Dr Seema Yadav
Dr

Seema Yadav

Email: 

Background

Seema Yadav is Postdoctoral Fellow with Centre for Animal Science at Queensland Alliance for Agriculture and Food Innovation. Her Ph.D. project was focused on implementing genomic selection to accelerate genetic gains in Australian sugarcane breeding programs. Before joining the UQ, she was working as an international consultant with the Quantitative Genetics cluster at the International rice research institute, Philippines. She has double master's degrees in Mathematics and Statistics. Her research interests include developing novel genomic prediction methods, specifically their ability to capture G x E interaction effects. She had deep interest in machine learning models and optimization techniques within this domain.

Availability

Dr Seema Yadav is:
Available for supervision

Qualifications

  • Doctor of Philosophy of Quantitative Genetics, The University of Queensland

Research interests

  • Quantitative genetics

  • Genomic prediction

  • Machine learning models

  • Operational research

Search Professor Seema Yadav’s works on UQ eSpace

10 works between 2020 and 2025
All (10) Journal Article (7) Other Outputs (2) Conference Publication (1)

1 - 10 of 10 works

2025

Journal Article

Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building

Yadav, S., Dillon, S., McNeil, M., Dinglasan, E., Mago, R., Dodds, P., Hickey, L. and Hayes, B. J. (2025). Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building. Theoretical and Applied Genetics, 138 (9) 242, 1-20. doi: 10.1007/s00122-025-05028-1

Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building

2025

Conference Publication

Metagenomic predictions for enteric methane emissions in sheep using long-read sequencing of rumen fluid samples

Li, Y., Nguyen, L.T., Ong, C.T., Yadav, S., Aldridge, M., Fitzgerald, P., van der Werf, J. and Ross, E.M. (2025). Metagenomic predictions for enteric methane emissions in sheep using long-read sequencing of rumen fluid samples. Association for the Advancement of Animal Breeding and Genetics (AAABG), Queenstown, New Zealand, 24-26 June 2025. Armidale, NSW, Australia: Association for the Advancement of Animal Breeding and Genetics.

Metagenomic predictions for enteric methane emissions in sheep using long-read sequencing of rumen fluid samples

2023

Journal Article

Use of continuous genotypes for genomic prediction in sugarcane

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Liu, Shouye, Nguyen, Loan To, Powell, Owen, Hickey, Lee T., Deomano, Emily, Atkin, Felicity, Voss‐Fels, Kai P. and Hayes, Ben J. (2023). Use of continuous genotypes for genomic prediction in sugarcane. The Plant Genome, 17 (1) e20417, e20417. doi: 10.1002/tpg2.20417

Use of continuous genotypes for genomic prediction in sugarcane

2023

Journal Article

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2023). Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies. Frontiers in Plant Science, 14 1260517, 1260517. doi: 10.3389/fpls.2023.1260517

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies

2023

Journal Article

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

Chen, Chensong, Powell, Owen, Dinglasan, Eric, Ross, Elizabeth M., Yadav, Seema, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben J. (2023). Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits. The Plant Genome, 16 (4) e20390, 1-13. doi: 10.1002/tpg2.20390

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

2023

Other Outputs

Optimising genomic selection for sugarcane

Yadav, Seema (2023). Optimising genomic selection for sugarcane. PhD Thesis, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland. doi: 10.14264/6d283df

Optimising genomic selection for sugarcane

2022

Other Outputs

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

Yadav, Seema, Ross, Elizabeth, Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2022). Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane. doi: 10.1101/2022.12.19.521119

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

2021

Journal Article

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

Yadav, Seema, Ross, Elizabeth M., Aitken, Karen S., Hickey, Lee T., Powell, Owen, Wei, Xianming, Voss-Fels, Kai P. and Hayes, Ben J. (2021). A linkage disequilibrium-based approach to position unmapped SNPs in crop species. BMC Genomics, 22 (1) 773, 1-9. doi: 10.1186/s12864-021-08116-w

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

2021

Journal Article

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

Yadav, Seema, Wei, Xianming, Joyce, Priya, Atkin, Felicity, Deomano, Emily, Sun, Yue, Nguyen, Loan T., Ross, Elizabeth M., Cavallaro, Tony, Aitken, Karen S., Hayes, Ben J. and Voss-Fels, Kai P. (2021). Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects. Theoretical and Applied Genetics, 134 (7), 2235-2252. doi: 10.1007/s00122-021-03822-1

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

2020

Journal Article

Accelerating genetic gain in sugarcane breeding using genomic selection

Yadav, Seema, Jackson, Phillip, Wei, Xianming, Ross, Elizabeth M., Aitken, Karen, Deomano, Emily, Atkin, Felicity, Hayes, Ben J. and Voss-Fels, Kai P. (2020). Accelerating genetic gain in sugarcane breeding using genomic selection. Agronomy, 10 (4) 585, 1-21. doi: 10.3390/agronomy10040585

Accelerating genetic gain in sugarcane breeding using genomic selection

Current funding

  • 2024 - 2029
    ARC Training Centre in Predictive Breeding for Agricultural Futures
    ARC Industrial Transformation Training Centres
    Open grant

Availability

Dr Seema Yadav is:
Available for supervision

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Supervision history

Current supervision

Enquiries

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