Martin Bohn
Overview
Explore the profile of Martin Bohn including associated specialties, affiliations and a list of published articles.
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15
Citations
263
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Recent Articles
1.
Lipps S, Bohn M, Rutkoski J, Butts-Wilmsmeyer C, Mideros S, Jamann T
Mol Plant Microbe Interact
. 2024 Dec;
PMID: 39700336
is one of the most important plant-pathogenic fungi that causes disease on wheat and maize, as it decreases yield in both crops and produces mycotoxins that pose a risk to...
2.
Lima D, Aviles A, Alpers R, Perkins A, Schoemaker D, Costa M, et al.
BMC Res Notes
. 2023 Sep;
16(1):219.
PMID: 37710302
Objectives: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and...
3.
Lima D, Washburn J, Varela J, Chen Q, Gage J, Romay M, et al.
BMC Res Notes
. 2023 Jul;
16(1):148.
PMID: 37461058
Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials,...
4.
Lima D, Aviles A, Alpers R, McFarland B, Kaeppler S, Ertl D, et al.
BMC Genom Data
. 2023 May;
24(1):29.
PMID: 37231352
Objectives: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative...
5.
Jarquin D, de Leon N, Romay C, Bohn M, Buckler E, Ciampitti I, et al.
Front Genet
. 2021 Mar;
11:592769.
PMID: 33763106
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt...
6.
Rogers A, Dunne J, Romay C, Bohn M, Buckler E, Ciampitti I, et al.
G3 (Bethesda)
. 2021 Feb;
11(2).
PMID: 33585867
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide...
7.
McFarland B, AlKhalifah N, Bohn M, Bubert J, Buckler E, Ciampitti I, et al.
BMC Res Notes
. 2020 Feb;
13(1):71.
PMID: 32051026
Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields...
8.
Vanous A, Gardner C, Blanco M, Martin-Schwarze A, Wang J, Li X, et al.
Plant Genome
. 2019 Apr;
12(1).
PMID: 30951103
Variation in kernel composition across maize ( L.) germplasm is affected by a combination of the plant's genotype, the environment in which it is grown, and the interaction between these...
9.
Shenstone E, Cooper J, Rice B, Bohn M, Jamann T, Lipka A
PLoS One
. 2018 Nov;
13(11):e0207752.
PMID: 30462727
The logistic mixed model (LMM) is well-suited for the genome-wide association study (GWAS) of binary agronomic traits because it can include fixed and random effects that account for spurious associations....
10.
Vanous A, Gardner C, Blanco M, Martin-Schwarze A, Lipka A, Flint-Garcia S, et al.
Plant Genome
. 2018 Jul;
11(2).
PMID: 30025021
Flowering and height related traits are extensively studied in maize for three main reasons: 1) easily obtained phenotypic measurements, 2) highly heritable, and 3) importance of these traits to adaptation...