Evidence map›Paper›PMID 28558688›Full record

ArticleBMC genomics2017

High-quality genetic mapping with ddRADseq in the non-model tree Quercus rubra.

Arpita Konar, Olivia Choudhury, Rebecca Bullis, Lauren Fiedler, Jacqueline M Kruser, Melissa T Stephens, Oliver Gailing, Scott Schlarbaum, Mark V Coggeshall, Margaret E Staton and 3 more

Abstract read
In one paragraph

Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Arpita KonarDepartment of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.
Olivia ChoudhuryDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
Rebecca BullisDepartment of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.
Lauren FiedlerDepartment of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.
Jacqueline M KruserInternal Medicine, Northwestern Memorial Hospital, Chicago, IL, 60611, USA.
Melissa T StephensDepartment of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.
Oliver GailingSchool of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.
Scott SchlarbaumDepartment of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, TN, 37996, USA.
Mark V CoggeshallSchool of Natural Resources, University of Missouri-Columbia, Columbia, MO, 65211, USA.
Margaret E StatonDepartment of Entomology and Plant Pathology, University of Tennessee, Knoxville, TN, 37996, USA.
John E CarlsonDepartment of Ecosystem Science and Management, Penn State, University Park, State College, PA, 16802, USA.
Scott EmrichDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
Jeanne Romero-SeversonDepartment of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA. jromeros@nd.edu.ORCID 0000-0003-4112-7238

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRestriction site associated DNA sequencing (RADseq) has the potential to be a broadly applicable, low-cost approach for high-quality genetic linkage mapping in forest trees lacking a reference genome. The statistical inference of linear order must be as accurate as possible for the correct ordering of sequence scaffolds and contigs to chromosomal locations. Accurate maps also facilitate the discovery of chromosome segments containing allelic variants conferring resistance to the biotic and abiotic stresses that threaten forest trees worldwide. We used ddRADseq for genetic mapping in the tree Quercus rubra, with an approach optimized to produce a high-quality map. Our study design also enabled us to model the results we would have obtained with less depth of coverage.

resultsOur sequencing design produced a high sequencing depth in the parents (248×) and a moderate sequencing depth (15×) in the progeny. The digital normalization method of generating a de novo reference and the SAMtools SNP variant caller yielded the most SNP calls (78,725). The major drivers of map inflation were multiple SNPs located within the same sequence (77% of SNPs called). The highest quality map was generated with a low level of missing data (5%) and a genome-wide threshold of 0.025 for deviation from Mendelian expectation. The final map included 849 SNP markers (1.8% of the 78,725 SNPs called). Downsampling the individual FASTQ files to model lower depth of coverage revealed that sequencing the progeny using 96 samples per lane would have yielded too few SNP markers to generate a map, even if we had sequenced the parents at depth 248×.

conclusionsThe ddRADseq technology produced enough high-quality SNP markers to make a moderately dense, high-quality map. The success of this project was due to high depth of coverage of the parents, moderate depth of coverage of the progeny, a good framework map, an optimized bioinformatics pipeline, and rigorous premapping filters. The ddRADseq approach is useful for the construction of high-quality genetic maps in organisms lacking a reference genome if the parents and progeny are sequenced at sufficient depth. Technical improvements in reduced representation sequencing (RRS) approaches are needed to reduce the amount of missing data.

Indexed as

Sequence Analysis, DNAChromosome MappingDNA Restriction EnzymesGenotyping TechniquesPolymorphism, Single NucleotideQuercusDNA Restriction EnzymesddRADseqDense linkage mappingQuercus rubraSequencing depth

Identifiers

PMID28558688
PMCPMC5450186

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.