| MegaLMM: mega-scale linear mixed models for genomic predictions with thousands of traits DE Runcie, J Qu, H Cheng, L Crawford Genome biology 22, 1-25, 2021 | 41 | 2021 |
| Exact distribution of linkage disequilibrium in the presence of mutation, selection, or minor allele frequency filtering J Qu, SD Kachman, D Garrick, RL Fernando, H Cheng Frontiers in genetics 11, 362, 2020 | 12 | 2020 |
| A goal programming approach for balancing diet costs and feed water use under different environmental conditions J Qu, TC Hsiao, EJ DePeters, D Zaccaria, RL Snyder, JG Fadel Journal of dairy science 102 (12), 11504-11522, 2019 | 10 | 2019 |
| JWAS version 2: leveraging biological information and highthroughput phenotypes into genomic prediction and association H Cheng, R Fernando, D Garrick, T Zhao, J Qu Proceedings of 12th World Congress on Genetics Applied to Livestock …, 2022 | 4 | 2022 |
| Supplementary Note to “MegaLMM: Mega-scale linear mixed models for multi-trait genomic prediction” D Runcie, J Qu, H Cheng, L Crawford | 1 | 2021 |
| Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits J Qu, D Runcie, H Cheng Genetics 223 (3), iyac183, 2023 | | 2023 |
| Mega-scale mixed models for genome-wide prediction with thousands of high-throughput phenotyping traits J Qu, DE Runcie, H Cheng Proceedings of 12th World Congress on Genetics Applied to Livestock …, 2022 | | 2022 |
| A Bayesian random regression method using mixture priors for genome‐enabled analysis of time‐series high‐throughput phenotyping data J Qu, G Morota, H Cheng The Plant Genome 15 (3), e20228, 2022 | | 2022 |
| MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits J Qu, D Runcie, H Cheng bioRxiv, 2022.05. 06.490983, 2022 | | 2022 |
Hao ChengUniversity of California, DavisVerified email at ucdavis.edu
