Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans

Background Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition. Results We propose an imputation method based on multivariate mixed models using pedigree information. Our methods comparison indicate that heritability of traits can be affected by the imputation method. Genotypes with missing values imputed with methods that make use of genealogic information can favor genetic analysis of highly polygenic traits, but not genome-wide prediction accuracy. The genotypic matrix captured the highest amount of genetic variance when missing loci were imputed by the method proposed in this paper. Conclusions We concluded that hidden Markov models and random forest imputation are more suitable to studies that aim analyses of highly heritable traits while pedigree-based methods can be used to best analyze traits with low heritability. Despite the notable contribution to heritability, advantages in genomic prediction were not observed by changing the imputation method. We identified significant differences across imputation methods in a dataset missing 20 % of the genotypic values. It means that genotypic data from genotyping technologies that provide a high proportion of missing values, such as GBS, should be handled carefully because the imputation method will impact downstream analysis

Yield prediction by machine learning from UAS‑based mulit‑sensor data fusion in soybean

16 p.Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.SI"Development of Analytical Tools for Drone-based Canopy Phenotyping in Crop Breeding" (American Institute of Food and Agriculture

Genetic Architecture of Soybean Yield and Agronomic Traits

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding

Variable Copy Number, Intra-Genomic Heterogeneities and Lateral Transfers of the 16S rRNA Gene in Pseudomonas

Even though the 16S rRNA gene is the most commonly used taxonomic marker in microbial ecology, its poor resolution is still not fully understood at the intra-genus level. In this work, the number of rRNA gene operons, intra-genomic heterogeneities and lateral transfers were investigated at a fine-scale resolution, throughout the Pseudomonas genus. In addition to nineteen sequenced Pseudomonas strains, we determined the 16S rRNA copy number in four other Pseudomonas strains by Southern hybridization and Pulsed-Field Gel Electrophoresis, and studied the intra-genomic heterogeneities by Denaturing Gradient Gel Electrophoresis and sequencing. Although the variable copy number (from four to seven) seems to be correlated with the evolutionary distance, some close strains in the P. fluorescens lineage showed a different number of 16S rRNA genes, whereas all the strains in the P. aeruginosa lineage displayed the same number of genes (four copies). Further study of the intra-genomic heterogeneities revealed that most of the Pseudomonas strains (15 out of 19 strains) had at least two different 16S rRNA alleles. A great difference (5 or 19 nucleotides, essentially grouped near the V1 hypervariable region) was observed only in two sequenced strains. In one of our strains studied (MFY30 strain), we found a difference of 12 nucleotides (grouped in the V3 hypervariable region) between copies of the 16S rRNA gene. Finally, occurrence of partial lateral transfers of the 16S rRNA gene was further investigated in 1803 full-length sequences of Pseudomonas available in the databases. Remarkably, we found that the two most variable regions (the V1 and V3 hypervariable regions) had probably been laterally transferred from another evolutionary distant Pseudomonas strain for at least 48.3 and 41.6% of the 16S rRNA sequences, respectively. In conclusion, we strongly recommend removing these regions of the 16S rRNA gene during the intra-genus diversity studies

Cooperative Adaptation to Establishment of a Synthetic Bacterial Mutualism

To understand how two organisms that have not previously been in contact can establish mutualism, it is first necessary to examine temporal changes in their phenotypes during the establishment of mutualism. Instead of tracing back the history of known, well-established, natural mutualisms, we experimentally simulated the development of mutualism using two genetically-engineered auxotrophic strains of Escherichia coli, which mimic two organisms that have never met before but later establish mutualism. In the development of this synthetic mutualism, one strain, approximately 10 hours after meeting the partner strain, started oversupplying a metabolite essential for the partner's growth, eventually leading to the successive growth of both strains. This cooperative phenotype adaptively appeared only after encountering the partner strain but before the growth of the strain itself. By transcriptome analysis, we found that the cooperative phenotype of the strain was not accompanied by the local activation of the biosynthesis and transport of the oversupplied metabolite but rather by the global activation of anabolic metabolism. This study demonstrates that an organism has the potential to adapt its phenotype after the first encounter with another organism to establish mutualism before its extinction. As diverse organisms inevitably encounter each other in nature, this potential would play an important role in the establishment of a nascent mutualism in nature

Proceedings of the 8th Annual Conference on the Science of Dissemination and Implementation

A1 Introduction to the 8(th) Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing resource for technical assistance: Community-based supervisors in public mental health Shannon Dorsey, Michael Pullmann S9 SBIRT implementation for adolescents in urban federally qualified health centers: Implementation outcomes Shannon Mitchell, Robert Schwartz, Arethusa Kirk, Kristi Dusek, Marla Oros, Colleen Hosler, Jan Gryczynski, Carolina Barbosa, Laura Dunlap, David Lounsbury, Kevin O'Grady, Barry Brown S10 PANEL: Tailoring Implementation Strategies to Context - Expert recommendations for tailoring strategies to context Laura Damschroder, Thomas Waltz, Byron Powell S11 PANEL: Tailoring Implementation Strategies to Context - Extreme facilitation: Helping challenged healthcare settings implement complex programs Mona Ritchie S12 PANEL: Tailoring Implementation Strategies to Context - Using menu-based choice tasks to obtain expert recommendations for implementing three high-priority practices in the VA Thomas Waltz S13 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Siri, rate my therapist: Using technology to automate fidelity ratings of motivational interviewing David Atkins, Zac E. Imel, Bo Xiao, Doğan Can, Panayiotis Georgiou, Shrikanth Narayanan S14 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Identifying indicators of implementation quality for computer-based ratings Cady Berkel, Carlos Gallo, Irwin Sandler, C. Hendricks Brown, Sharlene Wolchik, Anne Marie Mauricio S15 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Improving implementation of behavioral interventions by monitoring emotion in spoken speech Carlos Gallo, C. Hendricks Brown, Sanjay Mehrotra S16 Scorecards and dashboards to assure data quality of health management information system (HMIS) using R Dharmendra Chandurkar, Siddhartha Bora, Arup Das, Anand Tripathi, Niranjan Saggurti, Anita Raj S17 A big data approach for discovering and implementing patient safety insights Eric Hughes, Brian Jacobs, Eric Kirkendall S18 Improving the efficacy of a depression registry for use in a collaborative care model Danielle Loeb, Katy Trinkley, Michael Yang, Andrew Sprowell, Donald Nease S19 Measurement feedback systems as a strategy to support implementation of measurement-based care in behavioral health Aaron Lyon, Cara Lewis, Meredith Boyd, Abigail Melvin, Semret Nicodimos, Freda Liu, Nathanial Jungbluth S20 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Common loop assay: Methods of supporting learning collaboratives Allen Flynn S21 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Innovating audit and feedback using message tailoring models for learning health systems Zach Landis-Lewis S22 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Implementation science and learning health systems: Connecting the dots Anne Sales S23 Facilitation activities of Critical Access Hospitals during TeamSTEPPS implementation Jure Baloh, Marcia Ward, Xi Zhu S24 Organizational and social context of federally qualified health centers and variation in maternal depression outcomes Ian Bennett, Jurgen Unutzer, Johnny Mao, Enola Proctor, Mindy Vredevoogd, Ya-Fen Chan, Nathaniel Williams, Phillip Green S25 Decision support to enhance treatment of hospitalized smokers: A randomized trial Steven Bernstein, June-Marie Rosner, Michelle DeWitt, Jeanette Tetrault, James Dziura, Allen Hsiao, Scott Sussman, Patrick O’Connor, Benjamin Toll S26 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - A patient-centered approach to successful community transition after catastrophic injury Michael Jones, Julie Gassaway S27 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - Conducting PCOR to integrate mental health and cancer screening services in primary care Jonathan Tobin S28 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - A comparative effectiveness trial of optimal patient-centered care for US trauma care systems Douglas Zatzick S29 Preferences for in-person communication among patients in a multi-center randomized study of in-person versus telephone communication of genetic test results for cancer susceptibility Angela R Bradbury, Linda Patrick-Miller, Brian Egleston, Olufunmilayo I Olopade, Michael J Hall, Mary B Daly, Linda Fleisher, Generosa Grana, Pamela Ganschow, Dominique Fetzer, Amanda Brandt, Dana Farengo-Clark, Andrea Forman, Rikki S Gaber, Cassandra Gulden, Janice Horte, Jessica Long, Rachelle Lorenz Chambers, Terra Lucas, Shreshtha Madaan, Kristin Mattie, Danielle McKenna, Susan Montgomery, Sarah Nielsen, Jacquelyn Powers, Kim Rainey, Christina Rybak, Michelle Savage, Christina Seelaus, Jessica Stoll, Jill Stopfer, Shirley Yao and Susan Domchek S30 Working towards de-implementation: A mixed methods study in breast cancer surveillance care Erin Hahn, Corrine Munoz-Plaza, Jianjin Wang, Jazmine Garcia Delgadillo, Brian Mittman Michael Gould S31Integrating evidence-based practices for increasing cancer screenings in safety-net primary care systems: A multiple case study using the consolidated framework for implementation research Shuting (Lily) Liang, Michelle C. Kegler, Megan Cotter, Emily Phillips, April Hermstad, Rentonia Morton, Derrick Beasley, Jeremy Martinez, Kara Riehman S32 Observations from implementing an mHealth intervention in an FQHC David Gustafson, Lisa Marsch, Louise Mares, Andrew Quanbeck, Fiona McTavish, Helene McDowell, Randall Brown, Chantelle Thomas, Joseph Glass, Joseph Isham, Dhavan Shah S33 A multicomponent intervention to improve primary care provider adherence to chronic opioid therapy guidelines and reduce opioid misuse: A cluster randomized controlled trial protocol Jane Liebschutz, Karen Lasser S34 Implementing collaborative care for substance use disorders in primary care: Preliminary findings from the summit study Katherine Watkins, Allison Ober, Sarah Hunter, Karen Lamp, Brett Ewing S35 Sustaining a task-shifting strategy for blood pressure control in Ghana: A stakeholder analysis Juliet Iwelunmor, Joyce Gyamfi, Sarah Blackstone, Nana Kofi Quakyi, Jacob Plange-Rhule, Gbenga Ogedegbe S36 Contextual adaptation of the consolidated framework for implementation research (CFIR) in a tobacco cessation study in Vietnam Pritika Kumar, Nancy Van Devanter, Nam Nguyen, Linh Nguyen, Trang Nguyen, Nguyet Phuong, Donna Shelley S37 Evidence check: A knowledge brokering approach to systematic reviews for policy Sian Rudge S38 Using Evidence Synthesis to Strengthen Complex Health Systems in Low- and Middle-Income Countries Etienne Langlois S39 Does it matter: timeliness or accuracy of results? The choice of rapid reviews or systematic reviews to inform decision-making Andrea Tricco S40 Evaluation of the veterans choice program using lean six sigma at a VA medical center to identify benefits and overcome obstacles Sherry Ball, Anne Lambert-Kerzner, Christine Sulc, Carol Simmons, Jeneen Shell-Boyd, Taryn Oestreich, Ashley O'Connor, Emily Neely, Marina McCreight, Amy Labebue, Doreen DiFiore, Diana Brostow, P. Michael Ho, David Aron S41 The influence of local context on multi-stakeholder alliance quality improvement activities: A multiple case study Jillian Harvey, Megan McHugh, Dennis Scanlon S42 Increasing physical activity in early care and education: Sustainability via active garden education (SAGE) Rebecca Lee, Erica Soltero, Nathan Parker, Lorna McNeill, Tracey Ledoux S43 Marking a decade of policy implementation: The successes and continuing challenges of a provincial school food and nutrition policy in Canada Jessie-Lee McIsaac, Kate MacLeod, Nicole Ata, Sherry Jarvis, Sara Kirk S44 Use of research evidence among state legislators who prioritize mental health and substance abuse issues Jonathan Purtle, Elizabeth Dodson, Ross Brownson S45 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 1 designs Brian Mittman, Geoffrey Curran S46 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 2 designs Geoffrey Curran S47 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 3 designs Jeffrey Pyne S48 Linking team level implementation leadership and implementation climate to individual level attitudes, behaviors, and implementation outcomes Gregory Aarons, Mark Ehrhart, Elisa Torres S49 Pinpointing the specific elements of local context that matter most to implementation outcomes: Findings from qualitative comparative analysis in the RE-inspire study of VA acute stroke care Edward Miech S50 The GO score: A new context-sensitive instrument to measure group organization level for providing and improving care Edward Miech S51 A research network approach for boosting implementation and improvement Kathleen Stevens, I.S.R.N. Steering Council S52 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - The value of qualitative methods in implementation research Alison Hamilton S53 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - Learning evaluation: The role of qualitative methods in dissemination and implementation research Deborah Cohen S54 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - Qualitative methods in D&I research Deborah Padgett S55 PANEL: Maps & models: The promise of network science for clinical D&I - Hospital network of sharing patients with acute and chronic diseases in California Alexandra Morshed S56 PANEL: Maps & models: The promise of network science for clinical D&I - The use of social network analysis to identify dissemination targets and enhance D&I research study recruitment for pre-exposure prophylaxis for HIV (PrEP) among men who have sex with men Rupa Patel S57 PANEL: Maps & models: The promise of network science for clinical D&I - Network and organizational factors related to the adoption of patient navigation services among rural breast cancer care providers Beth Prusaczyk S58 A theory of de-implementation based on the theory of healthcare professionals’ behavior and intention (THPBI) and the becker model of unlearning David C. Aron, Divya Gupta, Sherry Ball S59 Observation of registered dietitian nutritionist-patient encounters by dietetic interns highlights low awareness and implementation of evidence-based nutrition practice guidelines Rosa Hand, Jenica Abram, Taylor Wolfram S60 Program sustainability action planning: Building capacity for program sustainability using the program sustainability assessment tool Molly Hastings, Sarah Moreland-Russell S61 A review of D&I study designs in published study protocols Rachel Tabak, Alex Ramsey, Ana Baumann, Emily Kryzer, Katherine Montgomery, Ericka Lewis, Margaret Padek, Byron Powell, Ross Brownson S62 PANEL: Geographic variation in the implementation of public health services: Economic, organizational, and network determinants - Model simulation techniques to estimate the cost of implementing foundational public health services Cezar Brian Mamaril, Glen Mays, Keith Branham, Lava Timsina S63 PANEL: Geographic variation in the implementation of public health services: Economic, organizational, and network determinants - Inter-organizational network effects on the implementation of public health services Glen Mays, Rachel Hogg S64 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Implementation fidelity, coalition functioning, and community prevention system transformation using communities that care Abigail Fagan, Valerie Shapiro, Eric Brown S65 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Expanding capacity for implementation of communities that care at scale using a web-based, video-assisted training system Kevin Haggerty, David Hawkins S66 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Effects of communities that care on reducing youth behavioral health problems Sabrina Oesterle, David Hawkins, Richard Catalano S68 When interventions end: the dynamics of intervention de-adoption and replacement Virginia McKay, M. Margaret Dolcini, Lee Hoffer S69 Results from next-d: can a disease specific health plan reduce incident diabetes development among a national sample of working-age adults with pre-diabetes? Tannaz Moin, Jinnan Li, O. Kenrik Duru, Susan Ettner, Norman Turk, Charles Chan, Abigail Keckhafer, Robert Luchs, Sam Ho, Carol Mangione S70 Implementing smoking cessation interventions in primary care settings (STOP): using the interactive systems framework Peter Selby, Laurie Zawertailo, Nadia Minian, Dolly Balliunas, Rosa Dragonetti, Sarwar Hussain, Julia Lecce S71 Testing the Getting To Outcomes implementation support intervention in prevention-oriented, community-based settings Matthew Chinman, Joie Acosta, Patricia Ebener, Patrick S Malone, Mary Slaughter S72 Examining the reach of a multi-component farmers’ market implementation approach among low-income consumers in an urban context Darcy Freedman, Susan Flocke, Eunlye Lee, Kristen Matlack, Erika Trapl, Punam Ohri-Vachaspati, Morgan Taggart, Elaine Borawski S73 Increasing implementation of evidence-based health promotion practices at large workplaces: The CEOs Challenge Amanda Parrish, Jeffrey Harris, Marlana Kohn, Kristen Hammerback, Becca McMillan, Peggy Hannon S74 A qualitative assessment of barriers to nutrition promotion and obesity prevention in childcare Taren Swindle, Geoffrey Curran, Leanne Whiteside-Mansell, Wendy Ward S75 Documenting institutionalization of a health communication intervention in African American churches Cheryl Holt, Sheri Lou Santos, Erin Tagai, Mary Ann Scheirer, Roxanne Carter, Janice Bowie, Muhiuddin Haider, Jimmie Slade, Min Qi Wang S76 Reduction in hospital utilization by underserved patients through use of a community-medical home Andrew Masica, Gerald Ogola, Candice Berryman, Kathleen Richter S77 Sustainability of evidence-based lay health advisor programs in African American communities: A mixed methods investigation of the National Witness Project Rachel Shelton, Lina Jandorf, Deborah Erwin S78 Predicting the long-term uninsured population and analyzing their gaps in physical access to healthcare in South Carolina Khoa Truong S79 Using an evidence-based parenting intervention in churches to prevent behavioral problems among Filipino youth: A randomized pilot study Joyce R. Javier, Dean Coffey, Sheree M. Schrager, Lawrence Palinkas, Jeanne Miranda S80 Sustainability of elementary school-based health centers in three health-disparate southern communities Veda Johnson, Valerie Hutcherson, Ruth Ellis S81 Childhood obesity prevention partnership in Louisville: creative opportunities to engage families in a multifaceted approach to obesity prevention Anna Kharmats, Sandra Marshall-King, Monica LaPradd, Fannie Fonseca-Becker S82 Improvements in cervical cancer prevention found after implementation of evidence-based Latina prevention care management program Deanna Kepka, Julia Bodson, Echo Warner, Brynn Fowler S83 The OneFlorida data trust: Achieving health equity through research & training capacity building Elizabeth Shenkman, William Hogan, Folakami Odedina, Jessica De Leon, Monica Hooper, Olveen Carrasquillo, Renee Reams, Myra Hurt, Steven Smith, Jose Szapocznik, David Nelson, Prabir Mandal S84 Disseminating and sustaining medical-legal partnerships: Shared value and social return on investment James Teufe

Quantitative Genomic Dissection of Soybean Yield Components

Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTL a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within family predictions and predicting families not observed included in the calibration set

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set

Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max).

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits