What is he best known for?
The fields of study he is best known for:
His main research concerns Quantitative trait locus, Genetics, Family-based QTL mapping, Bayes' theorem and Genetic architecture.
His Quantitative trait locus research includes elements of Epistasis, Linear model, Locus, Genetic variation and Inbred strain.
His work in the fields of Genetics, such as Inclusive composite interval mapping, Gene mapping, Genome and Allele, intersects with other areas such as Interval.
His Inclusive composite interval mapping research incorporates elements of Genetic model and Reversible-jump Markov chain Monte Carlo.
His Bayes' theorem research is multidisciplinary, relying on both Lasso, Prior probability and Model selection.
His studies in Lasso integrate themes in fields like Algorithm and Markov chain.
His most cited work include:
- Estimating polygenic effects using markers of the entire genome. (351 citations)
- Theoretical Basis of the Beavis Effect (328 citations)
- Bayesian LASSO for Quantitative Trait Loci Mapping (280 citations)
What are the main themes of his work throughout his whole career to date?
Quantitative trait locus, Genetics, Statistics, Family-based QTL mapping and Computational biology are his primary areas of study.
Shizhong Xu combines subjects such as Bayesian probability, Bayes' theorem, Inclusive composite interval mapping and Locus with his study of Quantitative trait locus.
His work is dedicated to discovering how Bayes' theorem, Linear model are connected with Generalized linear model and other disciplines.
His Statistics course of study focuses on Selection and Genetic gain.
His work carried out in the field of Family-based QTL mapping brings together such families of science as Evolutionary biology and Association mapping.
His research investigates the connection between Computational biology and topics such as Best linear unbiased prediction that intersect with problems in Biotechnology.
He most often published in these fields:
- Quantitative trait locus (64.80%)
- Genetics (51.40%)
- Statistics (24.58%)
What were the highlights of his more recent work (between 2017-2021)?
- Quantitative trait locus (64.80%)
- Mixed model (8.94%)
- Genome-wide association study (7.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Quantitative trait locus, Mixed model, Genome-wide association study, Computational biology and Genetic association.
His Quantitative trait locus study combines topics in areas such as Single-nucleotide polymorphism and Genetic variation.
His Single-nucleotide polymorphism study is concerned with Genetics in general.
His Genetics study frequently links to adjacent areas such as Selection.
His biological study spans a wide range of topics, including Data mining and Locus.
His Computational biology study incorporates themes from Phenotype, Gene, Best linear unbiased prediction and Predictability.
Between 2017 and 2021, his most popular works were:
- A multi-parent advanced generation inter-cross (MAGIC) population for genetic analysis and improvement of cowpea (Vigna unguiculata L. Walp.) (47 citations)
- Identification of QTL controlling domestication-related traits in cowpea (Vigna unguiculata L. Walp). (31 citations)
- Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population (30 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Quantitative trait locus, Genome-wide association study, Single-nucleotide polymorphism, Vigna and Biotechnology.
The concepts of his Genome-wide association study study are interwoven with issues in Statistics, Sample size determination and Wald test.
Single-nucleotide polymorphism is a subfield of Genetics that Shizhong Xu explores.
His Genetics study is mostly concerned with Genome, Synteny and Locus.
His study on Biotechnology also encompasses disciplines like
- Cultivar together with Genotype, Association mapping, Selection and Gene–environment interaction,
- Diallel cross which connect with Best linear unbiased prediction.
His study looks at the relationship between Best linear unbiased prediction and topics such as Population structure, which overlap with Mixed model, Genetic association, Statistical power and Computational biology.
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