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Items: 15

1.

A Simple Test Identifies Selection on Complex Traits.

Beissinger T, Kruppa J, Cavero D, Ha NT, Erbe M, Simianer H.

Genetics. 2018 May;209(1):321-333. doi: 10.1534/genetics.118.300857. Epub 2018 Mar 15.

PMID:
29545467
2.

The interplay of demography and selection during maize domestication and expansion.

Wang L, Beissinger TM, Lorant A, Ross-Ibarra C, Ross-Ibarra J, Hufford MB.

Genome Biol. 2017 Nov 13;18(1):215. doi: 10.1186/s13059-017-1346-4.

3.

Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution.

Beissinger TM, Morota G.

Plant Methods. 2017 Feb 23;13:8. doi: 10.1186/s13007-017-0159-5. eCollection 2017.

4.

Recent demography drives changes in linked selection across the maize genome.

Beissinger TM, Wang L, Crosby K, Durvasula A, Hufford MB, Ross-Ibarra J.

Nat Plants. 2016 Jun 13;2:16084. doi: 10.1038/nplants.2016.84.

PMID:
27294617
5.

MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken.

Morota G, Beissinger TM, Pe├▒agaricano F.

G3 (Bethesda). 2016 Aug 9;6(8):2447-53. doi: 10.1534/g3.116.031096.

6.

Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens.

Beissinger TM, Gholami M, Erbe M, Weigend S, Weigend A, de Leon N, Gianola D, Simianer H.

Heredity (Edinb). 2016 Feb;116(2):158-66. doi: 10.1038/hdy.2015.81. Epub 2015 Sep 9.

7.

Shared Genomic Regions Between Derivatives of a Large Segregating Population of Maize Identified Using Bulked Segregant Analysis Sequencing and Traditional Linkage Analysis.

Haase NJ, Beissinger T, Hirsch CN, Vaillancourt B, Deshpande S, Barry K, Buell CR, Kaeppler SM, de Leon N.

G3 (Bethesda). 2015 Jun 1;5(8):1593-602. doi: 10.1534/g3.115.017665.

8.

Defining window-boundaries for genomic analyses using smoothing spline techniques.

Beissinger TM, Rosa GJ, Kaeppler SM, Gianola D, de Leon N.

Genet Sel Evol. 2015 Apr 17;47:30. doi: 10.1186/s12711-015-0105-9.

9.

Selection for silage yield and composition did not affect genomic diversity within the Wisconsin Quality Synthetic maize population.

Lorenz AJ, Beissinger TM, Silva RR, de Leon N.

G3 (Bethesda). 2015 Feb 2;5(4):541-9. doi: 10.1534/g3.114.015263.

10.

Large effect QTL explain natural phenotypic variation for the developmental timing of vegetative phase change in maize (Zea mays L.).

Foerster JM, Beissinger T, de Leon N, Kaeppler S.

Theor Appl Genet. 2015 Mar;128(3):529-38. doi: 10.1007/s00122-014-2451-3. Epub 2015 Jan 10.

PMID:
25575839
11.

Insights into the effects of long-term artificial selection on seed size in maize.

Hirsch CN, Flint-Garcia SA, Beissinger TM, Eichten SR, Deshpande S, Barry K, McMullen MD, Holland JB, Buckler ES, Springer N, Buell CR, de Leon N, Kaeppler SM.

Genetics. 2014 Sep;198(1):409-21. doi: 10.1534/genetics.114.167155. Epub 2014 Jul 17.

12.

A genome-wide scan for evidence of selection in a maize population under long-term artificial selection for ear number.

Beissinger TM, Hirsch CN, Vaillancourt B, Deshpande S, Barry K, Buell CR, Kaeppler SM, Gianola D, de Leon N.

Genetics. 2014 Mar;196(3):829-40. doi: 10.1534/genetics.113.160655. Epub 2013 Dec 30.

13.

Marker density and read depth for genotyping populations using genotyping-by-sequencing.

Beissinger TM, Hirsch CN, Sekhon RS, Foerster JM, Johnson JM, Muttoni G, Vaillancourt B, Buell CR, Kaeppler SM, de Leon N.

Genetics. 2013 Apr;193(4):1073-81. doi: 10.1534/genetics.112.147710. Epub 2013 Feb 14.

14.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

Wu XL, Sun C, Beissinger TM, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D.

Genet Sel Evol. 2012 Sep 25;44:29. doi: 10.1186/1297-9686-44-29.

15.

A primer on high-throughput computing for genomic selection.

Wu XL, Beissinger TM, Bauck S, Woodward B, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D.

Front Genet. 2011 Feb 24;2:4. doi: 10.3389/fgene.2011.00004. eCollection 2011.

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