Leilei Cui is an Assistant Researcher at the School of Life Sciences, Nanchang University. He received his Ph.D. from Jiangxi Agricultural University (National Key Laboratory for Pig Genetic Improvement and Germplasm Innovation) under the supervision of Academician Lusheng Huang. He conducted postdoctoral research and participated in a joint Ph.D. training program at University College London (UCL), and was a visiting scholar at the Center for Quantitative Genetics and Genomics, Aarhus University, Denmark.

Dr. Cui is a member of the Chinese-American Association of Geneticists and the International Consortium of Seed Scientists. He serves as a reviewer for several international journals, including Ageing Cell, Bioinformatics, Advanced Biology, Scientific Reports, Bioinformatics Advances, BMC Genomics, and BioData Mining. To date, he has published 16 research articles in leading peer-reviewed journals such as Nature Genetics, Genome Biology, Science China-Life Sciences, Bioinformatics, and Genetics Selection Evolution, including six as first or co-first author.

He has led two national-level projects—a sub-project of the National Key R&D Program and a Young Scientists Fund from the National Natural Science Foundation of China—as well as one provincial-level project, with total research funding exceeding 3.6 million RMB.

His research focuses on quantitative genetics and bioinformatics, with an emphasis on developing novel GWAS models and computational tools—such as for identifying sex-specific genetic loci (gene-by-sex interactions) and dominant/overdominant loci (allelic interactions). He also works on mitochondrial genetics in mammals and applies these approaches to diverse populations (human, rat, mouse, and pig) to dissect the genetic architecture of complex traits including growth, obesity, bone quality, immunity, hematological parameters, and anxiety. Dr. Cui is experienced in multi-omics data analysis (genomics, transcriptomics, full-length transcriptomics, microbiome) and skilled in the full spectrum of complex trait genetic analysis: variant detection, phenotype-genotype association analysis, QTL fine-mapping, causal variant filtering and verification, and molecular mechanism exploration.