European maize landraces made accessible for plant breeding and genome-based studies

Hölker, Armin C.; Mayer, Manfred; Presterl, Thomas; Bolduan, Therese; Bauer, Eva; Ordás, Bernardo; Brauner, Pedro Corrêa; Ouzunova, Milena; Melchinger, Albrecht E.; Schön, Chris‐Carolin

doi:10.1007/s00122-019-03428-8

“…This indicates that the transcriptomic variation observed in our study mainly reflects general genotypic differences and only to a minor degree differences in cold tolerance or cold treatment effects. Substantial genetic variability between genotypes in contrast to treatment effects, as observed in our experiments, was also described in previous studies on the diversity of European flint landraces accessed through doubled haploids [4,8,27]. Similar results were obtained in a set of diverse European flint and dent inbred lines subjected to heat stress where the transcriptomic responses between heat tolerant and susceptible genotypes were highly divergent [33].…”

Section: Transcriptomic Diversity With Respect To Cold Tolerancesupporting

confidence: 90%

“…We hypothesized that European maize landraces display substantial variation for cold tolerance thus carrying beneficial alleles for this trait which might not be present in elite material. This notion is supported by a study which demonstrated that in a panel of 35 European maize flint landraces including "Petkuser Ferdinand rot" used in this study, one landrace covers at least 75% of the genomic variation of all landraces present in this panel [27]. Phenotypic variation in the DH population derived from the European maize flint landrace "Petkuser Ferdinand rot" used in this study was demonstrated by > 4-fold changes in cold tolerance of root growth (Fig.…”

Section: Discussionsupporting

confidence: 79%

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Transcriptomic diversity in seedling roots of European flint maize in response to cold

Frey

¹

,

Pitz

²

,

Schön

³

et al. 2020

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Background: Low temperatures decrease the capacity for biomass production and lead to growth retardation up to irreversible cellular damage in modern maize cultivars. European flint landraces are an untapped genetic resource for genes and alleles conferring cold tolerance which they acquired during their adaptation to the agroecological conditions in Europe. Results: Based on a phenotyping experiment of 276 doubled haploid lines derived from the European flint landrace "Petkuser Ferdinand Rot" diverging for cold tolerance, we selected 21 of these lines for an RNA-seq experiment. The different genotypes showed highly variable transcriptomic responses to cold. We identified 148, 3254 and 563 genes differentially expressed with respect to cold treatment, cold tolerance and growth rate at cold, respectively. Gene ontology (GO) term enrichment demonstrated that the detoxification of reactive oxygen species is associated with cold tolerance, whereas amino acids might play a crucial role as antioxidant precursors and signaling molecules. Conclusion: Doubled haploids representing a European maize flint landrace display genotype-specific transcriptome patterns associated with cold response, cold tolerance and seedling growth rate at cold. Identification of cold regulated genes in European flint germplasm, could be a starting point for introgressing such alleles in modern breeding material for maize improvement.

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“…In the following, we will consider genotypic data of 910 doubled haploid (DH) lines of two European maize ( Zea mays ) landraces ( n = 501 Kemater Landmais Gelb (KE) and n = 409 Petkuser Ferdinand Rot (PE), (Holker et al . 2019)) genotyped using the 600k Affymetrix® Axiom® Maize Array (Unterseer et al .…”

Section: Methodsmentioning

confidence: 99%

Improving imputation quality in BEAGLE for crop and livestock data

Pook¹,

Mayer

²

,

Geibel³

et al. 2019

Preprint

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Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals. KEYWORDSimputation BEAGLE reference panel reference genome 1 23 special tools for both cases have been developed. As fully homozy-24 gous lines are commonly present in crops, the software TASSEL 25 (Bradbury et al. 2007) was developed to work well on this data 26 structure (Swarts et al. 2014). Since pedigrees in animal breeding 27 1 INVESTIGATIONS can be much denser than in human populations (both w.r.t. depth 28 and family size), tools like FImpute (Sargolzaei et al. 2014) and 29 AlphaImpute (Hickey et al. 2011) have been developed to fully 30 utilize this information. 31In the imputation process all those methods use the fact that physi-32 cally close markers are likely inherited together, resulting in non-33 random associations of alleles. These methods thereby rely on the 34 knowledge of the physical position or at least the order of markers 35 for modeling linkage and thus the resulting linkage disequilibrium 36 (LD). In contrast, the software LinkImpute (Money et al. 2015) ac-37 counts for LD between pairs of markers and not their physical 38 positions. This can be particularly relevant for species in which no 39 reference sequence is available or whose genomes are known for a 40 high amount of translocations and inversions. 41 In contrast to other methods using a HMM, the Markov chain in 42 BEAGLE is not initialized by ...

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“…In the following, we will consider genotypic data of 910 doubled haploid (DH) lines of two European maize (Zea mays) landraces (n ¼ 501 Kemater Landmais Gelb (KE) and n ¼ 409 Petkuser Ferdinand Rot (PE), (Hölker et al 2019)) genotyped using the 600k Affymetrix Axiom Maize Array (Unterseer et al 2014). Markers were filtered for being assigned to the highest quality class (Poly High Resolution (Pirani et al 2013)), having a callrate of at least 90%, and for having at most 5% heterozygous calls, as no heterozygous calls are expected for DH lines.…”

Section: Genotype Data Usedmentioning

confidence: 99%

Improving Imputation Quality in BEAGLE for Crop and Livestock Data

Pook

¹

,

Mayer

²

,

Geibel

³

et al. 2020

G3 Genes|Genomes|Genetics

Self Cite

View full text Add to dashboard Cite

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

show abstract

European maize landraces made accessible for plant breeding and genome-based studies

Cited by 52 publications

References 42 publications

Transcriptomic diversity in seedling roots of European flint maize in response to cold

Transcriptomic diversity in seedling roots of European flint maize in response to cold

Improving imputation quality in BEAGLE for crop and livestock data

Improving Imputation Quality in BEAGLE for Crop and Livestock Data

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