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Fairlie W, Norman A, Edwards J, Mather DE, Kuchel H. Genetic analysis of late-maturity α-amylase in twelve wheat populations. PLANTA 2024; 259:40. [PMID: 38265531 PMCID: PMC10808134 DOI: 10.1007/s00425-023-04319-5] [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: 07/27/2023] [Accepted: 12/22/2023] [Indexed: 01/25/2024]
Abstract
MAIN CONCLUSION Genetic loci, particularly those with an effect in the independent panel, could be utilised to further reduce LMA expression when used with favourable combinations of genes known to affect LMA. Late maturity α-amylase (LMA) is a grain quality defect involving elevated α-amylase within the aleurone of wheat (Triticum aestivum L.) grains. The genes known to affect expression are the reduced height genes Rht-B1 (chromosome 4B) and Rht-D1 (chromosome 4D), and an ent-copalyl diphosphate synthase gene (LMA-1) on chromosome 7B. Other minor effect loci have been reported, but these are poorly characterised and further genetic understanding is needed. In this study, twelve F4-derived populations were created through single seed descent, genotyped and evaluated for LMA. LMA-1 haplotype C and the Rht-D1b allele substantially reduced LMA expression. The alternative dwarfing genes Rht13 and Rht18 had no significant effect on LMA expression. Additional quantitative trait loci (QTL) were mapped at 16 positions in the wheat genome. Effects on LMA expression were detected for four of these QTL in a large independent panel of Australian wheat lines. The QTL detected in mapping populations and confirmed in the large independent panel provide further opportunity for selection against LMA, especially if combined with Rht-D1b and/or favourable haplotypes of LMA-1.
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Affiliation(s)
- William Fairlie
- School of Agriculture, Food and Wine, The University of Adelaide, PMB1, Glen Osmond, SA, 5064, Australia.
- Australian Grain Technologies, PO Box 341, Roseworthy, SA, 5371, Australia.
| | - Adam Norman
- School of Agriculture, Food and Wine, The University of Adelaide, PMB1, Glen Osmond, SA, 5064, Australia
- Australian Grain Technologies, PO Box 341, Roseworthy, SA, 5371, Australia
| | - James Edwards
- School of Agriculture, Food and Wine, The University of Adelaide, PMB1, Glen Osmond, SA, 5064, Australia
- Australian Grain Technologies, PO Box 341, Roseworthy, SA, 5371, Australia
| | - Diane E Mather
- School of Agriculture, Food and Wine, The University of Adelaide, PMB1, Glen Osmond, SA, 5064, Australia
| | - Haydn Kuchel
- School of Agriculture, Food and Wine, The University of Adelaide, PMB1, Glen Osmond, SA, 5064, Australia
- Australian Grain Technologies, PO Box 341, Roseworthy, SA, 5371, Australia
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Raman H, Shamaya N, Pirathiban R, McVittie B, Raman R, Cullis B, Easton A. Quantitative Trait Loci for Genotype and Genotype by Environment Interaction Effects for Seed Yield Plasticity to Terminal Water-Deficit Conditions in Canola ( Brassica napus L.). PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12040720. [PMID: 36840067 PMCID: PMC9964187 DOI: 10.3390/plants12040720] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 06/12/2023]
Abstract
Canola plants suffer severe crop yield and oil content reductions when exposed to water-deficit conditions, especially during the reproductive stages of plant development. There is a pressing need to develop canola cultivars that can perform better under increased water-deficit conditions with changing weather patterns. In this study, we analysed genetic determinants for the main effects of quantitative trait loci (QTL), (Q), and the interaction effects of QTL and Environment (QE) underlying seed yield and related traits utilising 223 doubled haploid (DH) lines of canola in well-watered and water-deficit conditions under a rainout shelter. Moderate water-deficit at the pre-flowering stage reduced the seed yield to 40.8%. Multi-environmental QTL analysis revealed 23 genomic regions associated with days to flower (DTF), plant height (PH) and seed yield (SY) under well-watered and water-deficit conditions. Three seed yield QTL for main effects were identified on chromosomes A09, C03, and C09, while two were related to QE interactions on A02 and C09. Two QTL regions were co-localised to similar genomic regions for flowering time and seed yield (A09) and the second for plant height and chlorophyll content. The A09 QTL was co-located with a previously mapped QTL for carbon isotope discrimination (Δ13C) that showed a positive relationship with seed yield in the same population. Opposite allelic effects for plasticity in seed yield were identified due to QE interactions in response to water stress on chromosomes A02 and C09. Our results showed that QTL's allelic effects for DTF, PH, and SY and their correlation with Δ13C are stable across environments (field conditions, previous study) and contrasting water regimes (this study). The QTL and DH lines that showed high yield under well-watered and water-deficit conditions could be used to manipulate water-use efficiency for breeding improved canola cultivars.
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Affiliation(s)
- Harsh Raman
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - Nawar Shamaya
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - Ramethaa Pirathiban
- Centre for Biometrics and Data Science for Sustainable Primary Industries, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Brett McVittie
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - Rosy Raman
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - Brian Cullis
- Centre for Biometrics and Data Science for Sustainable Primary Industries, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Andrew Easton
- Advanta Seeds Pty Ltd., 268 Anzac Avenue, Toowoomba, QLD 4350, Australia
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Raman H, Raman R, Pirathiban R, McVittie B, Sharma N, Liu S, Qiu Y, Zhu A, Kilian A, Cullis B, Farquhar GD, Stuart‐Williams H, White R, Tabah D, Easton A, Zhang Y. Multienvironment QTL analysis delineates a major locus associated with homoeologous exchanges for water-use efficiency and seed yield in canola. PLANT, CELL & ENVIRONMENT 2022; 45:2019-2036. [PMID: 35445756 PMCID: PMC9325393 DOI: 10.1111/pce.14337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 04/06/2022] [Indexed: 05/29/2023]
Abstract
Canola varieties exhibit variation in drought avoidance and drought escape traits, reflecting adaptation to water-deficit environments. Our understanding of underlying genes and their interaction across environments in improving crop productivity is limited. A doubled haploid population was analysed to identify quantitative trait loci (QTL) associated with water-use efficiency (WUE) related traits. High WUE in the vegetative phase was associated with low seed yield. Based on the resequenced parental genome data, we developed sequence-capture-based markers and validated their linkage with carbon isotope discrimination (Δ13 C) in an F2 population. RNA sequencing was performed to determine the expression of candidate genes underlying Δ13 C QTL. QTL contributing to main and QTL × environment interaction effects for Δ13 C and yield were identified. One multiple-trait QTL for Δ13 C, days to flower, plant height, and seed yield was identified on chromosome A09. Interestingly, this QTL region overlapped with a homoeologous exchange (HE) event, suggesting its association with the multiple traits. Transcriptome analysis revealed 121 significantly differentially expressed genes underlying Δ13 C QTL on A09 and C09, including in HE regions. Sorting out the negative relationship between vegetative WUE and seed yield is a priority. Genetic and genomic resources and knowledge so developed could improve canola WUE and yield.
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Affiliation(s)
- Harsh Raman
- NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaNew South WalesAustralia
| | - Rosy Raman
- NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaNew South WalesAustralia
| | - Ramethaa Pirathiban
- Centre for Biometrics and Data Science for Sustainable Primary Industries, National Institute for Applied Statistics Research AustraliaUniversity of WollongongWollongongNew South WalesAustralia
| | - Brett McVittie
- NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaNew South WalesAustralia
| | - Niharika Sharma
- NSW Department of Primary IndustriesOrange Agricultural InstituteOrangeNew South WalesAustralia
| | - Shengyi Liu
- The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs of PRCOil Crops Research Institute, Chinese Academy of Agricultural SciencesWuhanHubeiChina
| | - Yu Qiu
- NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaNew South WalesAustralia
| | - Anyu Zhu
- Diversity Arrays Technology P/LUniversity of CanberraCanberraAustralian Capital TerritoryAustralia
| | - Andrzej Kilian
- Diversity Arrays Technology P/LUniversity of CanberraCanberraAustralian Capital TerritoryAustralia
| | - Brian Cullis
- Centre for Biometrics and Data Science for Sustainable Primary Industries, National Institute for Applied Statistics Research AustraliaUniversity of WollongongWollongongNew South WalesAustralia
| | - Graham D. Farquhar
- Research School of BiologyAustralian National UniversityCanberraAustralian Capital TerritoryAustralia
| | - Hilary Stuart‐Williams
- Research School of BiologyAustralian National UniversityCanberraAustralian Capital TerritoryAustralia
| | | | - David Tabah
- Advanta Seeds Pty LtdToowoombaQueenslandAustralia
| | | | - Yuanyuan Zhang
- The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs of PRCOil Crops Research Institute, Chinese Academy of Agricultural SciencesWuhanHubeiChina
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4
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Mago R, Chen C, Xia X, Whan A, Forrest K, Basnet BR, Perera G, Chandramohan S, Randhawa M, Hayden M, Bansal U, Huerta-Espino J, Singh RP, Bariana H, Lagudah E. Adult plant stem rust resistance in durum wheat Glossy Huguenot: mapping, marker development and validation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1541-1550. [PMID: 35199199 DOI: 10.1007/s00122-022-04052-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 01/28/2022] [Indexed: 05/12/2023]
Abstract
Adult plant stem rust resistance locus, QSrGH.cs-2AL, was identified in durum wheat Glossy Huguenot and mendelised as Sr63. Markers closely linked with Sr63 were developed. An F3 population from a Glossy Huguenot (GH)/Bansi cross used in a previous Australian study was advanced to F6 for molecular mapping of adult plant stem rust resistance. Maturity differences among F6 lines confounded assessments of stem rust response. GH was crossed with a stem rust susceptible F6 recombinant inbred line (RIL), GHB14 (M14), with similar maturity and an F6:7 population was developed through single seed descent method. F7 and F8 RILs were tested along with the parents at different locations. The F6 individual plants and both parents were genotyped using the 90 K single nucleotide polymorphism (SNP) wheat array. Stem rust resistance QTL on the long arms of chromosomes 1B (QSrGH.cs-1BL) and 2A (QSrGH.cs-2AL) were detected. QSrGH.cs-1BL and QSrGH.cs-2AL were both contributed by GH and explained 22% and 18% adult plant stem rust response variation, respectively, among GH/M14 RIL population. RILs carrying combinations of these QTL reduced more than 14% stem rust severity compared to those that possessed QSrGH.cs-1BL and QSrGH.cs-2AL individually. QSrGH.cs1BL was demonstrated to be the same as Sr58/Lr46/Yr29/Pm39 through marker genotyping. Lines lacking QSrGH.cs-1BL were used to Mendelise QSrGH.cs-2AL. Based on genomic locations of previously catalogued stem rust resistance genes and the QSrGH.cs-2AL map, it appeared to represent a new APR locus and was permanently named Sr63. SNP markers associated with Sr63 were converted to kompetetive allele-specific PCR (KASP) assays and were validated on a set of durum cultivars.
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Affiliation(s)
- Rohit Mago
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia.
| | - Chunhong Chen
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia
| | - Xiaodi Xia
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia
| | - Alex Whan
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia
| | - Kerrie Forrest
- Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC, 3083, Australia
| | - Bhoja R Basnet
- CIMMYT, Carretera Mexico-Veracruz Km 18, El Batan, Texcoco, Estado de México, Mexico
| | - Geetha Perera
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia
| | - Sutha Chandramohan
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia
| | - Mandeep Randhawa
- ICRAF House, CIMMYT Kenya, United Nations Avenue, Gigiri, Village Market, P.O. Box 1041, 00621, Nairobi, Kenya
| | - Matthew Hayden
- Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC, 3083, Australia
| | - Urmil Bansal
- Faculty of Science, School of Life and Environmental Sciences, The University of Sydney Plant Breeding Institute, 107 Cobbitty Road, Cobbitty, NSW, 2570, Australia
| | - Julio Huerta-Espino
- Campo Experimental Valle de México, INIFAP, Chapingo, Estado de México, Mexico
| | - Ravi P Singh
- CIMMYT, Carretera Mexico-Veracruz Km 18, El Batan, Texcoco, Estado de México, Mexico
| | - Harbans Bariana
- Faculty of Science, School of Life and Environmental Sciences, The University of Sydney Plant Breeding Institute, 107 Cobbitty Road, Cobbitty, NSW, 2570, Australia.
| | - Evans Lagudah
- CSIRO Agriculture and Food, P.O. Box 1700, Canberra, ACT, 2601, Australia.
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Cannon AE, Marston EJ, Kiszonas AM, Hauvermale AL, See DR. Late-maturity α-amylase (LMA): exploring the underlying mechanisms and end-use quality effects in wheat. PLANTA 2021; 255:2. [PMID: 34837530 PMCID: PMC8627422 DOI: 10.1007/s00425-021-03749-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/03/2021] [Indexed: 06/13/2023]
Abstract
MAIN CONCLUSION A comprehensive understanding of LMA from the underlying molecular aspects to the end-use quality effects will greatly benefit the global wheat industry and those whose livelihoods depend upon it. Late-maturity α-amylase (LMA) leads to the expression and protein accumulation of high pI α-amylases during late grain development. This α-amylase is maintained through harvest and leads to an unacceptable low falling number (FN), the wheat industry's standard measure for predicting end-use quality. Unfortunately, low FN leads to significant financial losses for growers. As a result, wheat researchers are working to understand and eliminate LMA from wheat breeding programs, with research aims that include unraveling the genetic, biochemical, and physiological mechanisms that lead to LMA expression. In addition, cereal chemists and quality scientists are working to determine if and how LMA-affected grain impacts end-use quality. This review is a comprehensive overview of studies focused on LMA and includes open questions and future directions.
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Affiliation(s)
- Ashley E. Cannon
- Wheat Health, Genetics, and Quality Research Unit, USDA Agricultural Research Service, Pullman, WA USA
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA USA
| | - Elliott J. Marston
- Department of Plant Pathology, Washington State University, Pullman, WA USA
| | - Alecia M. Kiszonas
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA USA
| | - Amber L. Hauvermale
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA USA
| | - Deven R. See
- Wheat Health, Genetics, and Quality Research Unit, USDA Agricultural Research Service, Pullman, WA USA
- Department of Plant Pathology, Washington State University, Pullman, WA USA
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6
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Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P, Leung H, Varshney RK, Cavanagh CR. MAGIC populations in crops: current status and future prospects. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2015; 128:999-1017. [PMID: 25855139 DOI: 10.1007/s00122-015-2506-0] [Citation(s) in RCA: 129] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 03/20/2015] [Indexed: 05/20/2023]
Abstract
MAGIC populations present novel challenges and opportunities in crops due to their complex pedigree structure. They offer great potential both for dissecting genomic structure and for improving breeding populations. The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery.
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Affiliation(s)
- B Emma Huang
- Digital Productivity and Agriculture Flagships, CSIRO, Dutton Park, QLD, 4102, Australia,
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7
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Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model. Genetics 2015; 200:59-68. [PMID: 25724382 DOI: 10.1534/genetics.114.171447] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 02/16/2015] [Indexed: 11/18/2022] Open
Abstract
Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.
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Abstract
Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis—be it multienvironment or multitrait — it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.
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Verbyla AP, George AW, Cavanagh CR, Verbyla KL. Whole-genome QTL analysis for MAGIC. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2014; 127:1753-1770. [PMID: 24927820 DOI: 10.1007/s00122-014-2337-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 05/21/2014] [Indexed: 05/28/2023]
Abstract
An efficient whole genome method of QTL analysis is presented for Multi-parent advanced generation integrated crosses. Multi-parent advanced generation inter-cross (MAGIC) populations have been developed for mice and several plant species and are useful for the genetic dissection of complex traits. The analysis of quantitative trait loci (QTL) in these populations presents some additional challenges compared with traditional mapping approaches. In particular, pedigree and marker information need to be integrated and founder genetic data needs to be incorporated into the analysis. Here, we present a method for QTL analysis that utilizes the probability of inheriting founder alleles across the whole genome simultaneously, either for intervals or markers. The probabilities can be found using three-point or Hidden Markov Model (HMM) methods. This whole-genome approach is evaluated in a simulation study and it is shown to be a powerful method of analysis. The HMM probabilities lead to low rates of false positives and low bias of estimated QTL effect sizes. An implementation of the approach is available as an R package. In addition, we illustrate the approach using a bread wheat MAGIC population.
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Affiliation(s)
- Arūnas P Verbyla
- Computational Informatics and Food Futures National Research Flagship, CSIRO, Atherton, QLD 4883, Australia,
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10
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Abstract
Progress in complex trait mapping in mice has been accelerated by the development of new populations suited to high-resolution mapping and by statistical methodologies that control for population structure. When combined with newly acquired catalogs of sequence variation in inbred strains, the genetic architecture of these new populations makes it possible to dissect complex traits down to the level of single variants. These analyses have shown not only that complex traits are caused by multiple contributing loci but also that each locus is likely due to the combined effects of multiple causal DNA variants. In combination with new rapid methods for producing transgenic mice that make it efficient to test candidate genes and variants, these advances significantly enhance the mouse genetics toolbox for dissecting quantitative traits.
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Affiliation(s)
- Richard Mott
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom; ,
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