Publications

Genomic and phenomic prediction for soybean seed yield, protein, and oil

Liza Van der Laan,  Kyle Parmley,  Mojdeh Saadati,  Hernan Torres Pacin,  Srikanth Panthulugiri,  Soumik Sarkar,  Baskar Ganapathysubramanian,  Aaron Lorenz,  Asheesh K. Singh

The Plant Genome, 18, e70002. https://doi.org/10.1002/tpg2.70002

Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean.

Espina, M.J.C., Lovell, J.T., Jenkins, J., Shu, S., Sreedasyam, A., Jordan, B.D., Webber, J., Boston, L., Brůna, T., Talag, J., Goodstein, D., Grimwood, J., Stacey, G., Cannon, S.B., Lorenz, A.J., Schmutz, J. and Stupar, R.M. (2024)

Plant J, 120: 1221-1235. https://doi.org/10.1111/tpj.17026

Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions

Vitale P, Laidò G, Dono G, Pecorella I, Ramasubramanian V, et al. (2024) 

PLOS ONE 19(11): e0310886. https://doi.org/10.1371/journal.pone.0310886

High-throughput phenotyping of soybean (Glycine max) transpiration response curves to rising atmospheric drying in a mapping population. 

Monnens D, López JR, McCoy E, Tamang BG, Lorenz AJ, Sadok W. 

Funct Plant Biol. 2024 Nov;51:FP23281. doi: 10.1071/FP23281. PMID: 39570732.

Soybean genomics research community strategic plan: A vision for 2024–2028.

Stupar, R. M., Locke, A. M., Allen, D. K., Stacey, M. G., Ma, J., Weiss, J., Nelson, R. T., Hudson, M. E., Joshi, T., Li, Z., Song, Q., Jedlicka, J. R., MacIntosh, G. C., Grant, D., Parrott, W. A., Clemente, T. E., Stacey, G., An, Y.-C., Aponte-Rivera, J., … O'Rourke, J. A. (2024). 

The Plant Genome, 17, e20516. https://doi.org/10.1002/tpg2.20516

Genomic predictions of genetic variances and correlations among traits for breeding crosses in soybean. 

Wartha, C.A., Lorenz, A.J. 

Heredity 133, 173–185 (2024). 

Variation in shoot architecture traits and their relationship to canopy coverage and light interception in soybean (Glycine max). 

Sreekanta, S., Haaning, A., Dobbels, A. et al. 

BMC Plant Biol 24, 194 (2024). 

A genomic analysis of the University of Nebraska Replicated Recurrent Selection program.

Lamkey, C.M., Lorenz, A.J. 

Theor Appl Genet 136, 243 (2023). https://doi.org/10.1007/s00122-023-04475-y

Genomic predictions of genetic variances and correlations among traits for breeding crosses in soybean. 

Wartha CA, Lorenz AJ. 

Heredity (Edinb). 2024 Sep;133(3):173-185. 

A genome-wide analysis of the USDA Soybean Isoline Collection

Gilbert, E., Merry, R., Campbell, B.W., Stupar, R.M., Lorenz, A.J.

Plant Genome, 2023, 16(2), e20310

Branch angle and leaflet shape are associated with canopy coverage in soybean

Virdi, K.S., Sreekanta, S., Dobbels, A., ...Lorenz, A.J., Muehlbauer, G.J.

Plant Genome, 2023, 16(2), e20304

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

Bandillo, N.B., Jarquin, D., Posadas, L.G., Lorenz, A.J., Graef, G.L.

Plant Genome, 2023, 16(1), e20285

Development of molecular inversion probes for soybean progeny genomic selection genotyping

Wang, H., Campbell, B., Happ, M., ...Pantalone, V., Hyten, D.

Plant Genome, 2023, 16(1), e20270

Virulence diversity of soybean cyst nematode in Minnesota.

Docherty, Lauren, A. Lorenz, and S. Chen. 

 JOURNAL OF NEMATOLOGY. Vol. 55. No. 1., 2023.

Evaluation of soybean accessions for resistance to the Japanese beetle

James P Menger, Siddhi Bhusal, Aaron J Lorenz, Robert L Koch,, 2023, 

Arthropod Management Tests, Volume 48, Issue 1, 2023, tsad123, https://doi.org/10.1093/amt/tsad123

Development of a controlled-environment assay to induce iron deficiency chlorosis in soybean by adjusting calcium carbonates, pH, and nodulation

Merry, R., Espina, M.J., Lorenz, A.J., Stupar, R.M.

Plant Methods, 2022, 18(1), 36

Erratum to: Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds (Crop Science, (2020), 60, 1, (62-81), 10.1002/csc2.20035) 

Falcon, C.M., Kaeppler, S.M., Spalding, E.P., ...Xu, W., de Leon, N.

Crop Science, 2022, 62(6), pp. 2568–2569

Candidate Genes Modulating Reproductive Timing in Elite US Soybean Lines Identified in Soybean Alleles of Arabidopsis Flowering Orthologs With Divergent Latitude Distribution

Dietz, N., Chan, Y.O., Scaboo, A., ...Joshi, T., Bilyeu, K.

Frontiers in Plant Science, 2022, 13, 889066

Plant Breeding for Intercropping in Temperate Field Crop Systems: A Review

Moore, V.M., Schlautman, B., Fei, S.-Z., ...Wells, S., Lorenz, A.J.

Frontiers in Plant Science, 2022, 13, 843065

Iron deficiency in soybean

Merry, R., Dobbels, A.A., Sadok, W., ...Stupar, R.M., Lorenz, A.J.

Crop Science, 2022, 62(1), pp. 36–52

Registration of M10-207102 soybean germplasm: A high-yielding, early-maturity line with elevated protein

Bhusal, S.J., Orf, J., Lorenz, A.J.

Journal of Plant Registrations, 2022, 16(1), pp. 132–136

Correction to: Characterizing introgression‑by‑environment interactions using maize near isogenic lines (Theoretical and Applied Genetics, (2020), 133, 10, (2761-2773), 10.1007/s00122-020-03630-z)

Li, Z., Tirado, S.B., Kadam, D.C., ...Springer, N.M., Hirsch, C.N.

Theoretical and Applied Genetics, 2021, 134(12), pp. 4077

Mining fiskeby iii and mandarin (Ottawa) expression profiles to understand iron stress tolerant responses in soybean

O’rourke, J.A., Morrisey, M.J., Merry, R., ...Stupar, R.M., Graham, M.A.

International Journal of Molecular Sciences, 2021, 22(20), 11032

The utility of genomic prediction models in evolutionary genetics

McGaugh, S.E., Lorenz, A.J., Flagel, L.E.

Proceedings of the Royal Society B: Biological Sciences, 2021, 288(1956), 20210693

Variation in Soybean Aphid (Hemiptera: Aphididae) Biotypes within Fields

Bhusal, S.J., Koch, R.L., Lorenz, A.J.

Journal of Economic Entomology, 2021, 114(3), pp. 1336–1344

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

Jarquin, D., de Leon, N., Romay, C., ...Yu, J., Lorenz, A.

Frontiers in Genetics, 2021, 11, 592769

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

Rogers, A.R., Dunne, J.C., Romay, C., ...De Leon, N., Holland, J.B.

G3: Genes, Genomes, Genetics, 2021, 11(2), jkaa050

Optimization of training sets for genomic prediction of early-stage single crosses in maize

Kadam, D.C., Rodriguez, O.R., Lorenz, A.J.

Theoretical and Applied Genetics, 2021, 134(2), pp. 687–699

Optimization of temporal UAS-based imagery analysis to estimate plant maturity date for soybean breeding

Volpato, L., Dobbels, A., Borem, A., Lorenz, A.J.

Plant Phenome Journal, 2021, 4(1), e20018

Characterizing introgression-by-environment interactions using maize near isogenic lines

Li, Z., Tirado, S.B., Kadam, D.C., ...Springer, N.M., Hirsch, C.N.

Theoretical and Applied Genetics, 2020, 133(10), pp. 2761–2773

Registration of ‘NE10589’ (Husker Genetics Brand Ruth) hard red winter wheat

Baenziger, P.S., Graybosch, R.A., Rose, D.J., ...El-Basyoni, I., Lorenz, A.

Journal of Plant Registrations, 2020, 14(3), pp. 388–397

Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation

Ravelombola, W.S., Qin, J., Shi, A., ...Young, N.D., Chen, S.

PLoS ONE, 2020, 15(7 July), e0235089

Dominance effects and functional enrichments improve prediction of agronomic traits in hybrid maize

Ramstein, G.P., Larsson, S.J., Cook, J.P., ...Buckler, E.S., Cinta Romay, M.

Genetics, 2020, 215(1), pp. 215–230

Belowground microbial communities respond to water deficit and are shaped by decades of maize hybrid breeding

Wang, P., Marsh, E.L., Kruger, G., Lorenz, A., Schachtman, D.P.

Environmental Microbiology, 2020, 22(3), pp. 889–904

Maize genomes to fields (G2F): 2014-2017 field seasons: Genotype, phenotype, climatic, soil, and inbred ear image datasets

McFarland, B.A., Alkhalifah, N., Bohn, M., ...Yeh, C.-T., De Leon, N.

BMC Research Notes, 2020, 13(1), 71

Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds

Falcon, C.M., Kaeppler, S.M., Spalding, E.P., ...Xu, W., de Leon, N.

Crop Science, 2020, 60(1), pp. 62–81

Genome-wide association study and genomic selection for soybean chlorophyll content associated with soybean cyst nematode tolerance

Ravelombola, W.S., Qin, J., Shi, A., ...Young, N.D., Chen, S.

BMC Genomics, 2019, 20(1), 904

Identification and fine-mapping of a soybean quantitative trait locus on chromosome 5 conferring tolerance to iron deficiency chlorosis

Merry, R., Butenhoff, K., Campbell, B.W., ...Lorenz, A.J., Stupar, R.M.

Plant Genome, 2019, 12(3), 190007

Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

(Plant Methods (2019) 15:97 DOI: 10.1186/s13007-019-0478-9)

Dobbels, A.A., Lorenz, A.J. Plant Methods, 2019, 15(1), 113

Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses

Neyhart, J.L., Lorenz, A.J., Smith, K.P.

G3: Genes, Genomes, Genetics, 2019, 9(10), pp. 3153–3165

Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

Dobbels, A.A., Lorenz, A.J. 

Plant Methods, 2019, 15(1), 97

Evaluation of nonparametric models for genomic prediction of early-stage single crosses in maize

Kadam, D.C., Lorenz, A.J. Crop Science, 2019, 59(4), pp. 1411–1423

Environmental stability study of soybeans with modified carbohydrate profiles in maturity groups 0 to v

Jo, H., Lorenz, A.J., Rainey, K.M., ...Chen, P., Bilyeu, K.D.

Crop Science, 2019, 59(4), pp. 1531–1543

Tx741, Tx777, Tx779, Tx780, and Tx782 inbred maize lines for yield and southern united states stress adaptation

Murray, S.C., Mayfield, K., Pekar, J., ...Xu, W., Betran, J.

Journal of Plant Registrations, 2019, 13(2), pp. 258–269

Genomic Dissection of Nonhost Resistance to Wheat Stem Rust in Brachypodium distachyon

Coletta, R.D., Hirsch, C.N., Rouse, M.N., Lorenz, A., Garvin, D.F.

Molecular Plant-Microbe Interactions, 2019, 32(4), pp. 392–400

Genome-wide association and gene co-expression network analyses reveal complex genetics of resistance to Goss’s Wilt of Maize

Singh, A., Li, G., Brohammer, A.B., ...Alfano, J.R., Lorenz, A.J.

G3: Genes, Genomes, Genetics, 2019, 9(10), pp. 3139–3152

Response surface analysis of genomic prediction accuracy values using quality control covariates in soybean

Jarquín, D., Howard, R., Graef, G., Lorenz, A.

Evolutionary Bioinformatics, 2019, 15

Genomic selection in preliminary yield trials in a winter wheat breeding program

Belamkar, V., Guttieri, M.J., Hussain, W., ...Lorenz, A.J., Baenziger, P.S.

G3: Genes, Genomes, Genetics, 2018, 8(8), pp. 2735–2747

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Alkhalifah, N., Campbell, D.A., Falcon, C.M., ...Edwards, J., Lawrence-Dill, C.J.

BMC Research Notes, 2018, 11(1), 452

A comparison between genotyping-by-sequencing and array-based scoring of SNPs for genomic prediction accuracy in winter wheat

Elbasyoni, I.S., Lorenz, A.J., Guttieri, M., ...Poland, J., Akhunov, E.

Plant Science, 2018, 270, pp. 123–130

Changes in dynamic leaf traits in maize associated with year of hybrid release

Luetchens, J., Lorenz, A.J.

Crop Science, 2018, 58(2), pp. 551–563

Genome-wide analysis of grain yield stability and environmental interactions in a multiparental soybean population

Xavier, A., Jarquin, D., Howard, R., ...Muir, W.M., Rainey, K.M.

G3: Genes, Genomes, Genetics, 2018, 8(2), pp. 519–529

Genome-wide association mapping of host-plant resistance to soybean aphid

Hanson, A.A., Lorenz, A.J., Hesler, L.S., ...Jiang, G.-L., Koch, R.L.

Plant Genome, 2018, 11, 180011

Dissecting the Genetic Basis of Local Adaptation in Soybean

Bandillo, N.B., Anderson, J.E., Kantar, M.B., ...Graef, G.L., Lorenz, A.J.

Scientific Reports, 2017, 7(1), 17195

The effect of artificial selection on phenotypic plasticity in maize

Gage, J.L., Jarquin, D., Romay, C., ...Yu, J., De Leon, N.

Nature Communications, 2017, 8(1), 1348

Erratum: Genomic prediction of single crosses in the early stages of a Maize hybrid breeding pipeline [G3 Genes-Genomes-Genetics, 6, (3443-3453)] DOI: 10.1534/g3.116.031286

Kadam, D.C., Potts, S.M., Bohn, M.O., Lipka, A.F., Lorenz, A.J.

G3: Genes, Genomes, Genetics, 2017, 7(10), pp. 3557–3558

Leveraging genomic prediction to scan germplasm collection for crop improvement

De Azevedo Peixoto, L., Moellers, T.C., Zhang, J., ...Beavis, W.D., Singh, A.K.

PLoS ONE, 2017, 12(6), e0179191

Evaluating methods of updating training data in long-term genomewide selection

Neyhart, J.L., Tiede, T., Lorenz, A.J., Smith, K.P.

G3: Genes, Genomes, Genetics, 2017, 7(5), pp. 1499–1510

Training population design and resource allocation for genomic selection in plant breeding

Lorenz, A., Nice, L.

Genomic Selection for Crop Improvement: New Molecular Breeding Strategies for Crop Improvement, 2017, pp. 7–22

Genome-wide association mapping of qualitatively inherited traits in a germplasm collection

Bandillo, N.B., Lorenz, A.J., Graef, G.L., ...Nelson, R.L., Specht, J.E.

Plant Genome, 2017, 10(2)

Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content

Campbell, M.T., Bandillo, N., Al Shiblawi, F.R.A., ...Lorenz, A.J., Walia, H.

PLoS Genetics, 2017, 13(6), e1006823

Genome-enabled prediction models for yield related traits in chickpea

Roorkiwal, M., Rathore, A., Das, R.R., ...Jannink, J.-L., Varshney, R.K.

Frontiers in Plant Science, 2016, 7(NOVEMBER2016), 1666

Mapping quantitative trait loci for resistance to goss’s bacterial wilt and leaf blight in north american maize by joint linkage analysis

Singh, A., Andersen, A.P., Jackson-Ziems, T.A., Lorenz, A.J.

Crop Science, 2016, 56(5), pp. 2306–2313

Genomic prediction of single crosses in the early stages of a maize hybrid breeding pipeline

Kadam, D.C., Potts, S.M., Bohn, M.O., Lipka, A.E., Lorenz, A.J.

G3: Genes, Genomes, Genetics, 2016, 6(11), pp. 3443–3453

Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

Jarquin, D., Specht, J., Lorenz, A.

G3: Genes, Genomes, Genetics, 2016, 6(8), pp. 2329–2341

Greenhouse screening of maize genotypes for deep root mass and related root traits and their association with grain yield under water-deficit conditions in the field

Ali, M.L., Luetchens, J., Singh, A., ...Kruger, G.R., Lorenz, A.J.

Euphytica, 2016, 207(1), pp. 79–94

Genetic variation in seminal and nodal root angle and their association with grain yield of maize under water-stressed field conditions

Ali, M.L., Luetchens, J., Nascimento, J., ...Kruger, G.R., Lorenz, A.J.

Plant and Soil, 2015, 397(1-2), pp. 213–225

A population structure and genome-wide association analysis on the USDA soybean germplasm collection

Bandillo, N., Jarquin, D., Song, Q., ...Specht, J., Lorenz, A.

Plant Genome, 2015, 8(3)

Adding genetically distant individuals to training populations reduces genomic prediction accuracy in Barley

Lorenz, A.J., Smith, K.P.

Crop Science, 2015, 55(6), pp. 2657–2667

Environmental Reviews and Case Studies: Bringing Unmanned Aerial Systems Closer to the Environment

Detweiler, C., Ore, J.-P., Anthony, D., ...Burgin, A., Lorenz, A.

Environmental Practice, 2015, 17(3), pp. 188–200

Characterization of Stem Rust Resistance in Wheat Cultivar Gage

Kumssa, T.T., Baenziger, P.S., Rouse, M.N., ...Lorenz, A.J., Poland, J.

Crop Science, 2015, 55(1), pp. 229–239

Screening genetic variation in maize for deep root mass in greenhouse and its association with grain yield under water-stressed field conditions

Liakat Ali, M., Luetchens, J., Singh, A., ...Kruger, G.R., Lorenz, A.J.

Maydica, 2015, 60(1), M2

Selection for silage yield and composition did not affect genomic diversity within the wisconsin quality synthetic maize population

Lorenz, A.J., Beissinger, T.M., Silva, R.R., de Leon, N.

G3: Genes, Genomes, Genetics, 2015, 5(4), pp. 541–549

On crop height estimation with UAVs

Anthony, D., Elbaum, S., Lorenz, A., Detweiler, C.

IEEE International Conference on Intelligent Robots and Systems, 2014, pp. 4805–4812, 6943245

Genotyping by sequencing for genomic prediction in a soybean breeding population

Jarquín, D., Kocak, K., Posadas, L., ...Graef, G., Lorenz, A.

BMC Genomics, 2014, 15(1), 740

Relative effect of drift and selection in diverging populations within a reciprocal recurrent selection program

Lamkey, C.M., Lorenz, A.J.

Crop Science, 2014, 54(2), pp. 576–585

Bridging conventional breeding and genomics for a more sustainable wheat production

Baenziger, P.S., Bakhsh, A., Lorenz, A., Walia, H.

Genomics of Plant Genetic Resources, 2014, 2, pp. 185–209

The phylogenetic relationships of US maize germplasm

Lorenz, A., Hoegemeyer, T.

Nature Genetics, 2013, 45(8), pp. 844–845

Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: A simulation experiment

Lorenz, A.J.

G3: Genes, Genomes, Genetics, 2013, 3(3), pp. 481–491

Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley

Lorenz, A.J., Smith, K.P., Jannink, J.-L.

Crop Science, 2012, 52(4), pp. 1609–1621

Association mapping and gene-gene interaction for stem rust resistance in CIMMYT spring wheat germplasm

Yu, L.-X., Lorenz, A., Rutkoski, J., ...Huerta-Espino, J., Sorrells, M.E.

Theoretical and Applied Genetics, 2011, 123(8), pp. 1257–1268

Genomic Selection in Plant Breeding. Knowledge and Prospects.

Lorenz, A.J., Chao, S., Asoro, F.G., ...Sorrells, M.E., Jannink, J.-L.

Advances in Agronomy, 2011, 110(C), pp. 77–123

Performance of single nucleotide polymorphisms versus haplotypes for genome-wide association analysis in barley

Lorenz, A.J., Hamblin, M.T., Jannink, J.-L.

PLoS ONE, 2010, 5(11), e14079

Plant breeding with Genomic selection: Gain per unit time and cost

Heffner, E.L., Lorenz, A.J., Jannink, J.-L., Sorrells, M.E.

Crop Science, 2010, 50(5), pp. 1681–1690

Erratum to Cell wall composition and ruminant digestibility of various maize tissues across development (Bioenerg. Res., (2010) 3, (28-37), 10.1007/s12155-009-9068-4)

Hansey, C.N., Lorenz, A.J., de Leon, N.

Bioenergy Research, 2010, 3(3), pp. 295–304

Genetic analysis of cell wall traits relevant to cellulosic ethanol production in maize (Zea mays L.)

Lorenz, A.J., Coors, J.G., Hansey, C.N., Kaeppler, S.M., de Leon, N.

Crop Science, 2010, 50(3), pp. 842–852

Genomic selection in plant breeding: From theory to practice

Jannink, J.-L., Lorenz, A.J., Iwata, H.

Briefings in Functional Genomics and Proteomics, 2010, 9(2), pp. 166–177, elq001

Cell wall composition and ruminant digestibility of various maize tissues across development

Hansey, C.N., Lorenz, A.J., de Leon, N.

Bioenergy Research, 2010, 3(1), pp. 28–37

Breeding maize for a bioeconomy: A literature survey examining harvest index and stover yield and their relationship to grain yield

Lorenz, A.J., Gustafson, T.J., Coors, J.G., de Leon, N.

Crop Science, 2010, 50(1), pp. 1–12

Correlating detergent fiber analysis and dietary fiber analysis data for corn stover collected by NIRS

Wolfrum, E.J., Lorenz, A.J., deLeon, N.

Cellulose, 2009, 16(4), pp. 577–585

Forage quality and composition measurements as predictors of ethanol yield from maize (Zea mays L.) stover

Lorenz, A.J., Anex, R.P., Isci, A., ...de Leon, N., Weimer, P.J.

Biotechnology for Biofuels, 2009, 2, 5

Characterization, genetic variation, and combining ability of maize traits relevant to the production of cellulosic ethanol

Lorenz, A.J., Coors, J.G., De Leon, N., ...Sluiter, A.D., Weimer, P.J.

Crop Science, 2009, 49(1), pp. 85–98

Development of a fluorescence-based method for monitoring glucose catabolism and its potential use in a biomass hydrolysis assay

Haney, L.J., Coors, J.G., Lorenz, A.J., ...Anex, R.P., Paul, M.P.

Biotechnology for Biofuels, 2008, 1, 17

What can be learned from silage breeding programs?

Lorenz, A.J., Coors, J.G.

Applied Biochemistry and Biotechnology, 2008, 148(1-3), pp. 261–270

Genetic variation and breeding potential of phytate and inorganic phosphorus in a maize population

Lorenz, A.J., Scott, M.P., Lamkey, K.R.

Crop Science, 2008, 48(1), pp. 79–84

Quantitative determination of phytate and inorganic phosphorus for maize breeding

Lorenz, A.J., Scott, M.P., Lamkey, K.R.

Crop Science, 2007, 47(2), pp. 600–606