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Kaseja K, Lambe N, Yates J, Smith E, Conington J. Genome wide association studies for carcass traits measured by video image analysis in crossbred lambs. Meat Sci 2024; 214:109518. [PMID: 38677055 DOI: 10.1016/j.meatsci.2024.109518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/12/2024] [Accepted: 04/14/2024] [Indexed: 04/29/2024]
Abstract
This is the first UK genome wide association study investigating potential links between Video Image Analysis (VIA) carcass traits and molecular polymorphisms in crossbred sheep. Phenotypic and genotypic data were collected from two crossbred lamb populations: Texel x Scotch Mule (TxSM, n = 2330) and Texel x Lleyn (TxL, n = 3816). Traits measured included live weights at birth, eight weeks and weaning (∼15 weeks). VIA-predicted traits included total weights and weights of fat, muscle and bone in the whole carcass and primal (hind leg, saddle, shoulder) regions. Within-breed heritabilities estimated for the VIA traits ranged from 0.01 to 0.70, indicating potential for inclusion of some traits in breeding programmes. The two crossbred populations differed in SNPs associated with different traits. Two SNPs on chromosomes two (s74618.1) and eight (s68536.1), respectively, reached genome-wise significance for TxSM, explaining <1% of trait variance, for whole carcass fat and muscle weights, hind leg and saddle fat weights and shoulder bone weights. For TxL, four SNPs reached genome-wise significance, on chromosome two for hind leg muscle weight (OAR2_117,959,202 and OAR2_11804335), on chromosome 10 for whole carcass bone weight (OAR19_8,995,957.1), and on chromosome 19 for weaning weight (s40847.1), each explaining <1% of trait genetic variation. Differences in apparent genetic control of carcass traits may be influenced by the lambs' cross-breed, but also by management decisions affecting environmental variance and trait definitions, which should be understood in order to define protocols for incorporation of carcass traits into (cross)breeding programmes. IMPLICATIONS: Combining VIA-measured carcass traits with conventional production traits in a breeding programme could potentially improve the production and product quality of meat sheep. Phenotypes for VIA traits could be collected relatively easily if VIA machines were present at all abattoir sites. The current study and future Genome Wide Association Studies may help to identify potentially informative molecular markers, that explain large proportions of the genetic variance observed in VIA-measured carcass traits. Including this information in the estimation of breeding values could increase the accuracy of prediction, increasing the potential rate of genetic improvement for product quality. This study confirms the polygenic architecture of the investigated carcass traits, with a small number of molecular markers that each explain a small amount of genetic variation. Further studies across breed types are recommended to further test and validate molecular markers for traits related to lamb carcass quality, as measured by video image analysis.
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Affiliation(s)
- K Kaseja
- Scotland's Rural College (SRUC), Peter Wilson Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JG, United Kingdom.
| | - N Lambe
- Scotland's Rural College (SRUC), Peter Wilson Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JG, United Kingdom
| | - J Yates
- The British Texel Sheep Society, Stoneleigh Park, Warwickshire CV8 2LG, United Kingdom
| | - E Smith
- The British Texel Sheep Society, Stoneleigh Park, Warwickshire CV8 2LG, United Kingdom
| | - J Conington
- Scotland's Rural College (SRUC), Peter Wilson Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JG, United Kingdom
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Green HE, Oliveira HRD, Alvarenga AB, Scramlin-Zuelly S, Grossi D, Schinckel AP, Brito LF. Genomic background of biotypes related to growth, carcass and meat quality traits in Duroc pigs based on principal component analysis. J Anim Breed Genet 2024; 141:163-178. [PMID: 37902119 DOI: 10.1111/jbg.12831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/14/2023] [Accepted: 10/14/2023] [Indexed: 10/31/2023]
Abstract
As the swine industry continues to explore pork quality traits alongside growth, feed efficiency and carcass leanness traits, it becomes imperative to understand their underlying genetic relationships. Due to this increase in the number of desirable traits, animal breeders must also consider methods to efficiently perform direct genetic changes for each trait and evaluate alternative selection indexes with different sets of phenotypic measurements. Principal component analysis (PCA) and genome-wide association studies (GWAS) can be combined to understand the genetic architecture and biological mechanisms by defining biological types (biotypes) that relate these valuable traits. Therefore, the main objectives of this study were to: (1) estimate genomic-based genetic parameters; (2) define animal biotypes utilizing PCA; and (3) utilize GWAS to link the biotypes to candidate genes and quantitative trait loci (QTL). The phenotypic dataset included 2583 phenotypic records from female Duroc pigs from a terminal sire line. The pedigree file contained 193,764 animals and the genotype file included 21,309 animals with 35,651 single nucleotide polymorphisms (SNPs). Eight principal components (PCs), accounting for a total of 99.7% of the population variation, were defined for three growth, eight conventional carcass, 10 pork quality and 18 novel carcass traits. The eight biotypes defined from the PCs were found to be related to growth rate, maturity, meat quality and body structure, which were then related to candidate genes. Of the 175 candidate genes found, six of them [LDHA (SSC1), PIK3C3 (SSC6), PRKAG3 (SSC15), VRTN (SSC7), DLST (SSC7) and PAPPA (SSC1)] related to four PCs were found to be associated with previously defined QTL, linking the biotypes with biological processes involved with muscle growth, fat deposition, glycogen levels and skeletal development. Further functional analyses helped to make connections between biotypes, relating them through common KEGG pathways and gene ontology (GO) terms. These findings contribute to a better understanding of the genetic relationships between growth, carcass and meat quality traits in Duroc pigs, enabling breeders to better understand the biological mechanisms underlying the phenotypic expression of these traits.
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Affiliation(s)
- Hannah E Green
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
- Fast Genetics, Saskatoon, Saskatchewan, Canada
| | | | | | | | | | - Allan P Schinckel
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
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Abdi H, Alipour H, Bernousi I, Jafarzadeh J, Rodrigues PC. Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS. Sci Rep 2023; 13:9927. [PMID: 37336905 DOI: 10.1038/s41598-023-36134-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/30/2023] [Indexed: 06/21/2023] Open
Abstract
Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert's method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.
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Affiliation(s)
- Hossein Abdi
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
| | - Hadi Alipour
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
| | - Iraj Bernousi
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
| | - Jafar Jafarzadeh
- Dryland Agricultural Research Institute (DARI), Agriculture Research, Education and Extension Organization (AREEO), Maragheh, Iran
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Buckner T, Johnson RK, Vanderlinden LA, Carry PM, Romero A, Onengut-Gumuscu S, Chen WM, Kim S, Fiehn O, Frohnert BI, Crume T, Perng W, Kechris K, Rewers M, Norris JM. Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population. Front Nutr 2023; 10:1040993. [PMID: 37057071 PMCID: PMC10086335 DOI: 10.3389/fnut.2023.1040993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Background Oxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins. Methods We conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10-8). Results The SNP rs143070873 was associated with (p < 5×10-8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis. Conclusion Genetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.
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Affiliation(s)
- Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Department of Kinesiology, Nutrition, and Dietetics, University of Northern Colorado, Greeley, CO, United States
| | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Department of Biomedical Informatics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Lauren A. Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Patrick M. Carry
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Alex Romero
- Department of Biomedical Informatics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Wei-Min Chen
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Soojeong Kim
- Department of Health Administration, Dongseo University, Busan, Republic of Korea
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California-Davis, Davis, CA, United States
| | - Brigitte I. Frohnert
- The Barbara Davis Center for Diabetes, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Tessa Crume
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Marian Rewers
- The Barbara Davis Center for Diabetes, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
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Taraszka K, Zaitlen N, Eskin E. Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations. PLoS Genet 2022; 18:e1010447. [PMID: 36342933 PMCID: PMC9671458 DOI: 10.1371/journal.pgen.1010447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 11/17/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022] Open
Abstract
We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
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Affiliation(s)
- Kodi Taraszka
- Department of Computer Science, University of California, Los Angeles, California, United States of America
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- * E-mail:
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6
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Reinert S. Quantitative genetics of pleiotropy and its potential for plant sciences. JOURNAL OF PLANT PHYSIOLOGY 2022; 276:153784. [PMID: 35944292 DOI: 10.1016/j.jplph.2022.153784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Stephan Reinert
- Friedrich-Alexander-University Erlangen-Nürnberg, Department of Biology, Division of Biochemistry, Biocomputing Lab, Staudtstraße 5, 91058, Erlangen, Germany.
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Fernandes SB, Casstevens TM, Bradbury PJ, Lipka AE. A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits. THE PLANT GENOME 2022; 15:e20200. [PMID: 35307964 DOI: 10.1002/tpg2.20200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high-throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype-to-phenotype relationships have focused on either multi-trait models that test a single marker at a time or multi-locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi-trait, multi-locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi-trait single-locus model and (b) a univariate multi-locus model. We used real marker data in maize (Zea mays L.) and soybean (Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi-trait models outperformed the univariate multi-locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol-related traits in maize grain, MSTEP identified the same peak-associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits.
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Affiliation(s)
- Samuel B Fernandes
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
| | | | | | - Alexander E Lipka
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
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Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, Gao X, Chen Y, Zhang L, Gao H, Li J, Xu L. Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle. Front Genet 2021; 12:750746. [PMID: 34912371 PMCID: PMC8667311 DOI: 10.3389/fgene.2021.750746] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3, MED28, NCAPG, LCORL, SLIT2, and IBSP, which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle.
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Affiliation(s)
- Qunhao Niu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianliu Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ling Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianzhen Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zezhao Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bo Zhu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xue Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yan Chen
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lupei Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huijiang Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junya Li
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingyang Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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Vilor-Tejedor N, Garrido-Martín D, Rodriguez-Fernandez B, Lamballais S, Guigó R, Gispert JD. Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO! Comput Struct Biotechnol J 2021; 19:5800-5810. [PMID: 34765095 PMCID: PMC8567328 DOI: 10.1016/j.csbj.2021.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/01/2022] Open
Abstract
Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.
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Affiliation(s)
- Natalia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | | | - Sander Lamballais
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Ma L, Qing C, Zhang M, Zou C, Pan G, Shen Y. GWAS with a PCA uncovers candidate genes for accumulations of microelements in maize seedlings. PHYSIOLOGIA PLANTARUM 2021; 172:2170-2180. [PMID: 34028036 DOI: 10.1111/ppl.13466] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/03/2021] [Accepted: 05/18/2021] [Indexed: 05/13/2023]
Abstract
Microelements are necessary for plant growth and development, they control key processes of physiological metabolism. Herein, we evaluated three accumulation-related performances for each of the four microelements (Fe, Zn, Cu, and Mn) among 305 inbred maize lines. Quantification of these microelements in maize roots and shoots revealed abundant phenotypic variations in the association panel, with the variation coefficients ranging from 0.31 to 0.76. Principal component analysis (PCA) of the three related traits (concentration in root, concentration in shoot, and transport coefficient) showed that PC1 and PC2 explained >95% of phenotypic variations for each element. The scores of PC1 and PC2 were thereby used for a genome-wide association study by combining 44,134 SNPs of this panel. A total of 27, 1, 5, and 3 SNPs were significantly (P < .05) associated with Zn-PC1, Zn-PC2, Cu-PC1, and Mn-PC2, respectively, with 11 genes closely linked (r2 > 0.8) to these SNPs. Of them, GRMZM2G142870, GRMZM2G045531, and GRMZM2G143512 were individually annotated as ABC transporter C family member 14, zinc transporter 3, and heavy metal ATPase10. A candidate gene association analysis further verified that GRMZM2G142870 and GRMZM2G045531 affect Zn and Mn accumulations, respectively. Evaluation of contrasting allele ratios in elite lines indicated that the majority of the alleles correlating with higher Zn or Cu had not been utilized in maize breeding. Integration of more "higher-accumulation" alleles in the elite lines will be practical for improving Zn and Cu accumulations in maize. Our findings contribute to genetic revelation and molecular marker-assisted selection of microelement accumulations in maize.
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Affiliation(s)
- Langlang Ma
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Chunyan Qing
- Mianyang Academy of Agricultural Sciences, Mianyang, China
| | - Minyan Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Chaoying Zou
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guangtang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yaou Shen
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
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Novel directions in data pre-processing and genome-wide association study (GWAS) methodologies to overcome ongoing challenges. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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12
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Ben Hdech D, Aubry C, Alibert B, Beucher D, Prosperi JM, Limami AM, Teulat B. Exploring natural diversity of Medicago truncatula reveals physiotypes and loci associated with the response of seedling performance to nitrate supply. PHYSIOLOGIA PLANTARUM 2020; 170:227-247. [PMID: 32492180 DOI: 10.1111/ppl.13144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/14/2020] [Accepted: 05/28/2020] [Indexed: 06/11/2023]
Abstract
Seedling pre-emergence is a critical phase of development for successful crop establishment because of its susceptibility to environmental conditions. In a context of reduced use of inorganic fertilizers, the genetic bases of the response of seedlings to nitrate supply received little attention. This issue is important even in legumes where nitrate absorption starts early after germination, before nodule development. Natural variation of traits characterizing seedling growth in the absence or presence of nitrate was investigated in a core collection of 192 accessions of Medicago truncatula. Plasticity indexes to the absence of nitrate were calculated. The genetic determinism of the traits was dissected by genome-wide association study (GWAS). The absence of nitrate affected seed biomass mobilization and root/shoot length ratio. However, the large range of genetic variability revealed different seedling performances within natural diversity. A principal component analysis (PCA) carried out with plasticity indexes highlighted four physiotypes of accessions differing in relationships between seedling elongation and seed biomass partitioning traits in response to the absence of nitrate. Finally, GWAS revealed 45 associations with single or combined traits corresponding to coordinates of accessions on PCA, as well as two clusters of genes encoding sugar transporters and glutathione transferases surrounding loci associated with seedling elongation traits.
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Affiliation(s)
- Douae Ben Hdech
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
| | - Catherine Aubry
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
| | - Bénédicte Alibert
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
| | - Daniel Beucher
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
| | - Jean-Marie Prosperi
- AGAP, Université de Montpellier, CIRAD, INRAE, Institut Agro, 2, place P. Viala, Montpellier cedex 1, 34060, France
| | - Anis M Limami
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
| | - Béatrice Teulat
- IRHS-UMR1345, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 42, rue Georges Morel, Beaucouzé, 49071, France
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Rice BR, Fernandes SB, Lipka AE. Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture. PLANT & CELL PHYSIOLOGY 2020; 61:1427-1437. [PMID: 32186727 DOI: 10.1093/pcp/pcaa039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 03/17/2020] [Indexed: 05/23/2023]
Abstract
Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.
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Affiliation(s)
- Brian R Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | | | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
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14
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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15
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Genomic regions associated with principal components for growth, visual score and reproductive traits in Nellore cattle. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.103936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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16
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Wolf JM, Barnard M, Xia X, Ryder N, Westra J, Tintle N. Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:719-730. [PMID: 31797641 PMCID: PMC6907735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The popularization of biobanks provides an unprecedented amount of genetic and phenotypic information that can be used to research the relationship between genetics and human health. Despite the opportunities these datasets provide, they also pose many problems associated with computational time and costs, data size and transfer, and privacy and security. The publishing of summary statistics from these biobanks, and the use of them in a variety of downstream statistical analyses, alleviates many of these logistical problems. However, major questions remain about how to use summary statistics in all but the simplest downstream applications. Here, we present a novel approach to utilize basic summary statistics (estimates from single marker regressions on single phenotypes) to evaluate more complex phenotypes using multivariate methods. In particular, we present a covariate-adjusted method for conducting principal component analysis (PCA) utilizing only biobank summary statistics. We validate exact formulas for this method, as well as provide a framework of estimation when specific summary statistics are not available, through simulation. We apply our method to a real data set of fatty acid and genomic data.
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Affiliation(s)
- Jack M Wolf
- Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Northfield, MN 55057, USA,
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