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Parée T, Noble L, Ferreira Gonçalves J, Teotónio H. rec-1 loss of function increases recombination in the central gene clusters at the expense of autosomal pairing centers. Genetics 2024; 226:iyad205. [PMID: 38001364 DOI: 10.1093/genetics/iyad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/03/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Meiotic control of crossover (CO) number and position is critical for homologous chromosome segregation and organismal fertility, recombination of parental genotypes, and the generation of novel genetic combinations. We here characterize the recombination rate landscape of a rec-1 loss of function modifier of CO position in Caenorhabditis elegans, one of the first ever modifiers discovered. By averaging CO position across hermaphrodite and male meioses and by genotyping 203 single-nucleotide variants covering about 95% of the genome, we find that the characteristic chromosomal arm-center recombination rate domain structure is lost in the loss of function rec-1 mutant. The rec-1 loss of function mutant smooths the recombination rate landscape but is insufficient to eliminate the nonuniform position of CO. Lower recombination rates in the rec-1 mutant are particularly found in the autosomal arm domains containing the pairing centers. We further find that the rec-1 mutant is of little consequence for organismal fertility and egg viability and thus for rates of autosomal nondisjunction. It nonetheless increases X chromosome nondisjunction rates and thus male appearance. Our findings question the maintenance of recombination rate heritability and genetic diversity among C. elegans natural populations, and they further suggest that manipulating genetic modifiers of CO position will help find quantitative trait loci located in low-recombining genomic regions normally refractory to discovery.
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Affiliation(s)
- Tom Parée
- Institut de Biologie de l'École Normale Supérieure, CNRS UMR, 8197, Inserm U1024, PSL Research University, Paris F-75005, France
| | - Luke Noble
- Institut de Biologie de l'École Normale Supérieure, CNRS UMR, 8197, Inserm U1024, PSL Research University, Paris F-75005, France
- EnviroDNA, 95 Albert St., Brunswick, Victoria 3065, Australia
| | - João Ferreira Gonçalves
- Institut de Biologie de l'École Normale Supérieure, CNRS UMR, 8197, Inserm U1024, PSL Research University, Paris F-75005, France
| | - Henrique Teotónio
- Institut de Biologie de l'École Normale Supérieure, CNRS UMR, 8197, Inserm U1024, PSL Research University, Paris F-75005, France
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2
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Mallard F, Afonso B, Teotónio H. Selection and the direction of phenotypic evolution. eLife 2023; 12:e80993. [PMID: 37650381 PMCID: PMC10564456 DOI: 10.7554/elife.80993] [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: 06/11/2022] [Accepted: 07/14/2023] [Indexed: 09/01/2023] Open
Abstract
Predicting adaptive phenotypic evolution depends on invariable selection gradients and on the stability of the genetic covariances between the component traits of the multivariate phenotype. We describe the evolution of six traits of locomotion behavior and body size in the nematode Caenorhabditis elegans for 50 generations of adaptation to a novel environment. We show that the direction of adaptive multivariate phenotypic evolution can be predicted from the ancestral selection differentials, particularly when the traits were measured in the new environment. Interestingly, the evolution of individual traits does not always occur in the direction of selection, nor are trait responses to selection always homogeneous among replicate populations. These observations are explained because the phenotypic dimension with most of the ancestral standing genetic variation only partially aligns with the phenotypic dimension under directional selection. These findings validate selection theory and suggest that the direction of multivariate adaptive phenotypic evolution is predictable for tens of generations.
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Affiliation(s)
- François Mallard
- Institut de Biologie de l’École Normale Supérieure, CNRS UMR 8197, Inserm U1024, PSL Research UniversityParisFrance
| | - Bruno Afonso
- Institut de Biologie de l’École Normale Supérieure, CNRS UMR 8197, Inserm U1024, PSL Research UniversityParisFrance
| | - Henrique Teotónio
- Institut de Biologie de l’École Normale Supérieure, CNRS UMR 8197, Inserm U1024, PSL Research UniversityParisFrance
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3
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Mallard F, Noble L, Baer CF, Teotónio H. Variation in mutational (co)variances. G3 (BETHESDA, MD.) 2023; 13:jkac335. [PMID: 36548954 PMCID: PMC9911065 DOI: 10.1093/g3journal/jkac335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/10/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022]
Abstract
Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode Caenorhabditis elegans that accumulated mutations in a nearly neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of C. elegans genotypes allows selection on new phenotypic dimensions of locomotion behavior.
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Affiliation(s)
- François Mallard
- Institut de Biologie de l’École Normale Supérieure, PSL Research University, CNRS UMR 8197, Inserm U1024, F-75005 Paris, France
| | - Luke Noble
- Institut de Biologie de l’École Normale Supérieure, PSL Research University, CNRS UMR 8197, Inserm U1024, F-75005 Paris, France
| | - Charles F Baer
- Department of Biology, University of Florida Genetics Institute, University of Florida, Gainsville, FL 32611, USA
| | - Henrique Teotónio
- Institut de Biologie de l’École Normale Supérieure, PSL Research University, CNRS UMR 8197, Inserm U1024, F-75005 Paris, France
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4
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Shaver AO, Garcia BM, Gouveia GJ, Morse AM, Liu Z, Asef CK, Borges RM, Leach FE, Andersen EC, Amster IJ, Fernández FM, Edison AS, McIntyre LM. An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics. Front Mol Biosci 2022; 9:930204. [PMID: 36438654 PMCID: PMC9682135 DOI: 10.3389/fmolb.2022.930204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/10/2022] [Indexed: 11/27/2022] Open
Abstract
Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.
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Affiliation(s)
- Amanda O. Shaver
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States
| | - Brianna M. Garcia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Alison M. Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Zihao Liu
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ricardo M. Borges
- Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Franklin E. Leach
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Environmental Health Science, University of Georgia, Athens, GA, United States
| | - Erik C. Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, United States
| | - I. Jonathan Amster
- Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Arthur S. Edison
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Lauren M. McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States,University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States,*Correspondence: Lauren M. McIntyre,
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5
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Widmayer SJ, Evans KS, Zdraljevic S, Andersen EC. Evaluating the power and limitations of genome-wide association studies in Caenorhabditis elegans. G3 (BETHESDA, MD.) 2022; 12:6583190. [PMID: 35536194 PMCID: PMC9258552 DOI: 10.1093/g3journal/jkac114] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/02/2022] [Indexed: 11/30/2022]
Abstract
Quantitative genetics in Caenorhabditis elegans seeks to identify naturally segregating genetic variants that underlie complex traits. Genome-wide association studies scan the genome for individual genetic variants that are significantly correlated with phenotypic variation in a population, or quantitative trait loci. Genome-wide association studies are a popular choice for quantitative genetic analyses because the quantitative trait loci that are discovered segregate in natural populations. Despite numerous successful mapping experiments, the empirical performance of genome-wide association study has not, to date, been formally evaluated in C. elegans. We developed an open-source genome-wide association study pipeline called NemaScan and used a simulation-based approach to provide benchmarks of mapping performance in collections of wild C. elegans strains. Simulated trait heritability and complexity determined the spectrum of quantitative trait loci detected by genome-wide association studies. Power to detect smaller-effect quantitative trait loci increased with the number of strains sampled from the C. elegans Natural Diversity Resource. Population structure was a major driver of variation in mapping performance, with populations shaped by recent selection exhibiting significantly lower false discovery rates than populations composed of more divergent strains. We also recapitulated previous genome-wide association studies of experimentally validated quantitative trait variants. Our simulation-based evaluation of performance provides the community with critical context to pursue quantitative genetic studies using the C. elegans Natural Diversity Resource to elucidate the genetic basis of complex traits in C. elegans natural populations.
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Affiliation(s)
- Samuel J Widmayer
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
| | - Kathryn S Evans
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
| | - Stefan Zdraljevic
- Department of Biological Chemistry, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Erik C Andersen
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
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6
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Macdonald SJ, Cloud-Richardson KM, Sims-West DJ, Long AD. Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing. Genetics 2022; 220:iyab238. [PMID: 35100395 PMCID: PMC8893256 DOI: 10.1093/genetics/iyab238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/19/2021] [Indexed: 01/22/2024] Open
Abstract
Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.
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Affiliation(s)
- Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA
| | | | - Dylan J Sims-West
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, CA 92697, USA
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7
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Tintori SC, Sloat SA, Rockman MV. Rapid Isolation of Wild Nematodes by Baermann Funnel. J Vis Exp 2022:10.3791/63287. [PMID: 35156660 PMCID: PMC8857960 DOI: 10.3791/63287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
Beyond being robust experimental model organisms, Caenorhabditis elegans and its relatives are also real animals that live in nature. Studies of wild nematodes in their natural environments are valuable for understanding many aspects of biology, including the selective regimes in which distinctive genomic and phenotypic characters evolve, the genetic basis for complex trait variation, and the natural genetic diversity fundamental to all animal populations. This manuscript describes a simple and efficient method for extracting nematodes from their natural substrates, including rotting fruits, flowers, fungi, leaf litter, and soil. The Baermann funnel method, a classical nematology technique, selectively isolates active nematodes from their substrates. Because it recovers nearly all active worms from the sample, the Baermann funnel technique allows for the recovery of rare and slow-growing genotypes that co-occur with abundant and fast-growing genotypes, which might be missed in extraction methods that involve multiple generations of reproduction. The technique is also well suited to addressing metagenetic, population-genetic, and ecological questions. It captures the entire population in a sample simultaneously, allowing an unbiased view of the natural distribution of ages, sexes, and genotypes. The protocol allows for deployment at scale in the field, rapidly converting substrates into worm plates, and the authors have validated it through fieldwork on multiple continents.
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Affiliation(s)
- Sophia C Tintori
- Department of Biology and Center for Genomics & Systems Biology, New York University
| | - Solomon A Sloat
- Department of Biology and Center for Genomics & Systems Biology, New York University
| | - Matthew V Rockman
- Department of Biology and Center for Genomics & Systems Biology, New York University;
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8
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Andersen EC, Rockman MV. Natural genetic variation as a tool for discovery in Caenorhabditis nematodes. Genetics 2022; 220:iyab156. [PMID: 35134197 PMCID: PMC8733454 DOI: 10.1093/genetics/iyab156] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/11/2021] [Indexed: 11/12/2022] Open
Abstract
Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.
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Affiliation(s)
- Erik C Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60201, USA
| | - Matthew V Rockman
- Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY 10003, USA
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9
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Evans KS, van Wijk MH, McGrath PT, Andersen EC, Sterken MG. From QTL to gene: C. elegans facilitates discoveries of the genetic mechanisms underlying natural variation. Trends Genet 2021; 37:933-947. [PMID: 34229867 DOI: 10.1016/j.tig.2021.06.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 11/15/2022]
Abstract
Although many studies have examined quantitative trait variation across many species, only a small number of genes and thereby molecular mechanisms have been discovered. Without these data, we can only speculate about evolutionary processes that underlie trait variation. Here, we review how quantitative and molecular genetics in the nematode Caenorhabditis elegans led to the discovery and validation of 37 quantitative trait genes over the past 15 years. Using these data, we can start to make inferences about evolution from these quantitative trait genes, including the roles that coding versus noncoding variation, gene family expansion, common versus rare variants, pleiotropy, and epistasis play in trait variation across this species.
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Affiliation(s)
- Kathryn S Evans
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA; Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
| | - Marijke H van Wijk
- Laboratory of Nematology, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands
| | - Patrick T McGrath
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Erik C Andersen
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA.
| | - Mark G Sterken
- Laboratory of Nematology, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands.
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10
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Pelizzola M, Behr M, Li H, Munk A, Futschik A. Multiple haplotype reconstruction from allele frequency data. NATURE COMPUTATIONAL SCIENCE 2021; 1:262-271. [PMID: 38217170 DOI: 10.1038/s43588-021-00056-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 03/12/2021] [Indexed: 01/15/2024]
Abstract
Because haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose an efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. Compared to other methods, haploSep is very fast, with linear computational complexity in the haplotype length. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.
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Affiliation(s)
- Marta Pelizzola
- Vetmeduni Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Merle Behr
- University of California, Berkeley, CA, USA
| | - Housen Li
- University of Göttingen, Göttingen, Germany
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
| | - Axel Munk
- University of Göttingen, Göttingen, Germany
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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