following a period of geographic separation. Genomic differentiation accumulated during geographic isolation can be homogenized by introgression at secondary contact, whereas genetic regions that cause low hybrid fitness can be shielded from such introgression. Here, we examined the genomic underpinning of speciation by investigating (1) the genetic basis of divergent pigmentation traits between species, (2) variation in differentiation across the genome, and (3) the evidence for selection maintaining differentiation in the pigmentation genes. Using tens of thousands of single nucleotide polymorphisms (SNPs) genotyped in hundreds of individuals within and near the hybrid zone, genome‐wide association mapping revealed a single SNP associated with cheek, crown, breast coloration, and flank streaking, reflecting pleiotropy (one gene affecting multiple traits) or close physical linkage of different genes affecting different traits. This SNP is within an intron of the RALY gene, hence we refer to it as the RALY SNP. We then examined between‐species genomic differentiation, using both genotyping‐by‐sequencing and whole genome sequencing. We found that the RALY SNP is within one of the highest peaks of differentiation, which contains three genes known to influence pigmentation: ASIP, EIF2S2, and RALY (the ASIP‐RALY gene block). Heterozygotes at this gene block are likely of reduced fitness, as the geographic cline of the RALY SNP has been narrow over two decades. Together, these results reflect at least one barrier to gene flow within this narrow (∼200 kb) genomic region that modulates plumage difference between species. Despite extensive gene flow between species across the genome, this study provides evidence that selection on a phenotype‐associated genomic region maintains a stable species boundary.

Taking the next steps to implement polygenic risk scoring for improved risk stratification and primary prevention of coronary artery disease.

Coronary artery disease (CAD) remains the leading cause of death worldwide. The role of hypertension, cholesterol, diabetes mellitus, and smoking in driving disease has been well recognized at a population level and has been the target of primary prevention strategies for over 50 years with substantial impact. However, in many cases, these factors alone do not provide enough precision at the individual level to allow physicians and patients to take appropriate preventive measures and many patients continue to suffer acute coronary syndromes in the absence of these risk factors. Recent advances in user-friendly chip designs, high speed throughput, and economic efficiency of genome-wide association studies complemented by advances in statistical analytical approaches have facilitated the rapid development of polygenic risk scores (PRSs). The latest PRSs combine data regarding hundreds of thousands of single-nucleotide polymorphisms to predict chronic diseases including CAD. Novel CAD PRSs are strong predictors of risk and may have application, in a complementary manner with existing risk prediction algorithms. However, there remain substantial controversies, and ultimately, we need to move forward from observational studies to prospectively and rigorously assess the potential impact if widespread implementation is to be aspired to. Consideration needs to be made of ethnicity, sex, as well as age, and risk estimate based on existing non-genomic algorithms. We provide an overview and commentary on the important advances in deriving and validating PRSs, as well as pragmatic considerations that will be required for implementation of the new knowledge into clinical practice.

High-Quality SNP Linkage Maps Improved QTL Mapping and Genome Assembly in Populus.

With the advances in high-throughput sequencing technologies and the development of new software for extracting single nucleotide polymorphisms (SNPs) across a mapping population, it is possible to construct high-quality genetic maps with thousands of SNPs in outbred forest trees. Two parent-specific linkage maps were constructed with restriction site-associated DNA sequencing data from an F1 hybrid population derived from Populus deltoides and Populus simonii, and applied in QTL mapping and genome assembly. The female P. deltoides map contained 4018 SNPs, which were divided into 19 linkage groups under a wide range of LOD thresholds from 7 to 55. The male P. simonii map showed similar characteristics, consisting of 2097 SNPs, which also belonged to 19 linkage groups under LOD thresholds of 7 to 29. The SNP order of each linkage group was optimal among different ordering results from several available software. Moreover, the linkage maps allowed the detection of 39 QTLs underlying tree height and 47 for diameter at breast height. In addition, the linkage maps improved the anchoring of 689 contigs of P. simonii to chromosomes. The 2 parental genetic maps of Populus are of high quality, especially in terms of SNP data quality, the SNP order within linkage groups, and the perfect match between the number of linkage groups and the karyotype of Populus, as well as the excellent performances in QTL mapping and genome assembly. Both approaches for extracting and ordering SNPs could be applied to other species for constructing high-quality genetic maps.

Detecting Disease-Associated SNP-SNP Interactions Using Progressive Screening Memetic Algorithm.

Hundreds of thousands of single nucleotide polymorphisms (SNPs)are currently available for genome-wide association study (GWAS). Detecting disease-associated SNP-SNP interactions is considered an important way to capture the underlying genetic causes of complex diseases. In the combinatorially explosive search space, evolutionary algorithms are promising in solving this difficult problem because of their controllable time complexity. However, in existing evolutionary algorithms, some possible SNP-SNP interactions are evaluated multiple times by the fitness function. Such reevaluations not only waste computing resources but also make these algorithms easy to fall into local optima. To tackle this drawback, a progressive screening memetic algorithm (PSMA)is proposed in the paper. PSMA first represents all possible SNP-SNP interactions in a constructed graph. Then, the proposed algorithm uses the progressive screening strategy to guarantee that every possible SNP-SNP interaction can only be evaluated once by reducing the constructed graph. Furthermore, two types of local search algorithms are introduced to enhance the detecting power of PSMA. For detecting disease-associated SNP-SNP interactions, experimental results show that our proposed method outperforms other existing state-of-the-art methods in terms of accuracy and time.

Development of a single nucleotide polymorphism array for population genomic studies in four European pine species.

Pines are some of the most ecologically and economically important tree species in the world, and many have enormous natural distributions or have been extensively planted. However, a lack of rapid genotyping capability is hampering progress in understanding the molecular basis of genetic variation in these species. Here, we deliver an efficient tool for genotyping thousands of single nucleotide polymorphism (SNP) markers across the genome that can be applied to genetic studies in pines. Polymorphisms from resequenced candidate genes and transcriptome sequences of P. sylvestris, P. mugo, P. uncinata, P. uliginosa and P. radiata were used to design a 49,829 SNP array (Axiom_PineGAP, Thermo Fisher). Over a third (34.68%) of the unigenes identified from the P. sylvestris transcriptome were represented on the array, which was used to screen samples of four pine species. The conversion rate for the array on all samples was 42% (N=20,795 SNPs) and was similar for SNPs sourced from resequenced candidate gene and transcriptome sequences. The broad representation of gene ontology terms by unigenes containing converted SNPs reflected their coverage across the full transcriptome. Over a quarter of successfully converted SNPs were polymorphic among all species, and the data were successful in discriminating among the species and some individual populations. The SNP array provides a valuable new tool to advance genetic studies in these species and demonstrates the effectiveness of the technology for rapid genotyping in species with large and complex genomes.

Lousy grouse: Comparing evolutionary patterns in Alaska galliform lice to understand host evolution and host-parasite interactions.

Understanding both sides of host–parasite relationships can provide more complete insights into host and parasite biology in natural systems. For example, phylogenetic and population genetic comparisons between a group of hosts and their closely associated parasites can reveal patterns of host dispersal, interspecies interactions, and population structure that might not be evident from host data alone. These comparisons are also useful for understanding factors that drive host–parasite coevolutionary patterns (e.g., codivergence or host switching) over different periods of time. However, few studies have compared the evolutionary histories between multiple groups of parasites from the same group of hosts at a regional geographic scale. Here, we used genomic data to compare phylogenomic and population genomic patterns of Alaska ptarmigan and grouse species (Aves: Tetraoninae) and two genera of their associated feather lice: Lagopoecus and Goniodes. We used whole‐genome sequencing to obtain hundreds of genes and thousands of single‐nucleotide polymorphisms (SNPs) for the lice and double‐digest restriction‐associated DNA sequences to obtain SNPs from Alaska populations of two species of ptarmigan. We found that both genera of lice have some codivergence with their galliform hosts, but these relationships are primarily characterized by host switching and phylogenetic incongruence. Population structure was also uncorrelated between the hosts and lice. These patterns suggest that grouse, and ptarmigan in particular, share habitats and have likely had historical and ongoing dispersal within Alaska. However, the two genera of lice also have sufficient dissimilarities in the relationships with their hosts to suggest there are other factors, such as differences in louse dispersal ability, that shape the evolutionary patterns with their hosts.

Chimpanzee (Pan troglodytes schweinfurthii) Population Spans Multiple Protected Areas in the Albertine Rift

We used mitochondrial DNA to examine gene flow in a region of western Uganda that has received little attention regarding chimpanzee population dynamics. The area is critical to gene flow between isolated Democratic Republic of Congo populations and the rest of East Africa. None of the chimpanzees in each of the 4 protected areas under consideration (Toro-Semliki Wildlife Reserve, Semuliki National Park, Rwenzori Mountains National Park and Itwara Central Forest Reserve) are fully habituated. Therefore, it is not clear whether one or more populations have historically used this fragmented landscape for (1) regular ranging and/or (2) infrequent dispersal. We incorporated the published sequences of the first hypervariable region of the D-loop of the mitochondrial genome from 3 previously sampled sites (n = 39) while also contributing the first extensive genetic sampling of chimpanzees in Toro-Semliki (n = 80). Our goal was to generate a historical baseline model of metapopulation dynamics in this region and determine which, if any, of these protected areas forms a fragmented landscape for a single chimpanzee population. According to a discriminant analysis of principal components, the haplotypes at Toro-Semliki form a central cluster, and Itwara is its nearest genetic neighbor. Rwenzori Mountains National Park is the most distant neighbor of all protected areas. We performed an analysis of molecular variance for 14 different population models that divided the samples from the 4 protected areas into 2, 3 or 4 populations. The best fit model included 3 populations: Toro-Semliki Wildlife Reserve and Itwara Forest Reserve comprised a single population; Semuliki National Park and Rwenzori Mountains National Park formed 2 additional separate populations (variance among = 9%, p = 0.014). The results indicated that some protected areas comprised distinctive populations, while others formed a fragmented landscape for a population’s ranging for foraging purposes. Therefore, the edges of a protected area do not always define a chimpanzee population. We propose a closer examination of those dynamics through renewed sampling. Advances in DNA extraction and next-generation sequencing will allow us to compare thousands of single nucleotide polymorphisms in the genomes of unhabituated chimpanzees living in each of these protected areas.

Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration.

Despite the importance of climate-adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron-rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype-phenotype-environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site-adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.

HiGwas: how to compute longitudinal GWAS data in population designs.

Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package. https://github.com/wzhy2000/higwas. rwu@phs.psu.edu. Supplementary data are available at Bioinformatics online.

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.

Somatic genetic drift and multilevel selection in a clonal seagrass.

All multicellular organisms are genetic mosaics owing to somatic mutations. The accumulation of somatic genetic variation in clonal species undergoing asexual (or clonal) reproduction may lead to phenotypic heterogeneity among autonomous modules (termed ramets). However, the abundance and dynamics of somatic genetic variation under clonal reproduction remain poorly understood. Here we show that branching events in a seagrass (Zostera marina) clone or genet lead to population bottlenecks of tissue that result in the evolution of genetically differentiated ramets in a process of somatic genetic drift. By studying inter-ramet somatic genetic variation, we uncovered thousands of single nucleotide polymorphisms that segregated among ramets. Ultra-deep resequencing of single ramets revealed that the strength of purifying selection on mosaic genetic variation was greater within than among ramets. Our study provides evidence for multiple levels of selection during the evolution of seagrass genets. Somatic genetic drift during clonal propagation leads to the emergence of genetically unique modules that constitute an elementary level of selection and individuality in long-lived clonal species.

Genotyping by multiplexed sequencing (GMS): A customizable platform for genomic selection.

As genotyping technologies continue to evolve, so have their throughput and multiplexing capabilities. In this study, we demonstrate a new PCR-based genotyping technology that multiplexes thousands of single nucleotide polymorphism (SNP) markers with high-throughput capabilities in a simple protocol using a two-step PCR approach. The bioinformatic pipeline is user friendly and yields results that are intuitive to interpret. This method was tested on two recombinant inbred line (RIL) populations that had previous genotyping data from the Illumina Infinium assay for Triticum aestivum L. and the two data sets were found to be 100% in agreement. The genotyping by multiplexed sequencing (GMS) protocol multiplexes 1,656 wheat SNP markers, 207 syntenic barley SNP markers, and 49 known informative markers, which generate a possible 2,433 data points (including homoeoalleles and paralogs). This genotyping approach has the flexibility of being sequenced on either the Ion Torrent or Illumina next generation sequencing (NGS) platforms. Products are the result of direct sequencing and are therefore more reliable than scatter plot analysis which is the output of other genotyping methods such as the Illumina Infinium assay, komeptitive allele specific PCR and other like technologies.

Integrated genome-wide investigations of the housefly, a global vector of diseases reveal unique dispersal patterns and bacterial communities across farms

BackgroundHouseflies (Musca domestica L.) live in intimate association with numerous microorganisms and is a vector of human pathogens. In temperate areas, houseflies will overwinter in environments constructed by humans and recolonize surrounding areas in early summer. However, the dispersal patterns and associated bacteria across season and location are unclear. We used genotyping-by-sequencing (GBS) for the simultaneous identification and genotyping of thousands of Single Nucleotide Polymorphisms (SNPs) to establish dispersal patterns of houseflies across farms. Secondly, we used 16S rRNA gene amplicon sequencing to establish the variation and association between bacterial communities and the housefly across farms.ResultsUsing GBS we identified 18,000 SNPs across 400 individuals sampled within and between 11 dairy farms in Denmark. There was evidence for sub-structuring of Danish housefly populations and with genetic structure that differed across season and sex. Further, there was a strong isolation by distance (IBD) effect, but with large variation suggesting that other hidden geographic barriers are important. Large individual variations were observed in the community structure of the microbiome and it was found to be dependent on location, sex, and collection time. Furthermore, the relative prevalence of putative pathogens was highly dependent on location and collection time.ConclusionWe were able to identify SNPs for the determination of the spatiotemporal housefly genetic structure, and to establish the variation and association between bacterial communities and the housefly across farms using novel next-generation sequencing (NGS) techniques. These results are important for disease prevention given the fine-scale population structure and IBD for the housefly, and that individual houseflies carry location specific bacteria including putative pathogens.

Comparative evaluation of genomic inbreeding parameters in seven commercial and autochthonous pig breeds

Single nucleotide polymorphism (SNP) genotyping tools, which can analyse thousands of SNPs covering the whole genome, have opened new opportunities to estimate the inbreeding level of animals directly using genome information. One of the most commonly used genomic inbreeding measures considers the proportion of the autosomal genome covered by runs of homozygosity (ROH), which are defined as continuous and uninterrupted chromosome portions showing homozygosity at all loci. In this study, we analysed the distribution of ROH in three commercial pig breeds (Italian Large White, n = 1968; Italian Duroc, n = 573; and Italian Landrace, n = 46) and four autochthonous breeds (Apulo-Calabrese, n = 90; Casertana, n = 90; Cinta Senese, n = 38; and Nero Siciliano, n = 48) raised in Italy, using SNP data generated from Illumina PorcineSNP60 BeadChip. We calculated ROH-based inbreeding coefficients (FROH) using ROH of different minimum length (1, 2, 4, 8, 16 Mbp) and compared them with several other genomic inbreeding coefficients (including the difference between observed and expected number of homozygous genotypes (FHOM)) and correlated all these genomic-based measures with the pedigree inbreeding coefficient (FPED) calculated for the pigs of some of these breeds. Autochthonous breeds had larger mean size of ROH than all three commercial breeds. FHOM was highly correlated (0.671 to 0.985) with FROH measures in all breeds. Apulo-Calabrese and Casertana had the highest FROH values considering all ROH minimum lengths (ranging from 0.273 to 0.189 and from 0.226 to 0.152, moving from ROH of minimum size of 1 Mbp (FROH1) to 16 Mbp (FROH16)), whereas the lowest FROH values were for Nero Siciliano (from 0.072 to 0.051) and Italian Large White (from 0.117 to 0.042). FROH decreased as the minimum length of ROH increased for all breeds. Italian Duroc had the highest correlations between all FROH measures and FPED (from 0.514 to 0.523) and between FHOM and FPED (0.485). Among all analysed breeds, Cinta Senese had the lowest correlation between FROH and FPED. This might be due to the imperfect measure of FPED, which, mainly in local breeds raised in extensive production systems, cannot consider a higher level of pedigree errors and a potential higher relatedness of the founder population. It appeared that ROH better captured inbreeding information in the analysed breeds and could complement pedigree-based inbreeding coefficients for the management of these genetic resources.

101 National Center for Applied Reproduction and Genomics (NCARG) evaluates accuracy of genomic prediction in commercial Angus cattle

Abstract One of the goals of the National Center of Applied Reproduction and Genomics (NCARG) is to demonstrate in real-world settings the potential of new technologies. To this end, we evaluated the predictive ability of the Zoetis GeneMax Advantage genomic prediction. The GeneMax Advantage test analyzes tens of thousands of single nucleotide polymorphisms (SNP) to predict the genetic potential of a commercial Angus female. Genetic predictions are provided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, and Yield. Indices of economically important traits are estimated on an index score (1–100 scale) and are reported in three indices; Cow Advantage Index, Feeder Advantage Index, and Total Advantage Index. Producers can use the scores and indices to make selection, culling, and mating decisions. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center was utilized to analyze Weaning Weight, Milk, Marbling, Fat Thickness, Ribeye Area and Carcass Weight. Progeny phenotypic data was matched to the respective dam, and the genomic predictions were then compared to the phenotypic data using correlation and linear models in R software. Linear models accounted for differences in sex, birth year, and the random effect of sire. Interestingly, all genomic predictions had correlations with progeny phenotypes that were significantly different from zero (P-value &amp;lt; 0.05). Likewise in the linear models, genomic predictions for all analyzed traits were significantly associated with calf performance (Table 1). Academics, farmers and ranchers, and extension professionals can trust the effectiveness of GeneMax Advantage genomic predictions in commercial Angus cattle.

Haplotypes-based genetic analysis: benefits and challenges

The increasing availability of Single Nucleotide Polymorphisms (SNPs) discovered by Next Generation Sequencing will enable a range of new genetic analyses in crops, which was not possible before. Concomitantly, researchers will face the challenge of handling large data sets at the whole-genome level. By grouping thousands of SNPs into a few hundred haplotype blocks, complexity of the data can be reduced with fewer statistical tests and a lower probability of spurious associations. Owing to the strong genome structure present in breeding lines of most crops, the deployment of haplotypes could be a powerful complement to improve efficiency of marker-assisted and genomic selection. This review describes in brief the commonly used approaches to construct haplotype blocks and some examples in animals and crops are cited where haplotype-based dissection of traits were proven beneficial. Some important considerations and facts while working with haplotypes in crops are reviewed at the end.

Exome resequencing and GWAS for growth, ecophysiology, and chemical and metabolomic composition of wood of Populus trichocarpa

BackgroundPopulus trichocarpa is an important forest tree species for the generation of lignocellulosic ethanol. Understanding the genomic basis of biomass production and chemical composition of wood is fundamental in supporting genetic improvement programs. Considerable variation has been observed in this species for complex traits related to growth, phenology, ecophysiology and wood chemistry. Those traits are influenced by both polygenic control and environmental effects, and their genome architecture and regulation are only partially understood. Genome wide association studies (GWAS) represent an approach to advance that aim using thousands of single nucleotide polymorphisms (SNPs). Genotyping using exome capture methodologies represent an efficient approach to identify specific functional regions of genomes underlying phenotypic variation.ResultsWe identified 813 K SNPs, which were utilized for genotyping 461 P. trichocarpa clones, representing 101 provenances collected from Oregon and Washington, and established in California. A GWAS performed on 20 traits, considering single SNP-marker tests identified a variable number of significant SNPs (p-value < 6.1479E-8) in association with diameter, height, leaf carbon and nitrogen contents, and δ15N. The number of significant SNPs ranged from 2 to 220 per trait. Additionally, multiple-marker analyses by sliding-windows tests detected between 6 and 192 significant windows for the analyzed traits. The significant SNPs resided within genes that encode proteins belonging to different functional classes as such protein synthesis, energy/metabolism and DNA/RNA metabolism, among others.ConclusionsSNP-markers within genes associated with traits of importance for biomass production were detected. They contribute to characterize the genomic architecture of P. trichocarpa biomass required to support the development and application of marker breeding technologies.