Marker Density Research Articles

Genetic mapping using 1.4M SNP array refined loci for fatty acid composition traits in Chinese Erhualian and Bamaxiang pigs.

Chinese indigenous pigs display marked genetic and phenotypic differences compared with western commercial pigs. In this study, we tested the association between 660K SNPs and longissimus muscle fatty acid composition traits in Chinese Erhualian (n=331) and Bamaxiang (n=315) pigs based on a customized 1.4 million SNP array. We identified a total of 64 significant associations for 20 fatty acid composition traits at the p-value threshold of 1×10-6 among which 42 associations in low linkage disequilibrium (r2 <.2) with previously reported loci were considered novel. We substantially improved the strength and precision of the associations at four previously detected loci near FADS2, ELOVL7, ELOVL6 and FASN genes, facilitating follow-up candidate gene studies. Moreover, we also identified loci near ABCD2, ACSBG1, ELOVL5, HPGDS, DAGT2, ACAD10 and ACSL1 genes with function relevant to metabolism of fatty acids. In this study, valuable genetic variants and candidate genes associated with fatty acid composition traits were identified in Erhualian and Bamaxiang pigs. Some identified loci could be used to improve pork nutrition in pig breeding practice. Using the SNP array with higher marker density and less ascertainment bias improved QTL detection power and precision in Chinese indigenous pigs.

Genomic differentiation as a tool for single nucleotide polymorphism prioritization for Genome wide association and phenotype prediction in livestock

Abstract Genome-wide association studies (GWAS) have been successful in detecting associations between single nucleotide polymorphisms (SNPs) and phenotypic variation and in identifying several causative mutations. However, SNPs with significant association identified using GWAS tend to explain only small fraction of the phenotypic variations. GWAS are affected by lack of power due to small sample size, large numbers of highly correlated markers, and the moderate to small effects of most quantitative trait loci (QTLs). This situation is further complicated by the continuous increase in marker density, especially with the availability of next-generation sequencing (NGS) data. The latter generates an unprecedented number of marker variants, with a complex linkage disequilibrium (LD) structure limiting the advantage and adequacy of existing methods that internally try to prioritize (filter) SNPs (e.g. BayesB, and BayesR). Consequently, it is becoming necessary to either filter SNPs before conducting the association analysis or to enlist additional sources of information. Methods that include biological prior information (e.g. BayesRC) are limited by the amount and quality of available prior information. Knowledge of genetic diversity based on evolutionary forces is beneficial for tracking loci influenced by selection. The fixation index (F ST ), as a measure of allele frequency variation among sub-populations, provides a tool to reveal genomic regions under selection pressure. In order to evaluate its usefulness as an additional source of information, a simulation was carried out. A trait with heritability of 0.4 was simulated and three subpopulations were created based on the empirical phenotypic distribution ( 95% quantile; and between 5% and 95% quantiles). Marker data was simulated to mimic a bovine chip of 600 K, 1 million, and 3 million SNP marker panels. Genetic complexity of the trait was modelled by the number of QTLs, their distribution, and the magnitude of their effects. Using different empirical cut off values for F ST , most QTLs were correctly detected using as few as 2.5% of SNP markers in the panels. Furthermore, the genomic similarity, calculated based on the selected SNPs, was very high (>0.80) for individuals with similar genetic and phenotypic values despite having limited to no pedigree relationship. These results indicate that filtering SNPs using F ST could be beneficial for use in GWAS by focusing on genome regions under selection pressure. High functional genomic similarity based on selected markers indicates similarity in SNP signatures, regardless of relatedness, and translates into high phenotypic correlation that could be used in decision making.

Whole-genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations.

Whole-genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved or resolved haplotypes, the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled-individual DNA (Pool-seq) and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in nonmodel species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g., structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently, though, no single WGR approach fulfils all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many nonmodel species and fields including conservation biology.

Constructing high-density genetic maps for polyploid sugarcane (Saccharum spp.) and identifying quantitative trait loci controlling brown rust resistance

Sugarcane (Saccharum spp.) is an important economic crop for producing edible sugar and bioethanol. Brown rust has long been a major disease impacting sugarcane production worldwide. Resistance resource and markers linked to resistance are valuable tools for disease resistance improvement. An F1 segregating population derived from a cross between two hybrid sugarcane clones, brown rust-susceptible CP95-1039 and brown rust-resistant CP88-1762, were genotyped using genotyping by sequencing approach and also phenotyped in a replicated field trial. Single nucleotide polymorphism (SNP) and presence/absence markers were called with seven different pipelines to maximize reliable marker identification. High-density maps were constructed for both parental clones with a total map length of 4224.4 cM, and a marker density of one marker per 1.7 cM for CP95-1039, and a total map length of 4373.2 cM, and one marker per 2.0 cM for CP88-1762. Among the seven SNP callers, Tassel and Genome Analysis ToolKit performed better than other callers in single-dose SNP detection and contribution to genetic maps. Two major quantitative trait loci (QTL) controlling brown rust resistance were identified, which can explain 21 and 30% of the phenotypic variation, respectively. The genetic maps generated here will improve our understanding of sugarcane’s complex genome structure and discovery of underlying sequence variations controlling agronomic traits. The putative QTL controlling brown rust resistance can effectively be utilized in sugarcane breeding programs to expedite the selection process of brown rust resistance after validation.

Genome wide association study of two phenology traits (flowering time and maturity date) in apple

The aim of Genome Wide Association Studies (GWAS) is to identify markers in tight linkage disequilibrium with loci controlling quantitative trait variation. These markers can then be used in marker-assisted selection (MAS) in fruit crops such as apple. The GWAS approach involves both phenotyping of a large population of mostly unrelated individuals for the traits of interest, and genotyping at high marker density. In the EU-FP7 project FruitBreedomics, almost 1,200 European diploid dessert apple accessions (old and/or local cultivars) from six germplasm collections were genotyped with the Affymetrix Axiom_Apple480K array (487,000 SNPs). Phenotypic data on a large number of traits have been gathered during the project. Here we focus on flowering period and harvesting date. Knowledge of the genetic control of these traits is necessary to develop cultivars that can face the challenges imposed by global climate change and to target cultivar development as a function of a prolonged vegetation period in the production regions. Different models were tested, including control for effects of population structure and relatedness between cultivars. The full model, controlling for both structure and relatedness, was shown to be the most appropriate to avoid spurious marker-trait associations. When analyzing data over all collections, one significant marker-trait association was obtained for each trait, on chromosomes 9 and 3, for flowering period and harvesting date, respectively. Thereby, genomic locations previously identified in bi-parental populations could now be confirmed for a genetically diverse germplasm.

Discovering peach QTLs with multiple progeny analysis

Although many peach QTLs responsible for the variability of traits have been identified and published so far, the number of molecular markers currently used in breeding is still limited. One of the reasons is the large QTL intervals produced, in part, by the limited progeny size. Here we report a QTL mapping approach that enlarges the progeny size by analyzing jointly multiple progenies. Our analysis included 1467 individuals from eighteen Prunus progenies, 13 from intra-specific crosses and five inter-specific between peach and closely related species. The progenies were grown in five locations, with no duplication between orchards. Data from phenology, tree, flower and fruit traits, fruit quality and yield measured in different locations and years were subjected to different standardization methods and integrated in a single data file. The populations were genotyped with the 9K SNP Illumina array, which increased considerably the marker density compared to previous studies. The QTL analysis was conducted with FlexQTLTM software. Here we describe and discuss the preliminary QTLs obtained for some of the traits analyzed (maturity date, percentage of red skin color and soluble solid content). The identification of donors of favorable alleles will represent an important tool for marker-assisted breeding. This study has been conducted in the frame of the Fruit Breedomics European project.

Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes.

Today, genomic prediction (GP) is an established technology in plant and animal breeding programs. Current standard methods are purely based on statistical considerations but do not make use of the abundant biological knowledge, which is easily available from public databases. Major questions that have to be answered before biological prior information can be used routinely in GP approaches are which types of information can be used, and at which points they can be incorporated into prediction methods. In this study, we propose a novel strategy to incorporate gene annotation into GP of complex phenotypes by defining haploblocks according to gene positions. Haplotype effects are then modeled as categorical or as numerical allele dosage variables. The underlying concept of this approach is to build the statistical model on variables representing the biologically functional units. We evaluate the new methods with data from a heterogeneous stock mouse population, the Drosophila Genetic Reference Panel (DGRP), and a rice breeding population from the Rice Diversity Panel. Our results show that using gene annotation to define haploblocks often leads to a comparable, but for some traits to a higher, predictive ability compared to SNP-based models or to haplotype models that do not use gene annotation information. Modeling gene interaction effects can further improve predictive ability. We also illustrate that the additional use of markers that have not been mapped to any gene in a second separate relatedness matrix does in many cases not lead to a relevant additional increase in predictive ability when the first matrix is based on haploblocks defined with gene annotation data, suggesting that intergenic markers only provide redundant information on the considered data sets. Therefore, gene annotation information seems to be appropriate to perceive the importance of DNA segments. Finally, we discuss the effects of gene annotation quality, marker density, and linkage disequilibrium on the performance of the new methods. To our knowledge, this is the first work that incorporates epistatic interaction or gene annotation into haplotype-based prediction approaches.

Oxygen-dependent regulation of tumor growth and metastasis in human breast cancer xenografts.

BackgroundTumor hypoxia is relevant for tumor growth, metabolism, resistance to chemotherapy and metastasis. We have previously shown that hyperoxia, using hyperbaric oxygen treatment (HBOT), attenuates tumor growth and shifts the phenotype from mesenchymal to epithelial (MET) in the DMBA-induced mammary tumor model. This study describes the effect of HBOT on tumor growth, angiogenesis, chemotherapy efficacy and metastasis in a triple negative MDA-MB-231 breast cancer model, and evaluates tumor growth using a triple positive BT-474 breast cancer model.Materials and methods5 x 105 cancer cells were injected s.c. in the groin area of NOD/SCID female mice. The BT-474 group was supplied with Progesterone and Estradiol pellets 2-days prior to tumor cell injection. Mice were divided into controls (1 bar, pO2 = 0.2 bar) or HBOT (2.5 bar, pO2 = 2.5 bar, 90 min, every third day until termination of the experiments). Treatment effects were determined by assessment of tumor growth, proliferation (Ki67-staining), angiogenesis (CD31-staining), metastasis (immunostaining), EMT markers (western blot), stromal components collagen type I, Itgb1 and FSP1 (immunostaining) and chemotherapeutic efficacy (5FU).ResultsHBOT significantly suppressed tumor growth in both the triple positive and negative tumors, and both MDA-MB-231 and BT-474 showed a decrease in proliferation after HBOT. No differences were found in angiogenesis or 5FU efficacy between HBOT and controls. Nevertheless, HBOT significantly reduced both numbers and total area of the metastastatic lesions, as well as reduced expression of N-cadherin, Axl and collagen type I measured in the MDA-MB-231 model. No change in stromal Itgb1 and FSP1 was found in either tumor model.ConclusionDespite the fact that behavior and prognosis of the triple positive and negative subtypes of cancer are different, the HBOT had a similar suppressive effect on tumor growth, indicating that they share a common oxygen dependent anti-tumor mechanism. Furthermore, HBOT significantly reduced the number and area of metastatic lesions in the triple negative model as well as a significant reduction in the EMT markers N-cadherin, Axl and density of collagen type I.

Does growth restriction increase the vulnerability to acute ventilation-induced brain injury in newborn lambs? Implications for future health and disease.

Fetal growth restriction (FGR) and preterm birth are frequent co-morbidities, both are independent risks for brain injury. However, few studies have examined the mechanisms by which preterm FGR increases the risk of adverse neurological outcomes. We aimed to determine the effects of prematurity and mechanical ventilation (VENT) on the brain of FGR and appropriately grown (AG, control) lambs. We hypothesized that FGR preterm lambs are more vulnerable to ventilation-induced acute brain injury. FGR was surgically induced in fetal sheep (0.7 gestation) by ligation of a single umbilical artery. After 4 weeks, preterm lambs were euthanized at delivery or delivered and ventilated for 2 h before euthanasia. Brains and cerebrospinal fluid (CSF) were collected for analysis of molecular and structural indices of early brain injury. FGRVENT lambs had increased oxidative cell damage and brain injury marker S100B levels compared with all other groups. Mechanical ventilation increased inflammatory marker IL-8 within the brain of FGRVENT and AGVENT lambs. Abnormalities in the neurovascular unit and increased blood-brain barrier permeability were observed in FGRVENT lambs, as well as an altered density of vascular tight junctions markers. FGR and AG preterm lambs have different responses to acute injurious mechanical ventilation, changes which appear to have been developmentally programmed in utero.

A High-Density Consensus Map of Common Wheat Integrating Four Mapping Populations Scanned by the 90K SNP Array.

A high-density consensus map is a powerful tool for gene mapping, cloning and molecular marker-assisted selection in wheat breeding. The objective of this study was to construct a high-density, single nucleotide polymorphism (SNP)-based consensus map of common wheat (Triticum aestivum L.) by integrating genetic maps from four recombinant inbred line populations. The populations were each genotyped using the wheat 90K Infinium iSelect SNP assay. A total of 29,692 SNP markers were mapped on 21 linkage groups corresponding to 21 hexaploid wheat chromosomes, covering 2,906.86 cM, with an overall marker density of 10.21 markers/cM. Compared with the previous maps based on the wheat 90K SNP chip detected 22,736 (76.6%) of the SNPs with consistent chromosomal locations, whereas 1,974 (6.7%) showed different chromosomal locations, and 4,982 (16.8%) were newly mapped. Alignment of the present consensus map and the wheat expressed sequence tags (ESTs) Chromosome Bin Map enabled assignment of 1,221 SNP markers to specific chromosome bins and 819 ESTs were integrated into the consensus map. The marker orders of the consensus map were validated based on physical positions on the wheat genome with Spearman rank correlation coefficients ranging from 0.69 (4D) to 0.97 (1A, 4B, 5B, and 6A), and were also confirmed by comparison with genetic position on the previously 40K SNP consensus map with Spearman rank correlation coefficients ranging from 0.84 (6D) to 0.99 (6A). Chromosomal rearrangements reported previously were confirmed in the present consensus map and new putative rearrangements were identified. In addition, an integrated consensus map was developed through the combination of five published maps with ours, containing 52,607 molecular markers. The consensus map described here provided a high-density SNP marker map and a reliable order of SNPs, representing a step forward in mapping and validation of chromosomal locations of SNPs on the wheat 90K array. Moreover, it can be used as a reference for quantitative trait loci (QTL) mapping to facilitate exploitation of genes and QTL in wheat breeding.

Genomewide association study for production and meat quality traits in Canchim beef cattle.

The commercial value of the bovine carcass is determined by a set of traits, such as weight, yield, back fat thickness, and marbling; therefore, the genetic improvement of growth, meat, and carcass quality traits is an important tool to add value to the supply chain. Genomewide association studies (GWAS) enable the identification of loci that control phenotypic expression of quantitative traits (QTL). Therefore, the objective of this work was to perform a GWAS to identify genomic regions and genes associated with growth, carcass traits, and meat quality in Canchim beef cattle. These traits were yearling weight (YW), rib eye area (REA), back fat thickness (BFT), and marbling (MARB). To increase sample size and marker density, genotype imputation was performed, and only markers imputed with greater than 95% accuracy were used. Genomewide association study was performed using a Bayesian approach, by the Bayes B statistical method, incorporating genotypes and phenotypes from 614 animals from both the Canchim breed and the MA genetic group (offspring of Charolais bulls and one-half Canchim + one-half Zebu cows). This investigation identified 1 and 4 genomic regions explaining 0.23 and 7.35% of the genetic variance for REA and YW, respectively. These regions harbor a total of 19 genes, 7 of which were classified for biological functions by functional analysis. Significant associations were not observed for BFT and MARB. The identification of QTL that had been previously described in the literature reinforces associations found in this study.

The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle.

Genomic selection (GS) has become an integral part of genetic evaluation methodology and has been applied to all major livestock species, including beef and dairy cattle, pigs, and chickens. Significant contributions in increased accuracy of selection decisions have been clearly illustrated in dairy cattle after practical application of GS. In the majority of U.S. beef cattle breeds, similar efforts have also been made to increase the accuracy of genetic merit estimates through the inclusion of genomic information into routine genetic evaluations using a variety of methods. However, prediction accuracies can vary relative to panel density, the number of folds used for folds cross-validation, and the choice of dependent variables (e.g., EBV, deregressed EBV, adjusted phenotypes). The aim of this study was to evaluate the accuracy of genomic predictors for Red Angus beef cattle with different strategies used in training and evaluation. The reference population consisted of 9,776 Red Angus animals whose genotypes were imputed to 2 medium-density panels consisting of over 50,000 (50K) and approximately 80,000 (80K) SNP. Using the imputed panels, we determined the influence of marker density, exclusion (deregressed EPD adjusting for parental information [DEPD-PA]) or inclusion (deregressed EPD without adjusting for parental information [DEPD]) of parental information in the deregressed EPD used as the dependent variable, and the number of clusters used to partition training animals (3, 5, or 10). A BayesC model with π set to 0.99 was used to predict molecular breeding values (MBV) for 13 traits for which EPD existed. The prediction accuracies were measured as genetic correlations between MBV and weighted deregressed EPD. The average accuracies across all traits were 0.540 and 0.552 when using the 50K and 80K SNP panels, respectively, and 0.538, 0.541, and 0.561 when using 3, 5, and 10 folds, respectively, for cross-validation. Using DEPD-PA as the response variable resulted in higher accuracies of MBV than those obtained by DEPD for growth and carcass traits. When DEPD were used as the response variable, accuracies were greater for threshold traits and those that are sex limited, likely due to the fact that these traits suffer from a lack of information content and excluding animals in training with only parental information substantially decreases the training population size. It is recommended that the contribution of parental average to deregressed EPD should be removed in the construction of genomic prediction equations. The difference in terms of prediction accuracies between the 2 SNP panels or the number of folds compared herein was negligible.

Development of a high-density linkage map and mapping of the three-pistil gene (Pis1) in wheat using GBS markers

BackgroundThe wheat mutant line three-pistil (TP) exhibits three pistils per floret. As TP normally has two or three seeds in each of the florets on the same spike, there is the possibility of increasing the number of grains per spike. Therefore, TP is a highly valuable mutant for breeding and for the study of floral development in wheat. To map the three-pistil gene (Pis1), genotyping-by-sequencing single-nucleotide polymorphism (GBS-SNP) data from an F2 mapping population (CM28 × CM28TP) was used to construct a genetic map that is of significant value.ResultsIn the present study, a high-density genetic map of wheat containing 2917 GBS-SNP markers was constructed. Twenty-one linkage groups were resolved, with a total length of 2371.40 cM. The individual chromosomes range from 2.64 cM to 454.55 cM with an average marker density of 0.81 cM. The Pis1 gene was mapped using this high-resolution map, and two flanking SNP markers tightly linked to the gene, M70 and M71, were identified. The Pis1 is 3.00 cM from M70 and 1.10 cM from M71. In bread wheat genome, M70 and M71 were found to delimit a physical distance of 3.40 Mb, which encompasses 127 protein-coding genes. To validate the GBS-generated genotypic data and to eliminate missing marker data in the Pis1 region, five Kompetitive Allele-Specific PCR (KASP) assays were designed from corresponding GBS sequences, which harbor SNPs that surround Pis1. Three KASP-SNP markers, KM70, KM71, and KM75, were remapped to the Pis1 gene region.ConclusionsThis work not only lays the foundation for the map-based cloning of Pis1 but can also serve as a valuable tool for studying marker-trait association of important traits and marker-assisted breeding in wheat.

Decreasing the Expression of GABAA α5 Subunit-Containing Receptors Partially Improves Cognitive, Electrophysiological, and Morphological Hippocampal Defects in the Ts65Dn Model of Down Syndrome.

Trisomy 21 or Down syndrome (DS) is the most common cause of intellectual disability of a genetic origin. The Ts65Dn (TS) mouse, which is the most commonly used and best-characterized mouse model of DS, displays many of the cognitive, neuromorphological, and biochemical anomalies that are found in the human condition. One of the mechanisms that have been proposed to be responsible for the cognitive deficits in this mouse model is impaired GABA-mediated inhibition. Because of the well-known modulatory role of GABAA α5 subunit-containing receptors in cognitive processes, these receptors are considered to be potential targets for improving the intellectual disability in DS. The chronic administration of GABAA α5-negative allosteric modulators has been shown to be procognitive without anxiogenic or proconvulsant side effects. In the present study, we use a genetic approach to evaluate the contribution of GABAA α5 subunit-containing receptors to the cognitive, electrophysiological, and neuromorphological deficits in TS mice. We show that reducing the expression of GABAA α5 receptors by deleting one or two copies of the Gabra5 gene in TS mice partially ameliorated the cognitive impairments, improved long-term potentiation, enhanced neural differentiation and maturation, and normalized the density of the GABAergic synapse markers. Reducing the gene dosage of Gabra5 in TS mice did not induce motor alterations and anxiety or affect the viability of the mice. Our results provide further evidence of the role of GABAA α5 receptor-mediated inhibition in cognitive impairment in the TS mouse model of DS.

Oxytocin receptor gene polymorphisms, attachment, and PTSD: Results from the National Health and Resilience in Veterans Study

The human oxytocin system is implicated in social behavior and stress recovery. Polymorphisms in the oxytocin receptor gene (OXTR) may interact with attachment style to predict stress-related psychopathology like posttraumatic stress disorder (PTSD). The objective of this study was to examine independent and interactive effects of the OXTR single nucleotide polymorphism (SNP) rs53576, which has been associated with stress reactivity, support-seeking, and PTSD in prior studies, and attachment style on risk for PTSD in a nationally representative sample of 2163 European-American (EA) U.S. military veterans who participated in two independent waves of the National Health and Resilience in Veterans Study (NHRVS). Results revealed that insecure attachment style [adjusted odds ratio (OR) = 4.29; p < 0.001] and the interaction of rs53576 and attachment style (OR = 2.58, p = 0.02) were associated with probable lifetime PTSD. Among individuals with the minor A allele, the prevalence of probable PTSD was significantly higher among those with an insecure attachment style (23.9%) than those with a secure attachment style (2.0%), equivalent to an adjusted OR of 10.7. We attempted to replicate these findings by utilizing dense marker data from a genome-wide association study of 2215 high-risk civilians; one OXTR variant, though not rs53576, was associated with PTSD. Exploratory analyses in the veteran sample revealed that the interaction between this variant and attachment style predicting probable PTSD approached statistical significance. Results indicate that polymorphisms in the OXTR gene and attachment style may contribute to vulnerability to PTSD in U.S. military veterans.

Genetic Diversity, Population Structure, and Linkage Disequilibrium of an Association-Mapping Panel Revealed by Genome-Wide SNP Markers in Sesame.

The characterization of genetic diversity and population structure can be used in tandem to detect reliable phenotype–genotype associations. In the present study, we genotyped a set of 366 sesame germplasm accessions by using 89,924 single-nucleotide polymorphisms (SNPs). The number of SNPs on each chromosome was consistent with the physical length of the respective chromosome, and the average marker density was approximately 2.67 kb/SNP. The genetic diversity analysis showed that the average nucleotide diversity of the panel was 1.1 × 10-3, with averages of 1.0 × 10-4, 2.7 × 10-4, and 3.6 × 10-4 obtained, respectively for three identified subgroups of the panel: Pop 1, Pop 2, and the Mixed. The genetic structure analysis revealed that these sesame germplasm accessions were structured primarily along the basis of their geographic collection, and that an extensive admixture occurred in the panel. The genome-wide linkage disequilibrium (LD) analysis showed that an average LD extended up to ∼99 kb. The genetic diversity and population structure revealed in this study should provide guidance to the future design of association studies and the systematic utilization of the genetic variation characterizing the sesame panel.

Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction.

Tar spot complex (TSC) is one of the most destructive foliar diseases of maize ( L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. Additional QTL were identified by linkage mapping alone. A major QTL located on bin 8.03 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 13.18 to 43.31% in linkage-mapping analysis. These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 8.03 and several minor QTL with smaller effects on other chromosomes. Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities. Prediction accuracy of TSC resistance was >0.50 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction. Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize.

A high-density intraspecific SNP linkage map of pigeonpea (Cajanas cajan L. Millsp.).

Pigeonpea (Cajanus cajan (L.) Millsp.) is a major food legume cultivated in semi-arid tropical regions including the Indian subcontinent, Africa, and Southeast Asia. It is an important source of protein, minerals, and vitamins for nearly 20% of the world population. Due to high carbon sequestration and drought tolerance, pigeonpea is an important crop for the development of climate resilient agriculture and nutritional security. However, pigeonpea productivity has remained low for decades because of limited genetic and genomic resources, and sparse utilization of landraces and wild pigeonpea germplasm. Here, we present a dense intraspecific linkage map of pigeonpea comprising 932 markers that span a total adjusted map length of 1,411.83 cM. The consensus map is based on three different linkage maps that incorporate a large number of single nucleotide polymorphism (SNP) markers derived from next generation sequencing data, using Illumina GoldenGate bead arrays, and genotyping with restriction site associated DNA (RAD) sequencing. The genotyping-by-sequencing enhanced the marker density but was met with limited success due to lack of common markers across the genotypes of mapping population. The integrated map has 547 bead-array SNP, 319 RAD-SNP, and 65 simple sequence repeat (SSR) marker loci. We also show here correspondence between our linkage map and published genome pseudomolecules of pigeonpea. The availability of a high-density linkage map will help improve the anchoring of the pigeonpea genome to its chromosomes and the mapping of genes and quantitative trait loci associated with useful agronomic traits.

Restoring Cystic Fibrosis Transmembrane Conductance Regulator Function Reduces Airway Bacteria and Inflammation in People with Cystic Fibrosis and Chronic Lung Infections.

Previous work indicates that ivacaftor improves cystic fibrosis transmembrane conductance regulator (CFTR) activity and lung function in people with cystic fibrosis and G551D-CFTR mutations but does not reduce density of bacteria or markers of inflammation in the airway. These findings raise the possibility that infection and inflammation may progress independently of CFTR activity once cystic fibrosis lung disease is established. To better understand the relationship between CFTR activity, airway microbiology and inflammation, and lung function in subjects with cystic fibrosis and chronic airway infections. We studied 12 subjects with G551D-CFTR mutations and chronic airway infections before and after ivacaftor. We measured lung function, sputum bacterial content, and inflammation, and obtained chest computed tomography scans. Ivacaftor produced rapid decreases in sputum Pseudomonas aeruginosa density that began within 48 hours and continued in the first year of treatment. However, no subject eradicated their infecting P. aeruginosa strain, and after the first year P. aeruginosa densities rebounded. Sputum total bacterial concentrations also decreased, but less than P. aeruginosa. Sputum inflammatory measures decreased significantly in the first week of treatment and continued to decline over 2 years. Computed tomography scans obtained before and 1 year after ivacaftor treatment revealed that ivacaftor decreased airway mucous plugging. Ivacaftor caused marked reductions in sputum P. aeruginosa density and airway inflammation and produced modest improvements in radiographic lung disease in subjects with G551D-CFTR mutations. However, P. aeruginosa airway infection persisted. Thus, measures that control infection may be required to realize the full benefits of CFTR-targeting treatments.

A SNP-Based Genetic Linkage Map of Soybean Using the SoySNP6K Illumina Infinium BeadChip Genotyping Array

This study reports a high density genetic linkage map based on the ‘Maryland 96-5722’ by ‘Spencer’ recombinant inbred line (RIL) population of soybean [Glycine max (L.) Merr.] and constructed exclusively with single nucleotide polymorphism (SNP) markers. The Illumina Infinium SoySNP6K BeadChip genotyping array produced 5,376 SNPs in the mapping population, with a 96.75% success rate. Significant level of goodness-of-fit for each locus was tested based on the observed vs. expected ratio (1:1). Out of 5,376 markers, 1,465 SNPs fit the 1:1 segregation rate having ≤20% missing data plus heterozygosity among the RILs. Among this 1,456 just 657 were polymorphic between the parents DNAs tested. These 657 SNPs were mapped using the JoinMap 4.0 software and 550 SNPs were distributed on 16 linkage groups (LGs) among the 20 chromosomes of the soybean genome. The total map length was just 201.57 centiMorgans (cM) with an average marker density of 0.37 cM. This is one of the high density SNP-based genetic linkage maps of soybean that will be used by the scientific community to map quantitative trait loci (QTL) and identify candidate genes for important agronomic traits in soybean.