Predicting Splicing from Primary Sequence with Deep Learning.

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.

High Definition Liposculpture in Male Patients Using Reciprocating Power-Assisted Liposuction Technology: Techniques and Results in a Prospective Study.

BACKGROUND: High-definition liposculpture (HDL) consists of techniques developed to emulate an athletic and attractive surface anatomy. OBJECTIVES: The aim of this study was to describe our experience, including techniques and postoperative outcomes, when performing HDL with power-assisted liposuction (PAL) technology in male patients. METHODS: In this prospective study PAL was used for fat extraction and to perform HDL. Intraoperative data were collected and the techniques used were recorded. Postoperatively, photographs were taken, and data were collected regarding the use of compression garments and pain medication, return to work and full activity, and satisfaction levels. RESULTS: All patients were males, with an average age of 32.3 years and a body mass index of 26.45 kg/m2; the follow-up period averaged 7.6 months. Intraoperatively, all patients received HDL of the chest, abdomen, back, and upper arms, and fat transfer to the chest. The average operative time was 3.4 hours. None of patients had drains left intraoperatively. Postoperatively, all patients were completely off their pain medications by postoperative day 9. They received, on average, 3.7 sessions of lymphatic massage and wore compression garments for an average of 3 weeks. They returned to work 6.1 days after surgery and were back to full activity in 3.2 weeks. Minor complications were reported in 2 patients. Patient satisfaction was on average 9.8 (on a scale of 1-10) at 6 months. CONCLUSIONS: HDL with PAL technology is a safe procedure that delivers reproducible natural-looking results with high patient satisfaction rates, low risk of complications, and a relatively short and tolerable recovery process.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation.

KEY MESSAGE: The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Aesthetic Plastic Surgery Checklist: A Safety Tool.

INTRODUCTION: About one in ten patients experiences iatrogenic events, and more than half of these occur in the perioperative environment. The objective of this study was to develop a complete and functional checklist for aesthetic plastic surgery and test it in patients who would undergo elective plastic surgeries. METHODS: Patient data were collected from a general hospital and the particular clinic between October 2013 and October 2015, through history, physical examination, diagnosis, laboratory tests, pre-, during, and postoperatively, and complications. An expanded safety checklist was developed and optimized for aesthetic plastic surgery based on the model presented by the WHO in 2009 with reference to the information related to the prevention of more frequent complications in this specialty. RESULTS: The tool was applied to 486 patients, of whom 430 (88 %) were women and 56 (12 %) were men. The most frequently performed procedure was liposuction with 30 % of cases, and the most widely used type of anesthesia (39 %) was local anesthesia + sedation. The greater adherence of professionals to the checklist was the group of residents (98 %). The observed complications were seromas (7 %), other complications unrelated to the wound (3 %), and hematoma (0.2 %) in only one patient who underwent facelift. CONCLUSION: The use of the checklist in addition to allowing data collection and the identification of potential risks promoted favorable changes in the attitudes of some professionals and generated interest in patient safety and teamwork. LEVEL OF EVIDENCE IV: This journal requires that the authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Displacements of fiducial markers in patients with prostate cancer treated with image guided radiotherapy: A single-institution descriptive study.

AIM: To describe daily displacements when using fiducial markers as surrogates for the target volume in patients with prostate cancer treated with IGRT. BACKGROUND: The higher grade of conformity achieved with the use of modern radiation technologies in prostate cancer can increase the risk of geographical miss; therefore, an associated protocol of IGRT is recommended. MATERIALS AND METHODS: A single-institution, retrospective, consecutive study was designed. 128 prostate cancer patients treated with daily on-line IGRT based on 2D kV orthogonal images were included. Daily displacement of the fiducial markers was considered as the difference between the position of the patient when using skin tattoos and the position after being relocated using fiducial markers. Measures of central tendency and dispersion were used to describe fiducial displacements. RESULTS: The implant itself took a mean time of 15 min. We did not detect any complications derived from the implant. 4296 sets of orthogonal images were identified, 128 sets of images corresponding to treatment initiation were excluded; 91 (2.1%) sets of images were excluded from the analysis after having identified that these images contained extreme outlier values. If IGRT had not been performed 25%, 10% or 5% of the treatments would have had displacements superior to 4, 7 or 9 mm respectively in any axis. CONCLUSIONS: Image guidance is required when using highly conformal techniques; otherwise, at least 10% of daily treatments could have significant displacements. IGRT based on fiducial markers, with 2D kV orthogonal images is a convenient and fast method for performing image guidance.

Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer.

Purpose: To assess the efficacy of an automated program for keratoconus and keratoconus suspect detection based on corneal measurements provided by a combined Placido disc and anterior segment optical coherence tomography (OCT) topographer. Methods: In a multicentric cross-sectional study, an artificial neural network (ANN) was created using 6677 eyes from an equal number of patients (classified as 2663 normal eyes, 1616 keratoconus eyes, 210 keratoconus suspect eyes, 1519 myopic postoperative eyes, and 669 abnormal eyes). Each group was randomly divided into a training set (70% of the dataset) and a validation set (the remaining 30%). A multilayer perceptron network with a backpropagation learning algorithm was developed for the study. Indexes used to train the ANN were based on curvature and elevation of both the anterior and posterior corneal surfaces and the new corneal OCT indexes-based on corneal, stromal, and epithelial thicknesses. Results: For keratoconus detection, our ANN showed an accuracy of 98.6%, precision of 96%, recall of 97.9%, and F1-score of 96.9%. For keratoconus suspect detection, our ANN showed an accuracy of 98.5%, precision of 83.6%, recall of 69.7%, and F1-score of 76%. Conclusions: Compared to previous literature, the addition of new OCT-based epithelial and stromal thickness indexes improves ANN detection capacity of keratoconus suspect eyes. For already stablished keratoconus our ANN detection capacity is excellent, but equivalent to previous evidence without incorporating such new OCT-based indexes. Translational Relevance: OCT-based epithelial and stromal thickness indexes improve ANN detection capacity of keratoconus on its early stages.

Enhancing the Automated Detection of Implantable Collamer Lens Vault Using Generative Adversarial Networks and Synthetic Data on Optical Coherence Tomography.

PURPOSE: To investigate the efficacy of incorporating Generative Adversarial Network (GAN) and synthetic images in enhancing the performance of a convolutional neural network (CNN) for automated estimation of Implantable Collamer Lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). METHODS: This study was a retrospective evaluation using synthetic data and real patient images in a deep learning framework. Synthetic ICL AS-OCT scans were generated using GANs and a secondary image editing algorithm, creating approximately 100,000 synthetic images. These were used alongside real patient scans to train a CNN for estimating ICL vault distance. The model's performance was evaluated using statistical metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) for the estimation of ICL vault distance. RESULTS: The study analyzed 4,557 AS-OCT B-scans from 138 eyes of 103 patients for training. An independent, retrospectively collected dataset of 2,454 AS-OCT images from 88 eyes of 56 patients, used prospectively for evaluation, served as the test set. When trained solely on real images, the CNN achieved a MAPE of 15.31%, MAE of 44.68 µm, and RMSE of 63.3 µm. However, with the inclusion of GAN-generated and algorithmically edited synthetic images, the performance significantly improved, achieving a MAPE of 8.09%, MAE of 24.83 µm, and RMSE of 32.26 µm. The R2 value was +0.98, indicating a strong positive correlation between actual and predicted ICL vault distances (P < .01). No statistically significant difference was observed between measured and predicted vault values (P = .58). CONCLUSIONS: The integration of GAN-generated and edited synthetic images substantially enhanced ICL vault estimation, demonstrating the efficacy of GANs and synthetic data in enhancing OCT image analysis accuracy. This model not only shows potential for assisting postoperative ICL evaluations, but also for improving OCT automation when data paucity is an issue. [J Refract Surg. 2024;40(4):e199-e207.].

'Single-Seed-SpeedBulks:' a protocol that combines 'speed breeding' with a cost-efficient modified single-seed descent method for rapid-generation-advancement in oat (Avena sativa L.).

BACKGROUND: In plant breeding, one of the most cost-effective and efficient ways to increase genetic gain is to reduce the breeding cycle time. In general, modern breeding methods for self-pollinated crops should strive to develop fixed lines at the lowest possible cost and in the minimum possible amount of time. Previous studies on spring oat (Avena sativa L.) showed that combining high plant density with limited soil fertility and moisture levels in a growth media like sand effectively decreases the time and cost of generating fixed single-seed descent lines. More recently, 'speed breeding,' or the exposure to prolonged photoperiod regimes of 22 h, has been shown to decrease flowering time in oat significantly. The goal of this study was to combine 'speed breeding' with high-density planting in a limited soil fertility media to reduce further the costs and time required to develop oat single-seed-descent lines. RESULTS: We grew oat plants at low density in potting-mix (control), high density in potting-mix (HD-soil), and high density in sand (HD-sand) under 16 and 22 h of day length. We observed that oat plants grown in HD-sand and exposed to 22 h day length reduced their flowering time by around 20 and 5 days on average compared to those grown in control conditions at 16 and 22 h, respectively. We also observed that 85% of plants grown at high density in sand produced a single seed when grown in bulk conditions. In contrast, only 40% of plants grown at high density in potting-mix produced a single seed. CONCLUSIONS: Our novel protocol showed that oat plants grown in high-density bulks, using sand media and 22-hour day length, reduced their flowering time by 20 days compared to control conditions and produced plants with single seeds, following closely single-seed descent assumptions while significantly reducing labor costs and greenhouse space. This methodology can be deployed in oat breeding programs to help them accelerate their rate of genetic grain for multiple traits.

Deep Learning-Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography.

PURPOSE: To develop and validate a deep learning neural network for automated measurement of implantable collamer lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). DESIGN: Cross-sectional retrospective study. METHODS: A total of 2647 AS-OCT scans were used from 139 eyes of 82 subjects who underwent ICL surgery in 3 different centers. Using transfer learning, a deep learning network was trained and validated for estimating the ICL vault on OCT. A trained operator separately reviewed all OCT scans and measured the central vault using a built-in caliper tool. The model was then separately tested on 191 scans. A Bland-Altman plot was constructed and the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), and determination coefficient (R2) were calculated to evaluate the strength and validity of the model. RESULTS: On the test set, the model achieved a MAPE of 3.42%, an MAE of 15.82 µm, a RMSE of 18.85 µm, a Pearson correlation coefficient r of +0.98 (P < .00001), and a coefficient of determination R2 of +0.96. There was no significant difference between the vaults of the test set labeled by the technician vs those estimated by the model: 478 ± 95 µm vs 475 ± 97 µm, respectively, P = .064). CONCLUSIONS: Using transfer learning, our deep learning neural network was able to accurately compute the ICL vault from AS-OCT scans, overcoming the limitations of an imbalanced data set and limited training data. Such an algorithm can assist the postoperative assessment in ICL surgery.

Rare X-linked variants carry predominantly male risk in autism, Tourette syndrome, and ADHD.

Autism spectrum disorder (ASD), Tourette syndrome (TS), and attention-deficit/hyperactivity disorder (ADHD) display strong male sex bias, due to a combination of genetic and biological factors, as well as selective ascertainment. While the hemizygous nature of chromosome X (Chr X) in males has long been postulated as a key point of "male vulnerability", rare genetic variation on this chromosome has not been systematically characterized in large-scale whole exome sequencing studies of "idiopathic" ASD, TS, and ADHD. Here, we take advantage of informative recombinations in simplex ASD families to pinpoint risk-enriched regions on Chr X, within which rare maternally-inherited damaging variants carry substantial risk in males with ASD. We then apply a modified transmission disequilibrium test to 13,052 ASD probands and identify a novel high confidence ASD risk gene at exome-wide significance (MAGEC3). Finally, we observe that rare damaging variants within these risk regions carry similar effect sizes in males with TS or ADHD, further clarifying genetic mechanisms underlying male vulnerability in multiple neurodevelopmental disorders that can be exploited for systematic gene discovery.

Sjögren's syndrome at the crossroad of polyautoimmunity.

The coexistence of autoimmune diseases (i.e., polyautoimmunity) in Sjögren's syndrome (SS) was investigated in a cross-sectional study involving 410 patients. Logistic regression analysis and the Rogers and Tanimoto index were used to evaluate risk factors and clustering, respectively. There were 134 (32.6%) patients with polyautoimmunity. The most frequent and closer coexistent diseases were autoimmune thyroid disease (21.5%), rheumatoid arthritis (8.3%), systemic lupus erythematosus (7.6%), and inflammatory bowel disease (0.7%) which together constituted a cluster group. There were 35 (8.5%) patients with multiple autoimmune syndrome. Besides disease duration, a history of habitual smoking and spontaneous abortion were found to be risk factors for the developing of polyautoimmunity. This study discloses a high prevalence of polyautoimmunity in SS, its associated risk factors and the grouping pattern of such a condition. These results may serve to define plausible approaches to study the common mechanisms of autoimmune diseases.

Efficacy of progressive thickness intrastromal corneal ring segments in the treatment of duck phenotype keratoconus.

PURPOSE: To evaluate clinical outcomes in patients with asymmetric duck phenotype keratoconus implanted with asymmetric progressive thickness intrastromal corneal ring segments (PT-ICRS, Keraring AS, Mediphacos, Belo Horizonte, Brazil), and to demonstrate improved visual acuity and symmetry in corneal flattening. METHODS: This single-center, retrospective, observational study evaluated the clinical outcomes at 1, 3, and 6 months after implantation of PT-ICRS in patients with duck phenotype keratoconus. After creating the intrastromal tunnel with a femtosecond laser, one 160-degree arc length PT-ICRS, either 150/250 µm or 200/300 µm, was implanted. Changes in uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), manifest refraction (MR), manifest refraction spherical equivalent (MRSE), mean keratometry (Kmean), maximum keratometry (Kmax), Coma@5mm, and manifest and topographical astigmatism were documented. RESULTS: The study cohort included 23 eyes of 17 patients with a mean age of 24.6 years. From baseline to 6 months following implantation, mean UDVA improved from 0.70 logarithm of the minimum angle of resolution (logMAR) (20/100 Snellen) to 0.22 logMAR (20/33 Snellen), CDVA improved from 0.33 logMAR (20/42 Snellen) to 0.14 logMAR (20/27 Snellen), the mean sphere and MRSE decreased from -1.05 diopters (D) and -2.70 D to -0.25 D and -0.80 D, respectively, and the Kmean and Kmax improved from 46.5 D and 53.7 D to 44.6 D and 48.7 D, respectively. CONCLUSIONS: The Keraring AS ICRS offers a safe, effective means of improving visual acuity while reducing refractive error, mean and max keratometry, and aberrations in patients with duck phenotype keratoconus.

Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean.

Rice (Oryza sativa L.)grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release. In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from -0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51-75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS. Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random forest had the highest predictive abilities and was especially effective for traits where large effect quantitative trait loci were identified. This study provides the foundation for deploying effective molecular breeding strategies for grain quality in Latin American rice breeding programs.

Identification of an Elite Core Panel as a Key Breeding Resource to Accelerate the Rate of Genetic Improvement for Irrigated Rice.

Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield. In an effort to identify and characterize the elite breeding pool of IRRI's irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha-1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha-1 year-1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha-1 year-1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha-1 cycle-1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential. We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

A universal core genetic map for rice.

To facilitate the creation of easily comparable, low-resolution genetic maps with evenly distributed markers in rice (Oryza sativa L.), we conceived of and developed a Universal Core Genetic Map (UCGM). With this aim, we derived a set of 165 anchors, representing clusters of three microsatellite or simple sequence repeat (SSR) markers arranged into non-recombining groups. Each anchor consists of at least three, closely linked SSRs, located within a distance below the genetic resolution provided by common, segregating populations (<500 individuals). We chose anchors that were evenly distributed across the rice chromosomes, with spacing between 2 and 3.5 Mbp (except in the telomeric regions, where spacing was 1.5 Mbp). Anchor selection was performed using in silico tools and data: the O. sativa cv. Nipponbare rice genome sequence, the CHARM tool, information from the Gramene database and the OrygenesDB database. Sixteen AA-genome accessions of the Oryza genus were used to evaluate polymorphisms for the selected markers, including accessions from O. sativa, O. glaberrima, O. barthii, O. rufipogon, O. glumaepatula and O. meridionalis. High levels of polymorphism were found for the tested O. sativa x O. glaberrima or O. sativa x wild rice combinations. We developed Paddy Map, a simple database that is helpful in selecting optimal sets of polymorphic SSRs for any cross that involves the previously mentioned species. Validation of the UCGM was done by using it to develop three interspecific genetic maps and by comparing genetic SSR locations with their physical positions on the rice pseudomolecules. In this study, we demonstrate that the UCGM is a useful tool for the rice genetics and breeding community, especially in strategies based on interspecific hybridisation.

Comparative Transcriptomics and Co-Expression Networks Reveal Tissue- and Genotype-Specific Responses of qDTYs to Reproductive-Stage Drought Stress in Rice (Oryza sativa L.).

Rice (Oryza sativa L.) is more sensitive to drought stress than other cereals. To dissect molecular mechanisms underlying drought-tolerant yield in rice, we applied differential expression and co-expression network approaches to transcriptomes from flag-leaf and emerging panicle tissues of a drought-tolerant yield introgression line, DTY-IL, and the recurrent parent Swarna, under moderate reproductive-stage drought stress. Protein turnover and efficient reactive oxygen species scavenging were found to be the driving factors in both tissues. In the flag-leaf, the responses further included maintenance of photosynthesis and cell wall reorganization, while in the panicle biosynthesis of secondary metabolites was found to play additional roles. Hub genes of importance in differential drought responses included an expansin in the flag-leaf and two peroxidases in the panicle. Overlaying differential expression data with allelic variation in DTY-IL quantitative trait loci allowed for the prioritization of candidate genes. They included a differentially regulated auxin-responsive protein, with DTY-IL-specific amino acid changes in conserved domains, as well as a protein kinase with a DTY-IL-specific frameshift in the C-terminal region. The approach highlights how the integration of differential expression and allelic variation can aid in the discovery of mechanism and putative causal contribution underlying quantitative trait loci for drought-tolerant yield.

Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa.

Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.

An improved 7K SNP array, the C7AIR, provides a wealth of validated SNP markers for rice breeding and genetics studies.

Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice (Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.

1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice.

BACKGROUND: While a multitude of genotyping platforms have been developed for rice, the majority of them have not been optimized for breeding where cost, turnaround time, throughput and ease of use, relative to density and informativeness are critical parameters of their utility. With that in mind we report the development of the 1K-Rice Custom Amplicon, or 1k-RiCA, a robust custom sequencing-based amplicon panel of ~ 1000-SNPs that are uniformly distributed across the rice genome, designed to be highly informative within indica rice breeding pools, and tailored for genomic prediction in elite indica rice breeding programs. RESULTS: Empirical validation tests performed on the 1k-RiCA showed average marker call rates of 95% with marker repeatability and concordance rates of 99%. These technical properties were not affected when two common DNA extraction protocols were used. The average distance between SNPs in the 1k-RiCA was 1.5 cM, similar to the theoretical distance which would be expected between 1,000 uniformly distributed markers across the rice genome. The average minor allele frequencies on a panel of indica lines was 0.36 and polymorphic SNPs estimated on pairwise comparisons between indica by indica accessions and indica by japonica accessions were on average 430 and 450 respectively. The specific design parameters of the 1k-RiCA allow for a detailed view of genetic relationships and unambiguous molecular IDs within indica accessions and good cost vs. marker-density balance for genomic prediction applications in elite indica germplasm. Predictive abilities of Genomic Selection models for flowering time, grain yield, and plant height were on average 0.71, 0.36, and 0.65 respectively based on cross-validation analysis. Furthermore the inclusion of important trait markers associated with 11 different genes and QTL adds value to parental selection in crossing schemes and marker-assisted selection in forward breeding applications. CONCLUSIONS: This study validated the marker quality and robustness of the 1k-RiCA genotypic platform for genotyping populations derived from indica rice subpopulation for genetic and breeding purposes including MAS and genomic selection. The 1k-RiCA has proven to be an alternative cost-effective genotyping system for breeding applications.