1
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Schoemaker DL, Qiu Y, de Leon N, Hirsch CN, Kaeppler SM. Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize. G3 (BETHESDA, MD.) 2023; 14:jkad256. [PMID: 37950891 PMCID: PMC10755172 DOI: 10.1093/g3journal/jkad256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/13/2023]
Abstract
The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.
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Affiliation(s)
- Dylan L Schoemaker
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Yinjie Qiu
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Shawn M Kaeppler
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin—Madison, Middleton, WI 53562, USA
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2
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Zhang T, Keele GR, Gyuricza IG, Vincent M, Brunton C, Bell TA, Hock P, Shaw GD, Munger SC, de Villena FPM, Ferris MT, Paulo JA, Gygi SP, Churchill GA. Multi-omics analysis identifies drivers of protein phosphorylation. Genome Biol 2023; 24:52. [PMID: 36944993 PMCID: PMC10031968 DOI: 10.1186/s13059-023-02892-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated. RESULTS We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes. CONCLUSIONS Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
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Affiliation(s)
- Tian Zhang
- Harvard Medical School, Boston, MA, 02115, USA
| | | | | | | | | | - Timothy A Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ginger D Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Martin T Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
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3
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Hong-Le T, Crouse WL, Keele GR, Holl K, Seshie O, Tschannen M, Craddock A, Das SK, Szalanczy AM, McDonald B, Grzybowski M, Klotz J, Sharma NK, Geurts AM, Key CCC, Hawkins G, Valdar W, Mott R, Solberg Woods LC. Genetic Mapping of Multiple Traits Identifies Novel Genes for Adiposity, Lipids, and Insulin Secretory Capacity in Outbred Rats. Diabetes 2023; 72:135-148. [PMID: 36219827 PMCID: PMC9797320 DOI: 10.2337/db22-0252] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 10/04/2022] [Indexed: 01/21/2023]
Abstract
Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids, and other metabolic traits. Physiological traits were measured in 1,519 male HS rats, with liver and adipose transcriptomes measured in >410 rats. Genotypes were imputed from low-coverage whole-genome sequencing. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), using both single nucleotide polymorphism (SNP)- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 14 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 10 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for fat pad weight and serum triglyceride pQTLs on Chr1, Krtcap3 for fat pad weight and serum triglyceride pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20, and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knockdown/out models, thereby shedding light on novel regulators of obesity.
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Affiliation(s)
- Thu Hong-Le
- Genetics Institute, University College London, London, U.K
| | - Wesley L. Crouse
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Katie Holl
- Medical College of Wisconsin, Milwaukee, WI
| | - Osborne Seshie
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | | | - Ann Craddock
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Swapan K. Das
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Alexandria M. Szalanczy
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Bailey McDonald
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | | | | | - Neeraj K. Sharma
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | | | - Chia-Chi Chuang Key
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Gregory Hawkins
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Richard Mott
- Genetics Institute, University College London, London, U.K
| | - Leah C. Solberg Woods
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC
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4
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Xiao H, Bozi LHM, Sun Y, Riley CL, Philip VM, Chen M, Li J, Zhang T, Mills EL, Emont MP, Sun W, Reddy A, Garrity R, Long J, Becher T, Vitas LP, Laznik-Bogoslavski D, Ordonez M, Liu X, Chen X, Wang Y, Liu W, Tran N, Liu Y, Zhang Y, Cypess AM, White AP, He Y, Deng R, Schöder H, Paulo JA, Jedrychowski MP, Banks AS, Tseng YH, Cohen P, Tsai LT, Rosen ED, Klein S, Chondronikola M, McAllister FE, Van Bruggen N, Huttlin EL, Spiegelman BM, Churchill GA, Gygi SP, Chouchani ET. Architecture of the outbred brown fat proteome defines regulators of metabolic physiology. Cell 2022; 185:4654-4673.e28. [PMID: 36334589 PMCID: PMC10040263 DOI: 10.1016/j.cell.2022.10.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 07/18/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.
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Affiliation(s)
- Haopeng Xiao
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
| | - Luiz H M Bozi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Yizhi Sun
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher L Riley
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Mandy Chen
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Evanna L Mills
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Margo P Emont
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Wenfei Sun
- Department of Bioengineering, Stanford University, CA 94305, USA; Department of Molecular and Cellular Physiology, Stanford University, CA 94305, USA
| | - Anita Reddy
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Garrity
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jiani Long
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Tobias Becher
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, NY 10065, USA
| | - Laura Potano Vitas
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Martha Ordonez
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Xinyue Liu
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Xiong Chen
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yun Wang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Weihai Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nhien Tran
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yitong Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yang Zhang
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Aaron M Cypess
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew P White
- Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Yuchen He
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Rebecca Deng
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Mark P Jedrychowski
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alexander S Banks
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Yu-Hua Tseng
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Paul Cohen
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, NY 10065, USA
| | - Linus T Tsai
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Evan D Rosen
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Samuel Klein
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO 63110, USA
| | | | | | | | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Bruce M Spiegelman
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Edward T Chouchani
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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5
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Mural RV, Sun G, Grzybowski M, Tross MC, Jin H, Smith C, Newton L, Andorf CM, Woodhouse MR, Thompson AM, Sigmon B, Schnable JC. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience 2022; 11:6673780. [PMID: 35997208 PMCID: PMC9396454 DOI: 10.1093/gigascience/giac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Indexed: 11/14/2022] Open
Abstract
Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.
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Affiliation(s)
- Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Guangchao Sun
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Marcin Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Michael C Tross
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Hongyu Jin
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Christine Smith
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Linsey Newton
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50010, USA.,Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | | | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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6
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Genome-wide association mapping of resistance to the sorghum aphid in Sorghum bicolor. Genomics 2022; 114:110408. [PMID: 35716823 DOI: 10.1016/j.ygeno.2022.110408] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 05/17/2022] [Accepted: 06/10/2022] [Indexed: 12/14/2022]
Abstract
Since 2013, the sorghum aphid (SA), Melanaphis sorghi (Theobald), has been a serious pest that hampers all types of sorghum production in the U.S. Known sorghum aphid resistance in sorghum is limited to a few genetic regions on SBI-06. In this study, a subset of the Sorghum Association Panel (SAP) was used along with some additional lines to identify genomic regions that confer sorghum aphid resistance. SAP lines were grown in the field and visually evaluated for SA resistance during the growing seasons of 2019 and 2020 in Tifton, GA. In 2020, the SAP accessions were also evaluated for SA resistance in the field using drone-based high throughput phenotyping (HTP). Flowering time was recorded in the field to confirm that our methods were sufficient for identifying known quantitative trait loci (QTL). This study combined phenotypic data from field-based visual ratings and reflectance data to identify genome-wide associated (GWAS) marker-trait associations (MTA) using genotyping-by-sequencing (GBS) data. Several MTAs were identified for SA-related traits across the genome, with a few common markers that were consistently identified on SBI-08 and SBI-10 for aphid count and plant damage, as well as loci for reflectance-based traits on SBI-02, SBI-03, and SBI-05. Candidate genes encoding leucine-rich repeats (LRR), Avr proteins, lipoxygenases (LOXs), calmodulins (CAM) dependent protein kinase, WRKY transcription factors, flavonoid biosynthesis genes, and 12-oxo-phytodienoic acid reductase were identified near SNPs that had significant associations with different SA traits. In this study, flowering time-related genes were also identified as a positive control for the methods. The total phenotypic variation explained by significant SNPs across SA-scored traits, reflectance data, and flowering time ranged from 6 to 61%, while the heritability value ranged from 4 to 69%. This study identified three new sources of resistant lines to sorghum aphid. These results supported the existing literature, and also revealed several new loci. Markers identified in this study will support marker-assisted breeding for sorghum aphid resistance.
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7
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Crouse WL, Keele GR, Gastonguay MS, Churchill GA, Valdar W. A Bayesian model selection approach to mediation analysis. PLoS Genet 2022; 18:e1010184. [PMID: 35533209 PMCID: PMC9129027 DOI: 10.1371/journal.pgen.1010184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 05/24/2022] [Accepted: 04/04/2022] [Indexed: 01/09/2023] Open
Abstract
Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for the genetic effects on the other. Alternatively, the phenotypes may be causally unrelated but share genetic loci. Mediation analysis represents a class of causal inference approaches used to determine which of these scenarios is most plausible. We have developed a general approach to mediation analysis based on Bayesian model selection and have implemented it in an R package, bmediatR. Bayesian model selection provides a flexible framework that can be tailored to different analyses. Our approach can incorporate prior information about the likelihood of models and the strength of causal effects. It can also accommodate multiple genetic variants or multi-state haplotypes. Our approach reports posterior probabilities that can be useful in interpreting uncertainty among competing models. We compared bmediatR with other popular methods, including the Sobel test, Mendelian randomization, and Bayesian network analysis using simulated data. We found that bmediatR performed as well or better than these alternatives in most scenarios. We applied bmediatR to proteome data from Diversity Outbred (DO) mice, a multi-parent population, and demonstrate the power of mediation with multi-state haplotypes. We also applied bmediatR to data from human cell lines to identify transcripts that are mediated through or are expressed independently from local chromatin accessibility. We demonstrate that Bayesian model selection provides a powerful and versatile approach to identify causal relationships in genetic studies using model organism or human data.
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Affiliation(s)
- Wesley L. Crouse
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gregory R. Keele
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | | | | | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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8
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LaPorte MF, Vachev M, Fenn M, Diepenbrock C. Simultaneous dissection of grain carotenoid levels and kernel color in biparental maize populations with yellow-to-orange grain. G3 (BETHESDA, MD.) 2022; 12:6506523. [PMID: 35100389 PMCID: PMC8895983 DOI: 10.1093/g3journal/jkac006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/30/2021] [Indexed: 01/19/2023]
Abstract
Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the US maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color quantitative trait loci ranging from 2.4% to 17.5%. These quantitative trait loci were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.
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Affiliation(s)
- Mary-Francis LaPorte
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Mishi Vachev
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Matthew Fenn
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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9
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Tross MC, Gaillard M, Zwiener M, Miao C, Grove RJ, Li B, Benes B, Schnable JC. 3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves. PeerJ 2022; 9:e12628. [PMID: 35036135 PMCID: PMC8710048 DOI: 10.7717/peerj.12628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/21/2021] [Indexed: 12/22/2022] Open
Abstract
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
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Affiliation(s)
- Michael C Tross
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Complex Biosystems Graduate Program, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Mathieu Gaillard
- Computer Science, Purdue University, West Lafayette, IN, United States of America
| | - Mackenzie Zwiener
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Chenyong Miao
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Ryleigh J Grove
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Lincoln North Star High School, Lincoln, NE, United States of America
| | - Bosheng Li
- Computer Science, Purdue University, West Lafayette, IN, United States of America
| | - Bedrich Benes
- Computer Science, Purdue University, West Lafayette, IN, United States of America.,Department of Computer Graphics Technology, Purdue University, West Lafayette, IN, United States of America
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Complex Biosystems Graduate Program, University of Nebraska - Lincoln, Lincoln, NE, United States of America
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10
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Keele GR, Zhang T, Pham DT, Vincent M, Bell TA, Hock P, Shaw GD, Paulo JA, Munger SC, Pardo-Manuel de Villena F, Ferris MT, Gygi SP, Churchill GA. Regulation of protein abundance in genetically diverse mouse populations. CELL GENOMICS 2021; 1:100003. [PMID: 36212994 PMCID: PMC9536773 DOI: 10.1016/j.xgen.2021.100003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 04/01/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022]
Abstract
Genetically diverse mouse populations are powerful tools for characterizing the regulation of the proteome and its relationship to whole-organism phenotypes. We used mass spectrometry to profile and quantify the abundance of 6,798 proteins in liver tissue from mice of both sexes across 58 Collaborative Cross (CC) inbred strains. We previously collected liver proteomics data from the related Diversity Outbred (DO) mice and their founder strains. We show concordance across the proteomics datasets despite being generated from separate experiments, allowing comparative analysis. We map protein abundance quantitative trait loci (pQTLs), identifying 1,087 local and 285 distal in the CC mice and 1,706 local and 414 distal in the DO mice. We find that regulatory effects on individual proteins are conserved across the mouse populations, in particular for local genetic variation and sex differences. In comparison, proteins that form complexes are often co-regulated, displaying varying genetic architectures, and overall show lower heritability and map fewer pQTLs. We have made this resource publicly available to enable quantitative analyses of the regulation of the proteome.
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Affiliation(s)
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Timothy A. Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ginger D. Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | | | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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11
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Diepenbrock CH, Ilut DC, Magallanes-Lundback M, Kandianis CB, Lipka AE, Bradbury PJ, Holland JB, Hamilton JP, Wooldridge E, Vaillancourt B, Góngora-Castillo E, Wallace JG, Cepela J, Mateos-Hernandez M, Owens BF, Tiede T, Buckler ES, Rocheford T, Buell CR, Gore MA, DellaPenna D. Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain. THE PLANT CELL 2021; 33:882-900. [PMID: 33681994 PMCID: PMC8226291 DOI: 10.1093/plcell/koab032] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 01/26/2021] [Indexed: 05/03/2023]
Abstract
Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.
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Affiliation(s)
| | - Daniel C Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Catherine B Kandianis
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Alexander E Lipka
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Peter J Bradbury
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- United States Department of Agriculture—Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - James B Holland
- United States Department of Agriculture—Agricultural Research Service, Plant Science Research Unit, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Edmund Wooldridge
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Elsa Góngora-Castillo
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Jason G Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia 30602
| | - Jason Cepela
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Maria Mateos-Hernandez
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Brenda F Owens
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Tyler Tiede
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- United States Department of Agriculture—Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Michael A Gore
- Authors for correspondence: (C.H.D.), (M.A.G.), and (D.D.P.)
| | - Dean DellaPenna
- Authors for correspondence: (C.H.D.), (M.A.G.), and (D.D.P.)
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12
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Keele GR, Prokop JW, He H, Holl K, Littrell J, Deal AW, Kim Y, Kyle PB, Attipoe E, Johnson AC, Uhl KL, Sirpilla OL, Jahanbakhsh S, Robinson M, Levy S, Valdar W, Garrett MR, Solberg Woods LC. Sept8/SEPTIN8 involvement in cellular structure and kidney damage is identified by genetic mapping and a novel human tubule hypoxic model. Sci Rep 2021; 11:2071. [PMID: 33483609 PMCID: PMC7822875 DOI: 10.1038/s41598-021-81550-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 01/05/2021] [Indexed: 01/29/2023] Open
Abstract
Chronic kidney disease (CKD), which can ultimately progress to kidney failure, is influenced by genetics and the environment. Genes identified in human genome wide association studies (GWAS) explain only a small proportion of the heritable variation and lack functional validation, indicating the need for additional model systems. Outbred heterogeneous stock (HS) rats have been used for genetic fine-mapping of complex traits, but have not previously been used for CKD traits. We performed GWAS for urinary protein excretion (UPE) and CKD related serum biochemistries in 245 male HS rats. Quantitative trait loci (QTL) were identified using a linear mixed effect model that tested for association with imputed genotypes. Candidate genes were identified using bioinformatics tools and targeted RNAseq followed by testing in a novel in vitro model of human tubule, hypoxia-induced damage. We identified two QTL for UPE and five for serum biochemistries. Protein modeling identified a missense variant within Septin 8 (Sept8) as a candidate for UPE. Sept8/SEPTIN8 expression increased in HS rats with elevated UPE and tubulointerstitial injury and in the in vitro hypoxia model. SEPTIN8 is detected within proximal tubule cells in human kidney samples and localizes with acetyl-alpha tubulin in the culture system. After hypoxia, SEPTIN8 staining becomes diffuse and appears to relocalize with actin. These data suggest a role of SEPTIN8 in cellular organization and structure in response to environmental stress. This study demonstrates that integration of a rat genetic model with an environmentally induced tubule damage system identifies Sept8/SEPTIN8 and informs novel aspects of the complex gene by environmental interactions contributing to CKD risk.
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Affiliation(s)
| | - Jeremy W Prokop
- HudsonAlpha Institute, Huntsville, AL, USA
- Department of Pediatrics and Human Development, Department of Pharmacology, Michigan State University, Grand Rapids, MI, USA
| | - Hong He
- Departments of Pediatrics and Physiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Katie Holl
- Departments of Pediatrics and Physiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - John Littrell
- Departments of Pediatrics and Physiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Aaron W Deal
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yunjung Kim
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick B Kyle
- Department of Pharmacology, Medicine (Nephrology), Pediatrics (Genetics), University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pathology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Esinam Attipoe
- Department of Pharmacology, Medicine (Nephrology), Pediatrics (Genetics), University of Mississippi Medical Center, Jackson, MS, USA
| | - Ashley C Johnson
- Department of Pharmacology, Medicine (Nephrology), Pediatrics (Genetics), University of Mississippi Medical Center, Jackson, MS, USA
| | - Katie L Uhl
- Department of Pediatrics and Human Development, Department of Pharmacology, Michigan State University, Grand Rapids, MI, USA
| | - Olivia L Sirpilla
- Department of Pediatrics and Human Development, Department of Pharmacology, Michigan State University, Grand Rapids, MI, USA
| | - Seyedehameneh Jahanbakhsh
- Department of Pediatrics and Human Development, Department of Pharmacology, Michigan State University, Grand Rapids, MI, USA
| | | | - Shawn Levy
- HudsonAlpha Institute, Huntsville, AL, USA
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael R Garrett
- Department of Pharmacology, Medicine (Nephrology), Pediatrics (Genetics), University of Mississippi Medical Center, Jackson, MS, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA.
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13
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Crouse WL, Kelada SNP, Valdar W. Inferring the Allelic Series at QTL in Multiparental Populations. Genetics 2020; 216:957-983. [PMID: 33082282 PMCID: PMC7768242 DOI: 10.1534/genetics.120.303393] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/12/2020] [Indexed: 12/25/2022] Open
Abstract
Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.
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Affiliation(s)
- Wesley L Crouse
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Samir N P Kelada
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
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14
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Binenbaum I, Atamni HAT, Fotakis G, Kontogianni G, Koutsandreas T, Pilalis E, Mott R, Himmelbauer H, Iraqi FA, Chatziioannou AA. Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity. BMC Genomics 2020; 21:761. [PMID: 33143653 PMCID: PMC7640698 DOI: 10.1186/s12864-020-07173-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 10/21/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population. RESULTS We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms. CONCLUSION Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.
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Affiliation(s)
- Ilona Binenbaum
- Division of Pediatric Hematology-Oncology, First Department of Pediatrics, National and Kapodistrian University of Athens, Athens, Greece
- Department of Biology, University of Patras, Patras, Greece
| | - Hanifa Abu-Toamih Atamni
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Georgios Fotakis
- Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
- e-NIOS PC, Kallithea, Athens, Greece
| | - Georgia Kontogianni
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Theodoros Koutsandreas
- e-NIOS PC, Kallithea, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Eleftherios Pilalis
- e-NIOS PC, Kallithea, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Richard Mott
- Department of Genetics, University College of London, London, UK
| | - Heinz Himmelbauer
- Institute of Computational Biology, Department of Biotechnology, University of Life Sciences and Natural Resources, Vienna (BOKU), Vienna, Austria
- Centre for Genomic Regulation (CRG), Barcelona, Spain
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Aristotelis A Chatziioannou
- e-NIOS PC, Kallithea, Athens, Greece.
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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15
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Edmondson EF, Gatti DM, Ray FA, Garcia EL, Fallgren CM, Kamstock DA, Weil MM. Genomic mapping in outbred mice reveals overlap in genetic susceptibility for HZE ion- and γ-ray-induced tumors. SCIENCE ADVANCES 2020; 6:eaax5940. [PMID: 32494593 PMCID: PMC7159905 DOI: 10.1126/sciadv.aax5940] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 01/14/2020] [Indexed: 05/02/2023]
Abstract
Cancer risk from galactic cosmic radiation exposure is considered a potential "showstopper" for a manned mission to Mars. Calculating the actual risks confronted by spaceflight crews is complicated by our limited understanding of the carcinogenic effects of high-charge, high-energy (HZE) ions, a radiation type for which no human exposure data exist. Using a mouse model of genetic diversity, we find that the histotype spectrum of HZE ion-induced tumors is similar to the spectra of spontaneous and γ-ray-induced tumors and that the genomic loci controlling susceptibilities overlap between groups for some tumor types. Where it occurs, this overlap indicates shared tumorigenesis mechanisms regardless of the type of radiation exposure and supports the use of human epidemiological data from γ-ray exposures to predict cancer risks from galactic cosmic rays.
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Affiliation(s)
- E. F. Edmondson
- Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
- Colorado State University, Fort Collins, CO 80523, USA
| | - D. M. Gatti
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - F. A. Ray
- Colorado State University, Fort Collins, CO 80523, USA
| | - E. L. Garcia
- Colorado State University, Fort Collins, CO 80523, USA
| | | | | | - M. M. Weil
- Colorado State University, Fort Collins, CO 80523, USA
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16
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Li C, Wu X, Li Y, Shi Y, Song Y, Zhang D, Li Y, Wang T. Genetic architecture of phenotypic means and plasticities of kernel size and weight in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3309-3320. [PMID: 31555889 DOI: 10.1007/s00122-019-03426-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 09/11/2019] [Indexed: 05/11/2023]
Abstract
Genetic relationships between the phenotypic means and plasticities of kernel size and weight revealed the common genetic control of these traits in maize. Kernel size and weight are crucial components of grain yield in maize, and phenotypic plasticity in these traits facilitates adaptations to changing environments. Elucidating the genetic architecture of the mean phenotypic values and plasticities of kernel size and weight may be essential for breeding climate-robust maize varieties. Here, a maize nested association mapping (CN-NAM) population and association panel were grown in different environments. A joint linkage analysis and genome-wide association mapping were performed for five kernel size and weight phenotypic traits and two phenotypic plasticity measures. The mean phenotypes and plasticities were significantly correlated. The overall results of quantitative trait locus (QTL) and candidate gene analyses indicated moderate and high levels of common genetic control for the two traits. Furthermore, the mean phenotypes or plasticities of the hundred-kernel weight and volume were commonly regulated to a high degree. One pleiotropic locus on chromosome 10 simultaneously controlled the mean phenotypic values and plasticities of kernel size and weight. Therefore, the plasticity of kernel size and weight might be indirectly selected during maize breeding; however, selecting for high or low plasticity in combination with high or low mean phenotypic values of kernel size and weight traits may be difficult.
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Affiliation(s)
- Chunhui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xun Wu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yongxiang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunsu Shi
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yanchun Song
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dengfeng Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yu Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Tianyu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
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17
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Abstract
In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.
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18
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Samayoa LF, Cao A, Santiago R, Malvar RA, Butrón A. Genome-wide association analysis for fumonisin content in maize kernels. BMC PLANT BIOLOGY 2019; 19:166. [PMID: 31029090 PMCID: PMC6486958 DOI: 10.1186/s12870-019-1759-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/04/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND Plant breeding has been proposed as one of the most effective and environmentally safe methods to control fungal infection and to reduce fumonisin accumulation. However, conventional breeding can be hampered by the complex genetic architecture of resistance to fumonisin accumulation and marker-assisted selection is proposed as an efficient alternative. In the current study, GWAS has been performed for the first time for detecting high-resolution QTL for resistance to fumonisin accumulation in maize kernels complementing published GWAS results for Fusarium ear rot. RESULTS Thirty-nine SNPs significantly associated with resistance to fumonisin accumulation in maize kernels were found and clustered into 17 QTL. Novel QTLs for fumonisin content would be at bins 3.02, 5.02, 7.05 and 8.07. Genes with annotated functions probably implicated in resistance to pathogens based on previous studies have been highlighted. CONCLUSIONS Breeding approaches to fix favorable functional variants for genes implicated in maize immune response signaling may be especially useful to reduce kernel contamination with fumonisins without significantly interfering in mycelia development and growth and, consequently, in the beneficial endophytic behavior of Fusarium verticillioides.
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Affiliation(s)
- L. F. Samayoa
- Misión Biológica de Galicia (MBG - CSIC), Box 28, 36080 Pontevedra, Spain
- Present address at department of Crop Science, North Carolina State University, Raleigh, NC 27695 USA
| | - A. Cao
- Misión Biológica de Galicia (MBG - CSIC), Box 28, 36080 Pontevedra, Spain
- Facultad de Biología, Department Biología Vegetal y Ciencias del Suelo, Universidad de Vigo, As Lagoas Marcosende, 36310 Vigo, Spain
- Agrobiología Ambiental, Calidad de Suelos y Plantas (BVE1-UVIGO), Unidad Asociada a la MBG – CSIC, 36143 Pontevedra, Spain
| | - R. Santiago
- Facultad de Biología, Department Biología Vegetal y Ciencias del Suelo, Universidad de Vigo, As Lagoas Marcosende, 36310 Vigo, Spain
- Agrobiología Ambiental, Calidad de Suelos y Plantas (BVE1-UVIGO), Unidad Asociada a la MBG – CSIC, 36143 Pontevedra, Spain
| | - R. A. Malvar
- Misión Biológica de Galicia (MBG - CSIC), Box 28, 36080 Pontevedra, Spain
- Agrobiología Ambiental, Calidad de Suelos y Plantas (BVE1-UVIGO), Unidad Asociada a la MBG – CSIC, 36143 Pontevedra, Spain
| | - A. Butrón
- Misión Biológica de Galicia (MBG - CSIC), Box 28, 36080 Pontevedra, Spain
- Agrobiología Ambiental, Calidad de Suelos y Plantas (BVE1-UVIGO), Unidad Asociada a la MBG – CSIC, 36143 Pontevedra, Spain
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19
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Noll KE, Ferris MT, Heise MT. The Collaborative Cross: A Systems Genetics Resource for Studying Host-Pathogen Interactions. Cell Host Microbe 2019; 25:484-498. [PMID: 30974083 PMCID: PMC6494101 DOI: 10.1016/j.chom.2019.03.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Host genetic variation has a major impact on infectious disease susceptibility. The study of pathogen resistance genes, largely aided by mouse models, has significantly advanced our understanding of infectious disease pathogenesis. The Collaborative Cross (CC), a newly developed multi-parental mouse genetic reference population, serves as a tractable model system to study how pathogens interact with genetically diverse populations. In this review, we summarize progress utilizing the CC as a platform to develop improved models of pathogen-induced disease and to map polymorphic host response loci associated with variation in susceptibility to pathogens.
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Affiliation(s)
- Kelsey E Noll
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Martin T Ferris
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Mark T Heise
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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20
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Ward BP, Brown-Guedira G, Kolb FL, Van Sanford DA, Tyagi P, Sneller CH, Griffey CA. Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia. PLoS One 2019; 14:e0208217. [PMID: 30794545 PMCID: PMC6386437 DOI: 10.1371/journal.pone.0208217] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 02/05/2019] [Indexed: 01/19/2023] Open
Abstract
Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.
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Affiliation(s)
- Brian P. Ward
- Department Of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Gina Brown-Guedira
- Eastern Regional Small Grains Genotyping Laboratory, USDA-ARS, Raleigh, North Carolina, United States of America
| | - Frederic L. Kolb
- Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America
| | - David A. Van Sanford
- Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky, United States of America
| | - Priyanka Tyagi
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Clay H. Sneller
- Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio, United States of America
| | - Carl A. Griffey
- Department Of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, Virginia, United States of America
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21
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Stagnati L, Lanubile A, Samayoa LF, Bragalanti M, Giorni P, Busconi M, Holland JB, Marocco A. A Genome Wide Association Study Reveals Markers and Genes Associated with Resistance to Fusarium verticillioides Infection of Seedlings in a Maize Diversity Panel. G3 (BETHESDA, MD.) 2019; 9:571-579. [PMID: 30567831 PMCID: PMC6385986 DOI: 10.1534/g3.118.200916] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 12/18/2018] [Indexed: 12/12/2022]
Abstract
Fusarium verticillioides infects maize, causing ear rot, yield loss and contamination by fumonisin mycotoxins. The fungus can be transmitted via kernels and cause systemic infection in maize. Maize resistance to the fungus may occur at different developmental stages, from seedling to maturity. Resistance during kernel germination is part of the plant-pathogen interaction and so far this aspect has not been investigated. In the present study, a genome wide association study (GWAS) of resistance to Fusarium during the seedling developmental stage was conducted in a maize diversity panel using 226,446 SNP markers. Seedling germination and disease phenotypes were scored on artificially inoculated kernels using the rolled towel assay. GWAS identified 164 SNPs significantly associated with the traits examined. Four SNPs were associated with disease severity score after inoculation, 153 were associated with severity in asymptomatic kernels and 7 with the difference between the severity ratings in inoculated and non-inoculated seeds. A set of genes containing or physically near the significant SNPs were identified as candidates for Fusarium resistance at the seedling stage. Functional analysis revealed that many of these genes are directly involved in plant defense against pathogens and stress responses, including transcription factors, chitinase, cytochrome P450, and ubiquitination proteins. In addition, 25 genes were found in high linkage disequilibrium with the associated SNPs identified by GWAS and four of them directly involved in disease resistance. These findings contribute to understanding the complex system of maize-F. verticillioides and may improve genomic selection for Fusarium resistance at the seedling stage.
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Affiliation(s)
- Lorenzo Stagnati
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Alessandra Lanubile
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Luis F Samayoa
- Department of Crop & Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Mario Bragalanti
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Paola Giorni
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Matteo Busconi
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - James B Holland
- Department of Crop & Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
- U.S. Department of Agriculture-Agricultural Research Service, Plant Science Research Unit, Raleigh, North Carolina 27695
| | - Adriano Marocco
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
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22
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Schaefer RJ, Michno JM, Jeffers J, Hoekenga O, Dilkes B, Baxter I, Myers CL. Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize. THE PLANT CELL 2018; 30:2922-2942. [PMID: 30413654 PMCID: PMC6354270 DOI: 10.1105/tpc.18.00299] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 10/08/2018] [Accepted: 10/31/2018] [Indexed: 05/02/2023]
Abstract
Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.
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Affiliation(s)
- Robert J Schaefer
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jean-Michel Michno
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Joseph Jeffers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Owen Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, New York 14850
| | - Brian Dilkes
- Department of Biochemistry, Purdue University, West Lafayette, Indiana 47907
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- U.S. Department of Agriculture-Agricultural Research Service Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
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23
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Sabourin JA, Cropp CD, Sung H, Brody LC, Bailey-Wilson JE, Wilson AF. ComPaSS-GWAS: A method to reduce type I error in genome-wide association studies when replication data are not available. Genet Epidemiol 2018; 43:102-111. [PMID: 30334581 DOI: 10.1002/gepi.22168] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/10/2018] [Accepted: 09/26/2018] [Indexed: 01/22/2023]
Abstract
Results from association studies are traditionally corroborated by replicating the findings in an independent data set. Although replication studies may be comparable for the main trait or phenotype of interest, it is unlikely that secondary phenotypes will be comparable across studies, making replication problematic. Alternatively, there may simply not be a replication sample available because of the nature or frequency of the phenotype. In these situations, an approach based on complementary pairs stability selection for genome-wide association study (ComPaSS-GWAS), is proposed as an ad-hoc alternative to replication. In this method, the sample is randomly split into two conditionally independent halves multiple times (resamples) and a GWAS is performed on each half in each resample. Similar in spirit to testing for association with independent discovery and replication samples, a marker is corroborated if its p-value is significant in both halves of the resample. Simulation experiments were performed for both nongenetic and genetic models. The type I error rate and power of ComPaSS-GWAS were determined and compared to the statistical properties of a traditional GWAS. Simulation results show that the type I error rate decreased as the number of resamples increased with only a small reduction in power and that these results were comparable with those from a traditional GWAS. Blood levels of vitamin pyridoxal 5'-phosphate from the Trinity Student Study (TSS) were used to validate this approach. The results from the validation study were compared to, and were consistent with, those obtained from previously published independent replication data and functional studies.
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Affiliation(s)
- Jeremy A Sabourin
- Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Baltimore, Maryland
| | - Cheryl D Cropp
- Statistical Genetics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Baltimore, Maryland.,Integrated Cancer Genomics Division, Translational Genomics Research Institute (TGen), Phoenix, Arizona.,McWhorter School of Pharmacy, Samford University, Birmingham, Alabama
| | - Heejong Sung
- Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Baltimore, Maryland
| | - Lawrence C Brody
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Bethesda, Maryland
| | - Joan E Bailey-Wilson
- Statistical Genetics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Baltimore, Maryland
| | - Alexander F Wilson
- Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute/National Institutes of Health (NHGRI/NIH), Baltimore, Maryland
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24
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Toosi A, Fernando RL, Dekkers JCM. Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis. Genet Sel Evol 2018; 50:32. [PMID: 29914353 PMCID: PMC6006859 DOI: 10.1186/s12711-018-0402-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 06/01/2018] [Indexed: 12/18/2022] Open
Abstract
Background Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method. Methods We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison. Results SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate. Conclusions This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.
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Affiliation(s)
- Ali Toosi
- Cobb-Vantress Inc., 4703 US HWY 412 E, Siloam Springs, AR, 72761, USA.
| | - Rohan L Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50010, USA
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, 50010, USA
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25
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Abstract
Heterogeneous Stock (HS) populations allow for fine-resolution genetic mapping of a variety of complex traits. HS mice and rats were created from breeding together eight inbred strains, followed by maintaining the colony in a manner that minimizes inbreeding. After 50 or more generations of breeding, the resulting animals' chromosomes represent a genetic mosaic of the founders' haplotypes, with the average distance between recombination events in the centiMorgan range. This allows for genetic mapping to only a few Mb, a much smaller region than what can be identified using traditional F2 intercross or backcross mapping strategies. HS animals have been used to fine-map a variety of complex traits including anxiety and fear behaviors, diabetes, asthma, and heart disease, among others. Once a quantitative trait locus (QTL) has been identified, founder sequence and expression analysis can be used to identify underlying causal genes. In the following review, we provide an overview of how HS rats and mice have been used to identify genetic loci, and in some cases the causal genes, underlying complex traits. We discuss the creation and breeding strategies for both HS rats and mice. We then discuss the statistical analyses used to identify genetic loci, as well as strategies to identify causal genes underlying these loci. We end the chapter by discussing limitations faced when using HS populations, including several statistical challenges that have not been fully resolved.
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Affiliation(s)
- Leah C Solberg Woods
- Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53130, USA.
| | - Richard Mott
- UCL Genetics Institute, University College London, Gower St., London, WC1E 6BT, UK
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26
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Keele GR, Prokop JW, He H, Holl K, Littrell J, Deal A, Francic S, Cui L, Gatti DM, Broman KW, Tschannen M, Tsaih SW, Zagloul M, Kim Y, Baur B, Fox J, Robinson M, Levy S, Flister MJ, Mott R, Valdar W, Solberg Woods LC. Genetic Fine-Mapping and Identification of Candidate Genes and Variants for Adiposity Traits in Outbred Rats. Obesity (Silver Spring) 2018; 26:213-222. [PMID: 29193816 PMCID: PMC5740008 DOI: 10.1002/oby.22075] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/20/2017] [Accepted: 10/21/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Obesity is a major risk factor for multiple diseases and is in part heritable, yet the majority of causative genetic variants that drive excessive adiposity remain unknown. Here, outbred heterogeneous stock (HS) rats were used in controlled environmental conditions to fine-map novel genetic modifiers of adiposity. METHODS Body weight and visceral fat pad weights were measured in male HS rats that were also genotyped genome-wide. Quantitative trait loci (QTL) were identified by genome-wide association of imputed single-nucleotide polymorphism (SNP) genotypes using a linear mixed effect model that accounts for unequal relatedness between the HS rats. Candidate genes were assessed by protein modeling and mediation analysis of expression for coding and noncoding variants, respectively. RESULTS HS rats exhibited large variation in adiposity traits, which were highly heritable and correlated with metabolic health. Fine-mapping of fat pad weight and body weight revealed three QTL and prioritized five candidate genes. Fat pad weight was associated with missense SNPs in Adcy3 and Prlhr and altered expression of Krtcap3 and Slc30a3, whereas Grid2 was identified as a candidate within the body weight locus. CONCLUSIONS These data demonstrate the power of HS rats for identification of known and novel heritable mediators of obesity traits.
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Affiliation(s)
- Gregory R. Keele
- Department of Genetics, University of North Carolina at Chapel Hill, NC
| | | | - Hong He
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Katie Holl
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - John Littrell
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Aaron Deal
- Wake Forest School of Medicine, Department of Internal Medicine, Winston Salem, NC
| | - Sanja Francic
- University College London Genetics Institute, London, UK
| | - Leilei Cui
- University College London Genetics Institute, London, UK
| | | | - Karl W. Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI
| | - Michael Tschannen
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Shirng-Wern Tsaih
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Maie Zagloul
- Department of Genetics, University of North Carolina at Chapel Hill, NC
| | - Yunjung Kim
- Department of Genetics, University of North Carolina at Chapel Hill, NC
| | - Brittany Baur
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Joseph Fox
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | | | | | - Michael J. Flister
- Medical College of Wisconsin, Departments of Pediatrics and Physiology, Milwaukee, WI
| | - Richard Mott
- University College London Genetics Institute, London, UK
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, NC
| | - Leah C Solberg Woods
- Wake Forest School of Medicine, Department of Internal Medicine, Winston Salem, NC
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27
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Diepenbrock CH, Kandianis CB, Lipka AE, Magallanes-Lundback M, Vaillancourt B, Góngora-Castillo E, Wallace JG, Cepela J, Mesberg A, Bradbury PJ, Ilut DC, Mateos-Hernandez M, Hamilton J, Owens BF, Tiede T, Buckler ES, Rocheford T, Buell CR, Gore MA, DellaPenna D. Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain. THE PLANT CELL 2017; 29:2374-2392. [PMID: 28970338 PMCID: PMC5774569 DOI: 10.1105/tpc.17.00475] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 08/23/2017] [Accepted: 09/29/2017] [Indexed: 05/19/2023]
Abstract
Tocopherols, tocotrienols, and plastochromanols (collectively termed tocochromanols) are lipid-soluble antioxidants synthesized by all plants. Their dietary intake, primarily from seed oils, provides vitamin E and other health benefits. Tocochromanol biosynthesis has been dissected in the dicot Arabidopsis thaliana, which has green, photosynthetic seeds, but our understanding of tocochromanol accumulation in major crops, whose seeds are nonphotosynthetic, remains limited. To understand the genetic control of tocochromanols in grain, we conducted a joint linkage and genome-wide association study in the 5000-line U.S. maize (Zea mays) nested association mapping panel. Fifty-two quantitative trait loci for individual and total tocochromanols were identified, and of the 14 resolved to individual genes, six encode novel activities affecting tocochromanols in plants. These include two chlorophyll biosynthetic enzymes that explain the majority of tocopherol variation, which was not predicted given that, like most major cereal crops, maize grain is nonphotosynthetic. This comprehensive assessment of natural variation in vitamin E levels in maize establishes the foundation for improving tocochromanol and vitamin E content in seeds of maize and other major cereal crops.
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Affiliation(s)
- Christine H Diepenbrock
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Catherine B Kandianis
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Alexander E Lipka
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Elsa Góngora-Castillo
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Jason G Wallace
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Jason Cepela
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Alex Mesberg
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Peter J Bradbury
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- U.S. Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - Daniel C Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | | | - John Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Brenda F Owens
- Department of Agronomy, Purdue University, West Lafayette, IN 47907
| | - Tyler Tiede
- Department of Agronomy, Purdue University, West Lafayette, IN 47907
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- U.S. Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | | | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Dean DellaPenna
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
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Identification of trans Protein QTL for Secreted Airway Mucins in Mice and a Causal Role for Bpifb1. Genetics 2017; 207:801-812. [PMID: 28851744 DOI: 10.1534/genetics.117.300211] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 08/22/2017] [Indexed: 12/14/2022] Open
Abstract
Mucus hyper-secretion is a hallmark feature of asthma and other muco-obstructive airway diseases. The mucin proteins MUC5AC and MUC5B are the major glycoprotein components of mucus and have critical roles in airway defense. Despite the biomedical importance of these two proteins, the loci that regulate them in the context of natural genetic variation have not been studied. To identify genes that underlie variation in airway mucin levels, we performed genetic analyses in founder strains and incipient lines of the Collaborative Cross (CC) in a house dust mite mouse model of asthma. CC founder strains exhibited significant differences in MUC5AC and MUC5B, providing evidence of heritability. Analysis of gene and protein expression of Muc5ac and Muc5b in incipient CC lines (n = 154) suggested that post-transcriptional events were important regulators of mucin protein content in the airways. Quantitative trait locus (QTL) mapping identified distinct, trans protein QTL for MUC5AC (chromosome 13) and MUC5B (chromosome 2). These two QTL explained 18 and 20% of phenotypic variance, respectively. Examination of the MUC5B QTL allele effects and subsequent phylogenetic analysis allowed us to narrow the MUC5B QTL and identify Bpifb1 as a candidate gene. Bpifb1 mRNA and protein expression were upregulated in parallel to MUC5B after allergen challenge, and Bpifb1 knockout mice exhibited higher MUC5B expression. Thus, BPIFB1 is a novel regulator of MUC5B.
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Genome-wide dissection of heterosis for yield traits in two-line hybrid rice populations. Sci Rep 2017; 7:7635. [PMID: 28794433 PMCID: PMC5550440 DOI: 10.1038/s41598-017-06742-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/16/2017] [Indexed: 11/24/2022] Open
Abstract
Heterosis has been widely utilized in agriculture and is important for world food safety. Many genetic models have been proposed as mechanisms underlying heterosis during the past century, yet more evidence is needed to support such models. To investigate heterosis in two-line hybrid rice, we generated a partial diallel crossing scheme, which consisted of approximately 500 F1 hybrids derived from 14 male sterile lines and 39 restorer lines. In this population, increased panicle number played the most important role in yield heterosis of hybrid rice. Genome-wide association studies identified many QTLs related to the yield traits of F1 hybrids, better paternal heterosis and special combining ability. Relevant genes, including Hd3a, qGL3, OsmiR156h, and LAX2, were identified as candidates within these QTLs. Nearly forty percent of the QTLs had only two genotypes in the F1 hybrids, mainly because the maternal lines were under intense selective pressure. Further analysis found male sterile lines and restorer lines made different superior allele contributions to F1 hybrids, and their contributions varied among different traits. These results extend our understanding of the molecular basis of heterosis in two-line hybrid rice.
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Zan Y, Sheng Z, Lillie M, Rönnegård L, Honaker CF, Siegel PB, Carlborg Ö. Artificial Selection Response due to Polygenic Adaptation from a Multilocus, Multiallelic Genetic Architecture. Mol Biol Evol 2017; 34:2678-2689. [DOI: 10.1093/molbev/msx194] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Strable J, Wallace JG, Unger-Wallace E, Briggs S, Bradbury PJ, Buckler ES, Vollbrecht E. Maize YABBY Genes drooping leaf1 and drooping leaf2 Regulate Plant Architecture. THE PLANT CELL 2017; 29:1622-1641. [PMID: 28698237 PMCID: PMC5559738 DOI: 10.1105/tpc.16.00477] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 06/12/2017] [Accepted: 07/07/2017] [Indexed: 05/19/2023]
Abstract
Leaf architecture directly influences canopy structure, consequentially affecting yield. We discovered a maize (Zea mays) mutant with aberrant leaf architecture, which we named drooping leaf1 (drl1). Pleiotropic mutations in drl1 affect leaf length and width, leaf angle, and internode length and diameter. These phenotypes are enhanced by natural variation at the drl2 enhancer locus, including reduced expression of the drl2-Mo17 allele in the Mo17 inbred. A second drl2 allele, produced by transposon mutagenesis, interacted synergistically with drl1 mutants and reduced drl2 transcript levels. The drl genes are required for proper leaf patterning, development and cell proliferation of leaf support tissues, and for restricting auricle expansion at the midrib. The paralogous loci encode maize CRABS CLAW co-orthologs in the YABBY family of transcriptional regulators. The drl genes are coexpressed in incipient and emergent leaf primordia at the shoot apex, but not in the vegetative meristem or stem. Genome-wide association studies using maize NAM-RIL (nested association mapping-recombinant inbred line) populations indicated that the drl loci reside within quantitative trait locus regions for leaf angle, leaf width, and internode length and identified rare single nucleotide polymorphisms with large phenotypic effects for the latter two traits. This study demonstrates that drl genes control the development of key agronomic traits in maize.
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Affiliation(s)
- Josh Strable
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011
- Interdepartmental Plant Biology, Iowa State University, Ames, Iowa 50011
| | - Jason G Wallace
- Department of Crop and Soil Sciences, The University of Georgia, Athens, Georgia 30602
| | - Erica Unger-Wallace
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011
| | - Sarah Briggs
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011
| | - Peter J Bradbury
- U.S. Department of Agriculture-Agriculture Research Service, Ithaca, New York 14853
| | - Edward S Buckler
- U.S. Department of Agriculture-Agriculture Research Service, Ithaca, New York 14853
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853
| | - Erik Vollbrecht
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011
- Interdepartmental Plant Biology, Iowa State University, Ames, Iowa 50011
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Cheng R, Doerge RW, Borevitz J. Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping. G3 (BETHESDA, MD.) 2017; 7:813-822. [PMID: 28064191 PMCID: PMC5345711 DOI: 10.1534/g3.116.037531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 12/29/2016] [Indexed: 01/13/2023]
Abstract
Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.
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Affiliation(s)
- Riyan Cheng
- Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia
| | - R W Doerge
- Department of Statistics, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Justin Borevitz
- Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia
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Enhancing genomic prediction with genome-wide association studies in multiparental maize populations. Heredity (Edinb) 2017; 118:585-593. [PMID: 28198815 DOI: 10.1038/hdy.2017.4] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 01/03/2017] [Accepted: 01/12/2017] [Indexed: 01/30/2023] Open
Abstract
Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.
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Imputation-Based Fine-Mapping Suggests That Most QTL in an Outbred Chicken Advanced Intercross Body Weight Line Are Due to Multiple, Linked Loci. G3-GENES GENOMES GENETICS 2017; 7:119-128. [PMID: 27799342 PMCID: PMC5217102 DOI: 10.1534/g3.116.036012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The Virginia chicken lines have been divergently selected for juvenile body weight for more than 50 generations. Today, the high- and low-weight lines show a >12-fold difference for the selected trait, 56-d body weight. These lines provide unique opportunities to study the genetic architecture of long-term, single-trait selection. Previously, several quantitative trait loci (QTL) contributing to weight differences between the lines were mapped in an F2-cross between them, and these were later replicated and fine-mapped in a nine-generation advanced intercross of them. Here, we explore the possibility to further increase the fine-mapping resolution of these QTL via a pedigree-based imputation strategy that aims to better capture the genetic diversity in the divergently selected, but outbred, founder lines. The founders of the intercross were high-density genotyped, and then pedigree-based imputation was used to assign genotypes throughout the pedigree. Imputation increased the marker density 20-fold in the selected QTL, providing 6911 markers for the subsequent analysis. Both single-marker association and multi-marker backward-elimination analyses were used to explore regions associated with 56-d body weight. The approach revealed several statistically and population structure independent associations and increased the mapping resolution. Further, most QTL were also found to contain multiple independent associations to markers that were not fixed in the founder populations, implying a complex underlying architecture due to the combined effects of multiple, linked loci perhaps located on independent haplotypes that still segregate in the selected lines.
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Maurer A, Sannemann W, Léon J, Pillen K. Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations. Heredity (Edinb) 2016; 118:477-485. [PMID: 27966535 PMCID: PMC5520528 DOI: 10.1038/hdy.2016.121] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 11/14/2016] [Accepted: 11/15/2016] [Indexed: 01/08/2023] Open
Abstract
The emergence of multiparental mapping populations enabled plant geneticists to gain deeper insights into the genetic architecture of major agronomic traits and to map quantitative trait loci (QTLs) controlling the expression of these traits. Although the investigated mapping populations are similar, one open question is whether genotype data should be modelled as identical by state (IBS) or identical by descent (IBD). Whereas IBS simply makes use of raw genotype scores to distinguish alleles, IBD data are derived from parental offspring information. We report on comparing IBS and IBD by applying two multiple regression models on four traits studied in the barley nested association mapping (NAM) population HEB-25. We observed that modelling parent-specific IBD genotypes produced a lower number of significant QTLs with increased prediction abilities compared with modelling IBS genotypes. However, at lower trait heritabilities the IBS model produced higher prediction abilities. We developed a method to estimate multiallelic QTL effects in multiparental populations from simple biallelic IBS data. This method is based on cumulating IBS-derived single-nucleotide polymorphism (SNP) effect estimates in a defined genetic region surrounding a QTL. Comparing the resulting parent-specific QTL effects with those obtained from IBD approaches revealed high accordance that could be confirmed through simulations. The method turned out to be also applicable to a barley multiparent advanced generation inter-cross (MAGIC) population. The 'cumulation method' represents a universal approach to differentiate parent-specific QTL effects in multiparental populations, even if no IBD information is available. In future, the method could further benefit from the availability of much denser SNP maps.
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Affiliation(s)
- A Maurer
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - W Sannemann
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - J Léon
- Institute for Crop Science and Resource Conservation, University Bonn, Bonn, Germany
| | - K Pillen
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany
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A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat Commun 2016; 7:13299. [PMID: 27830750 PMCID: PMC5109549 DOI: 10.1038/ncomms13299] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 09/21/2016] [Indexed: 12/22/2022] Open
Abstract
All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.
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Li C, Sun B, Li Y, Liu C, Wu X, Zhang D, Shi Y, Song Y, Buckler ES, Zhang Z, Wang T, Li Y. Numerous genetic loci identified for drought tolerance in the maize nested association mapping populations. BMC Genomics 2016; 17:894. [PMID: 27825295 PMCID: PMC5101730 DOI: 10.1186/s12864-016-3170-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Accepted: 10/18/2016] [Indexed: 12/18/2022] Open
Abstract
Background Maize requires more water than most other crops; therefore, the water use efficiency of this crop must be improved for maize production under undesirable land and changing environmental conditions. Results To elucidate the genetic control of drought in maize, we evaluated approximately 5000 inbred lines from 30 linkage-association joint mapping populations under two contrasting water regimes for seven drought-related traits, including yield and anthesis-silking interval (ASI). The joint linkage analysis was conducted to identify 220 quantitative trait loci (QTLs) under well-watered conditions and 169 QTLs under water-stressed conditions. The genome-wide association analysis identified 365 single nucleotide polymorphisms (SNPs) associated with drought-related traits, and these SNPs were located in 354 candidate genes. Fifty-two of these genes showed significant differential expression in the inbred line B73 under the well-watered and water-stressed conditions. In addition, genomic predictions suggested that the moderate-density SNPs obtained through genotyping-by-sequencing were able to make accurate predictions in the nested association mapping population for drought-related traits with moderate-to-high heritability under the water-stressed conditions. Conclusions The results of the present study provide important information that can be used to understand the genetic basis of drought stress responses and facilitate the use of beneficial alleles for the improvement of drought tolerance in maize. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3170-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chunhui Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Baocheng Sun
- Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, 830000, China
| | - Yongxiang Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Cheng Liu
- Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, 830000, China
| | - Xun Wu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Dengfeng Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yunsu Shi
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yanchun Song
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA. .,USA Department of Agriculture-Agricultural Research Service, Ithaca, NY, USA.
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA.
| | - Tianyu Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Yu Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Olukolu BA, Bian Y, De Vries B, Tracy WF, Wisser RJ, Holland JB, Balint-Kurti PJ. The Genetics of Leaf Flecking in Maize and Its Relationship to Plant Defense and Disease Resistance. PLANT PHYSIOLOGY 2016; 172:1787-1803. [PMID: 27670817 PMCID: PMC5100796 DOI: 10.1104/pp.15.01870] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 09/22/2016] [Indexed: 05/20/2023]
Abstract
Physiological leaf spotting, or flecking, is a mild-lesion phenotype observed on the leaves of several commonly used maize (Zea mays) inbred lines and has been anecdotally linked to enhanced broad-spectrum disease resistance. Flecking was assessed in the maize nested association mapping (NAM) population, comprising 4,998 recombinant inbred lines from 25 biparental families, and in an association population, comprising 279 diverse maize inbreds. Joint family linkage analysis was conducted with 7,386 markers in the NAM population. Genome-wide association tests were performed with 26.5 million single-nucleotide polymorphisms (SNPs) in the NAM population and with 246,497 SNPs in the association population, resulting in the identification of 18 and three loci associated with variation in flecking, respectively. Many of the candidate genes colocalizing with associated SNPs are similar to genes that function in plant defense response via cell wall modification, salicylic acid- and jasmonic acid-dependent pathways, redox homeostasis, stress response, and vesicle trafficking/remodeling. Significant positive correlations were found between increased flecking, stronger defense response, increased disease resistance, and increased pest resistance. A nonlinear relationship with total kernel weight also was observed whereby lines with relatively high levels of flecking had, on average, lower total kernel weight. We present evidence suggesting that mild flecking could be used as a selection criterion for breeding programs trying to incorporate broad-spectrum disease resistance.
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Affiliation(s)
- Bode A Olukolu
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - Yang Bian
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - Brian De Vries
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - William F Tracy
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - Randall J Wisser
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - James B Holland
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.)
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.)
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.)
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
| | - Peter J Balint-Kurti
- Department of Plant Pathology, North Carolina State University, Raleigh, North Carolina 27695-7616 (B.A.O., P.J.B.-K.);
- Department of Crop Science, North Carolina State University, Raleigh, North Carolina 27695-7620 (B.A.O., Y.B., J.B.H.);
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706 (B.D.V., W.F.T.);
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716 (R.J.W.); and
- United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, North Carolina 27695 (J.B.H., P.J.B.-K.)
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Wang H, Li K, Hu X, Liu Z, Wu Y, Huang C. Genome-wide association analysis of forage quality in maize mature stalk. BMC PLANT BIOLOGY 2016; 16:227. [PMID: 27769176 PMCID: PMC5073832 DOI: 10.1186/s12870-016-0919-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 10/12/2016] [Indexed: 05/21/2023]
Abstract
BACKGROUND Plant digestibility of silage maize (Zea mays L.) has a large influence on nutrition intake for animal feeding. Improving forage quality will enhance the utilization efficiency and feeding value of forage maize. Dissecting the genetic basis of forage quality will improve our understanding of the complex nature of cell wall biosynthesis and degradation, which is also helpful for breeding good quality silage maize. RESULTS Acid detergent fiber (ADF), neutral detergent fiber (NDF) and in vitro dry matter digestibility (IVDMD) of stalk were evaluated in a diverse maize population, which is comprised of 368 inbred lines and planted across seven environments. Using a mixed model accounting for population structure and polygenic background effects, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) significantly associated with forage quality. Scanning 559,285 SNPs across the whole genome, 73, 41 and 82 SNPs were found to be associated with ADF, NDF, and IVDMD, respectively. Each significant SNP explained 4.2 %-6.2 % of the phenotypic variation. Underlying these associated loci, 56 genes were proposed as candidate genes for forage quality. CONCLUSIONS Of all the candidate genes proposed by GWAS, we only found a C3H gene (ZmC3H2) that is directly involved in cell wall component biosynthesis. The candidate genes found in this study are mainly involved in signal transduction, stress resistance, and transcriptional regulation of cell wall biosynthetic gene expression. Adding high digestibility maize into the association panel would be helpful for increasing genetic variability and identifying more genes associated with forage quality traits. Cloning and functional validation of these genes would be helpful for understanding the molecular mechanism of the fiber content and digestibility. These findings provide us new insights into cell wall formation and deposition.
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Affiliation(s)
- Hongwu Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Kun Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xiaojiao Hu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhifang Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yujin Wu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Changling Huang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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40
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Richter A, Schaff C, Zhang Z, Lipka AE, Tian F, Köllner TG, Schnee C, Preiß S, Irmisch S, Jander G, Boland W, Gershenzon J, Buckler ES, Degenhardt J. Characterization of Biosynthetic Pathways for the Production of the Volatile Homoterpenes DMNT and TMTT in Zea mays. THE PLANT CELL 2016; 28:2651-2665. [PMID: 27662898 PMCID: PMC5134970 DOI: 10.1105/tpc.15.00919] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 08/05/2016] [Accepted: 09/23/2016] [Indexed: 05/20/2023]
Abstract
Plant volatiles not only have multiple defense functions against herbivores, fungi, and bacteria, but also have been implicated in signaling within the plant and toward other organisms. Elucidating the function of individual plant volatiles will require more knowledge of their biosynthesis and regulation in response to external stimuli. By exploiting the variation of herbivore-induced volatiles among 26 maize (Zea mays) inbred lines, we conducted a nested association mapping and genome-wide association study (GWAS) to identify a set of quantitative trait loci (QTLs) for investigating the pathways of volatile terpene production. The most significant identified QTL affects the emission of (E)-nerolidol, linalool, and the two homoterpenes (E)-3,8-dimethyl-1,4,7-nonatriene (DMNT) and (E,E)-4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT). GWAS associated a single nucleotide polymorphism in the promoter of the gene encoding the terpene synthase TPS2 with this QTL Biochemical characterization of TPS2 verified that this plastid-localized enzyme forms linalool, (E)-nerolidol, and (E,E)-geranyllinalool. The subsequent conversion of (E)-nerolidol into DMNT maps to a P450 monooxygenase, CYP92C5, which is capable of converting nerolidol into DMNT by oxidative degradation. A QTL influencing TMTT accumulation corresponds to a similar monooxygenase, CYP92C6, which is specific for the conversion of (E,E)-geranyllinalool to TMTT The DMNT biosynthetic pathway and both monooxygenases are distinct from those previously characterized for DMNT and TMTT synthesis in Arabidopsis thaliana, suggesting independent evolution of these enzymatic activities.
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Affiliation(s)
- Annett Richter
- Institute for Pharmacy, Martin Luther University Halle-Wittenberg, D-06120 Halle, Germany
| | - Claudia Schaff
- Institute for Pharmacy, Martin Luther University Halle-Wittenberg, D-06120 Halle, Germany
| | - Zhiwu Zhang
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Alexander E Lipka
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Feng Tian
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Tobias G Köllner
- Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany
| | | | - Susanne Preiß
- Institute for Pharmacy, Martin Luther University Halle-Wittenberg, D-06120 Halle, Germany
| | - Sandra Irmisch
- Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany
| | - Georg Jander
- Boyce Thompson Institute for Plant Research, Ithaca, New York 14853
| | - Willhelm Boland
- Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany
| | | | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- U.S. Department of Agriculture-Agricultural Research Service, Ithaca, New York 14853
| | - Jörg Degenhardt
- Institute for Pharmacy, Martin Luther University Halle-Wittenberg, D-06120 Halle, Germany
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Wallace JG, Zhang X, Beyene Y, Semagn K, Olsen M, Prasanna BM, Buckler ES. Genome‐wide Association for Plant Height and Flowering Time across 15 Tropical Maize Populations under Managed Drought Stress and Well‐Watered Conditions in Sub‐Saharan Africa. CROP SCIENCE 2016; 56:2365-2378. [PMID: 0 DOI: 10.2135/cropsci2015.10.0632] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Affiliation(s)
- Jason G. Wallace
- Dep. of Crop and Soil Sciences The Univ. of Georgia Athens GA 30602‐6810
- Inst. for Genomic Diversity Cornell Univ. Ithaca NY 14853
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT) Apdo. Postal 6‐641 06600 Mexico, DF Mexico
| | - Yoseph Beyene
- CIMMYT P.O. Box 1041, Village Market 00621 Nairobi Kenya
| | - Kassa Semagn
- Dep. of Agricultural, Food and Nutritional Science Univ. of Alberta Edmonton Canada
| | - Michael Olsen
- CIMMYT P.O. Box 1041, Village Market 00621 Nairobi Kenya
| | | | - Edward S. Buckler
- Inst. for Genomic Diversity Cornell Univ. Ithaca NY 14853
- USDA – Agricultural Research Service Ithaca NY 14853
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Olukolu BA, Tracy WF, Wisser R, De Vries B, Balint-Kurti PJ. A Genome-Wide Association Study for Partial Resistance to Maize Common Rust. PHYTOPATHOLOGY 2016; 106:745-51. [PMID: 27003507 DOI: 10.1094/phyto-11-15-0305-r] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Quantitative resistance to maize common rust (causal agent Puccinia sorghi) was assessed in an association mapping population of 274 diverse inbred lines. Resistance to common rust was found to be moderately correlated with resistance to three other diseases and with the severity of the hypersensitive defense response previously assessed in the same population. Using a mixed linear model accounting for the confounding effects of population structure and flowering time, genome-wide association tests were performed based at 246,497 single-nucleotide polymorphism loci. Three loci associated with maize common rust resistance were identified. Candidate genes at each locus had predicted roles, mainly in cell wall modification. Other candidate genes included a resistance gene and a gene with a predicted role in regulating accumulation of reactive oxygen species.
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Affiliation(s)
- Bode A Olukolu
- First author: Department of Plant Pathology and Department of Horticulture, North Carolina State University, Raleigh 27695; second and fourth authors: Department of Agronomy, University of Wisconsin-Madison, Madison 53706; third author: Department of Plant & Soil Sciences, University of Delaware, Newark 19716; and fifth author: Department of Plant Pathology, North Carolina State University, and United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, NC 27695
| | - William F Tracy
- First author: Department of Plant Pathology and Department of Horticulture, North Carolina State University, Raleigh 27695; second and fourth authors: Department of Agronomy, University of Wisconsin-Madison, Madison 53706; third author: Department of Plant & Soil Sciences, University of Delaware, Newark 19716; and fifth author: Department of Plant Pathology, North Carolina State University, and United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, NC 27695
| | - Randall Wisser
- First author: Department of Plant Pathology and Department of Horticulture, North Carolina State University, Raleigh 27695; second and fourth authors: Department of Agronomy, University of Wisconsin-Madison, Madison 53706; third author: Department of Plant & Soil Sciences, University of Delaware, Newark 19716; and fifth author: Department of Plant Pathology, North Carolina State University, and United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, NC 27695
| | - Brian De Vries
- First author: Department of Plant Pathology and Department of Horticulture, North Carolina State University, Raleigh 27695; second and fourth authors: Department of Agronomy, University of Wisconsin-Madison, Madison 53706; third author: Department of Plant & Soil Sciences, University of Delaware, Newark 19716; and fifth author: Department of Plant Pathology, North Carolina State University, and United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, NC 27695
| | - Peter J Balint-Kurti
- First author: Department of Plant Pathology and Department of Horticulture, North Carolina State University, Raleigh 27695; second and fourth authors: Department of Agronomy, University of Wisconsin-Madison, Madison 53706; third author: Department of Plant & Soil Sciences, University of Delaware, Newark 19716; and fifth author: Department of Plant Pathology, North Carolina State University, and United States Department of Agriculture-Agricultural Research Service Plant Science Research Unit, Raleigh, NC 27695
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Xiao Y, Tong H, Yang X, Xu S, Pan Q, Qiao F, Raihan MS, Luo Y, Liu H, Zhang X, Yang N, Wang X, Deng M, Jin M, Zhao L, Luo X, Zhou Y, Li X, Liu J, Zhan W, Liu N, Wang H, Chen G, Cai Y, Xu G, Wang W, Zheng D, Yan J. Genome-wide dissection of the maize ear genetic architecture using multiple populations. THE NEW PHYTOLOGIST 2016; 210:1095-106. [PMID: 26715032 DOI: 10.1111/nph.13814] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 11/23/2015] [Indexed: 05/02/2023]
Abstract
Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.
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Affiliation(s)
- Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hao Tong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaohong Yang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Qingchun Pan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Qiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mohammad Sharif Raihan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuehai Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaqing Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Min Deng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Minliang Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lijun Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xin Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yang Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jie Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Zhan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Nannan Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hong Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Gengshen Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ye Cai
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Gen Xu
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Weidong Wang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Debo Zheng
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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Rönnegård L, McFarlane SE, Husby A, Kawakami T, Ellegren H, Qvarnström A. Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation. Methods Ecol Evol 2016; 7:792-799. [PMID: 27478587 PMCID: PMC4950150 DOI: 10.1111/2041-210x.12535] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Accepted: 12/12/2015] [Indexed: 12/03/2022]
Abstract
Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long‐term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single‐nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long‐term study of collared flycatchers in Sweden. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response. The method is available in the r package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long‐term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.
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Affiliation(s)
- Lars Rönnegård
- Department of Clinical Sciences Swedish University of Agricultural Sciences SE-75007 Uppsala Sweden
| | - S Eryn McFarlane
- Department of Animal Ecology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden
| | - Arild Husby
- Department of Biosciences Metapopulation Research Centre University of Helsinki PO Box 65FI-00014 Helsinki Finland; Department of Biology Centre for Biodiversity Dynamics Norwegian University of Science and Technology N-7491 Trondheim Norway
| | - Takeshi Kawakami
- Department of Evolutionary Biology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden
| | - Hans Ellegren
- Department of Evolutionary Biology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden
| | - Anna Qvarnström
- Department of Animal Ecology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden
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45
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Jagodic M, Stridh P. Positional Gene Cloning in Experimental Populations. Methods Mol Biol 2016; 1304:3-24. [PMID: 25103675 DOI: 10.1007/7651_2014_108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Positional cloning is a technique that identifies a trait-associated gene based on its location in the genome and involves methods such as linkage analysis, association mapping, and bioinformatics. This approach can be used for gene identification even when little is known about the molecular basis of the trait. Vast majority of traits are regulated by multiple genomic loci called quantitative trait loci (QTL). We describe experimental populations and designs that can be used for positional cloning, including backcrosses, intercrosses, and heterogeneous stocks, and advantages and disadvantages of different approaches. Once the phenotype and genotype of each individual in an experimental population have been determined, QTL identification can be accomplished. We describe the statistical tools used to identify the existence, location, and significance of QTLs. These different methods have advantages and disadvantages to consider when selecting the appropriate model to be used, which is briefly discussed.Although the objective of QTL mapping is to identify genomic regions associated with a trait, the ultimate goal is to identify the gene and the genetic variation (which is often quantitative trait nucleotide, QTN) or haplotype that is responsible for the phenotype. By discovering the function of causative variants or haplotypes we can understand the molecular changes that lead to the phenotype. We briefly describe how the genomic sequences can be exploited to identify QTNs and how these can be validated in congenic strains and functionally tested to understand their influence on phenotype expression.
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Affiliation(s)
- Maja Jagodic
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden,
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46
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Gralinski LE, Ferris MT, Aylor DL, Whitmore AC, Green R, Frieman MB, Deming D, Menachery VD, Miller DR, Buus RJ, Bell TA, Churchill GA, Threadgill DW, Katze MG, McMillan L, Valdar W, Heise MT, Pardo-Manuel de Villena F, Baric RS. Genome Wide Identification of SARS-CoV Susceptibility Loci Using the Collaborative Cross. PLoS Genet 2015; 11:e1005504. [PMID: 26452100 PMCID: PMC4599853 DOI: 10.1371/journal.pgen.1005504] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 08/15/2015] [Indexed: 01/21/2023] Open
Abstract
New systems genetics approaches are needed to rapidly identify host genes and genetic networks that regulate complex disease outcomes. Using genetically diverse animals from incipient lines of the Collaborative Cross mouse panel, we demonstrate a greatly expanded range of phenotypes relative to classical mouse models of SARS-CoV infection including lung pathology, weight loss and viral titer. Genetic mapping revealed several loci contributing to differential disease responses, including an 8.5Mb locus associated with vascular cuffing on chromosome 3 that contained 23 genes and 13 noncoding RNAs. Integrating phenotypic and genetic data narrowed this region to a single gene, Trim55, an E3 ubiquitin ligase with a role in muscle fiber maintenance. Lung pathology and transcriptomic data from mice genetically deficient in Trim55 were used to validate its role in SARS-CoV-induced vascular cuffing and inflammation. These data establish the Collaborative Cross platform as a powerful genetic resource for uncovering genetic contributions of complex traits in microbial disease severity, inflammation and virus replication in models of outbred populations.
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Affiliation(s)
- Lisa E. Gralinski
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - David L. Aylor
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Alan C. Whitmore
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Richard Green
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Matthew B. Frieman
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Damon Deming
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Vineet D. Menachery
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Darla R. Miller
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ryan J. Buus
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Timothy A. Bell
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | | | - David W. Threadgill
- Department of Veterinary Pathobiology, Texas A&M University, College Station, Texas, United States of America
| | - Michael G. Katze
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Leonard McMillan
- Department of Computer Science, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Mark T. Heise
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ralph S. Baric
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
- * E-mail:
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47
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Sheng Z, Pettersson ME, Honaker CF, Siegel PB, Carlborg Ö. Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment. Genome Biol 2015; 16:219. [PMID: 26438066 PMCID: PMC4595211 DOI: 10.1186/s13059-015-0785-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 09/21/2015] [Indexed: 11/23/2022] Open
Abstract
Background Artificial selection provides a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions where allele-frequencies have diverged during selection. To avoid false positive signatures of selection, it is necessary to show that a sweep affects a selected trait before it can be considered adaptive. Here, we confirm candidate, genome-wide distributed selective sweeps originating from the standing genetic variation in a long-term selection experiment on high and low body weight of chickens. Results Using an intercross between the two divergent chicken lines, 16 adaptive selective sweeps were confirmed based on their association with the body weight at 56 days of age. Although individual additive effects were small, the fixation for alternative alleles across the loci contributed at least 40 % of the phenotypic difference for the selected trait between these lines. The sweeps contributed about half of the additive genetic variance present within and between the lines after 40 generations of selection, corresponding to a considerable portion of the additive genetic variance of the base population. Conclusions Long-term, single-trait, bi-directional selection in the Virginia chicken lines has resulted in a gradual response to selection for extreme phenotypes without a drastic reduction in the genetic variation. We find that fixation of several standing genetic variants across a highly polygenic genetic architecture made a considerable contribution to long-term selection response. This provides new fundamental insights into the dynamics of standing genetic variation during long-term selection and adaptation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0785-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zheya Sheng
- Division of Computational Genetics, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
| | - Mats E Pettersson
- Division of Computational Genetics, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden. .,Present address: Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
| | - Christa F Honaker
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Paul B Siegel
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Örjan Carlborg
- Division of Computational Genetics, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
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48
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Rutledge H, Baran-Gale J, de Villena FPM, Chesler EJ, Churchill GA, Sethupathy P, Kelada SNP. Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population. BMC Genomics 2015; 16:633. [PMID: 26303911 PMCID: PMC4548451 DOI: 10.1186/s12864-015-1732-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 06/29/2015] [Indexed: 12/17/2022] Open
Abstract
Background Allergic airway diseases (AADs) such as asthma are characterized in part by granulocytic airway inflammation. The gene regulatory networks that govern granulocyte recruitment are poorly understood, but evidence is accruing that microRNAs (miRNAs) play an important role. To identify miRNAs that may underlie AADs, we used two complementary approaches that leveraged the genotypic and phenotypic diversity of the Collaborative Cross (CC) mouse population. In the first approach, we sought to identify miRNA expression quantitative trait loci (eQTL) that overlap QTL for AAD-related phenotypes. Specifically, CC founder strains and incipient lines of the CC were sensitized and challenged with house dust mite allergen followed by measurement of granulocyte recruitment to the lung. Total lung RNA was isolated and miRNA was measured using arrays for CC founders and qRT-PCR for incipient CC lines. Results Among CC founders, 92 miRNAs were differentially expressed. We measured the expression of 40 of the most highly expressed of these 92 miRNAs in the incipient lines of the CC and identified 18 eQTL corresponding to 14 different miRNAs. Surprisingly, half of these eQTL were distal to the corresponding miRNAs, and even on different chromosomes. One of the largest-effect local miRNA eQTL was for miR-342-3p, for which we identified putative causal variants by bioinformatic analysis of the effects of single nucleotide polymorphisms on RNA structure. None of the miRNA eQTL co-localized with QTL for eosinophil or neutrophil recruitment. In the second approach, we constructed putative miRNA/mRNA regulatory networks and identified three miRNAs (miR-497, miR-351 and miR-31) as candidate master regulators of genes associated with neutrophil recruitment. Analysis of a dataset from human keratinocytes transfected with a miR-31 inhibitor revealed two target genes in common with miR-31 targets correlated with neutrophils, namely Oxsr1 and Nsf. Conclusions miRNA expression in the allergically inflamed murine lung is regulated by genetic loci that are smaller in effect size compared to mRNA eQTL and often act in trans. Thus our results indicate that the genetic architecture of miRNA expression is different from mRNA expression. We identified three miRNAs, miR-497, miR-351 and miR-31, that are candidate master regulators of genes associated with neutrophil recruitment. Because miR-31 is expressed in airway epithelia and is predicted to target genes with known links to neutrophilic inflammation, we suggest that miR-31 is a potentially novel regulator of airway inflammation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1732-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Holly Rutledge
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA.
| | - Jeanette Baran-Gale
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA.
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | | | | | - Praveen Sethupathy
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Samir N P Kelada
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Marsico Lung Institute, University of North Carolina, Chapel Hill, NC, USA.
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49
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Abstract
Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects.
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50
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Abstract
The genetic basis of type 2 diabetes remains incompletely defined despite the use of multiple genetic strategies. Multiparental populations such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few megabases, significantly decreasing the number of potential candidate genes compared to traditional mapping strategies. In the present work, we employed expression and sequence analysis in HS rats (Rattus norvegicus) to identify Tpcn2 as a likely causal gene underlying a 3.1-Mb locus for glucose and insulin levels. Global gene expression analysis on liver identified Tpcn2 as the only gene in the region that is differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation. Tpcn2 also maps as a cis-regulating expression QTL and is negatively correlated with fasting glucose levels. We used founder sequence to identify variants within this region and assessed association between 18 variants and diabetic traits by conducting a mixed-model analysis, accounting for the complex family structure of the HS. We found that two variants were significantly associated with fasting glucose levels, including a nonsynonymous coding variant within Tpcn2. Studies in Tpcn2 knockout mice demonstrated a significant decrease in fasting glucose levels and insulin response to a glucose challenge relative to those in wild-type mice. Finally, we identified variants within Tpcn2 that are associated with fasting insulin in humans. These studies indicate that Tpcn2 is a likely causal gene that may play a role in human diabetes and demonstrate the utility of multiparental populations for positionally cloning genes within complex loci.
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