Charles Laurin

11.5k total citations · 2 hit papers
15 papers, 5.0k citations indexed

About

Charles Laurin is a scholar working on Genetics, Molecular Biology and Clinical Psychology. According to data from OpenAlex, Charles Laurin has authored 15 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Genetics, 2 papers in Molecular Biology and 2 papers in Clinical Psychology. Recurrent topics in Charles Laurin's work include Genetic Associations and Epidemiology (12 papers), Genetic and phenotypic traits in livestock (6 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Charles Laurin is often cited by papers focused on Genetic Associations and Epidemiology (12 papers), Genetic and phenotypic traits in livestock (6 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Charles Laurin collaborates with scholars based in United States, United Kingdom and Australia. Charles Laurin's co-authors include David M. Evans, Gibran Hemani, Tom R. Gaunt, Jie Zheng, Benjamin Elsworth, Philip Haycock, George Davey Smith, Caroline L. Relton, Valeriia Haberland and James Yarmolinsky and has published in prestigious journals such as Bioinformatics, PLoS ONE and Biological Psychiatry.

In The Last Decade

Charles Laurin

15 papers receiving 5.0k citations

Hit Papers

The MR-Base platform supports systematic causal inference... 2016 2026 2019 2022 2018 2016 1000 2.0k 3.0k 4.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The MR-Base platform supports systematic causal inference across the human phenome
    2018 4.4k citations Gibran Hemani, Jie Zheng et al. eLife profile →
  2. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis
    2016 406 citations Jie Zheng, A. Mesut Erzurumluoglu et al. Bioinformatics profile →

Peers

Charles Laurin
Denis Baird United Kingdom
Valeriia Haberland United Kingdom
Vanessa Y. Tan United Kingdom
Ryan Langdon United Kingdom
Benjamin Elsworth United Kingdom
Marie Verbanck France
Adrián Cortés United Kingdom
Fabiola Del Greco M Italy
Kaitlin H. Wade United Kingdom
Hashem A. Shihab United Kingdom
Denis Baird United Kingdom
Charles Laurin
Citations per year, relative to Charles Laurin Charles Laurin (= 1×) peers Denis Baird

Countries citing papers authored by Charles Laurin

Since Specialization
Citations

This map shows the geographic impact of Charles Laurin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charles Laurin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Laurin more than expected).

Fields of papers citing papers by Charles Laurin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles Laurin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charles Laurin. The network helps show where Charles Laurin may publish in the future.

Co-authorship network of co-authors of Charles Laurin

This figure shows the co-authorship network connecting the top 25 collaborators of Charles Laurin. A scholar is included among the top collaborators of Charles Laurin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charles Laurin. Charles Laurin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Hemani, Gibran, Jie Zheng, Benjamin Elsworth, et al.. (2018). The MR-Base platform supports systematic causal inference across the human phenome. eLife. 7. 4374 indexed citations breakdown →
2.
Cuéllar-Partida, Gabriel, Charles Laurin, Susan M. Ring, et al.. (2018). Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans. Human Molecular Genetics. 27(16). 2927–2939. 17 indexed citations
3.
Laurin, Charles, Gabriel Cuéllar-Partida, Gibran Hemani, et al.. (2017). Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices. Behavior Genetics. 48(1). 67–79. 4 indexed citations
4.
Zheng, Jie, Philip Haycock, Gibran Hemani, et al.. (2016). LD hub and MR-base: online platforms for preforming LD score regression and Mendelian randomization analysis using GWAS summary data. Behavior Genetics. 46(6). 815–815. 5 indexed citations
5.
Zheng, Jie, A. Mesut Erzurumluoglu, Benjamin Elsworth, et al.. (2016). LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 33(2). 272–279. 406 indexed citations breakdown →
6.
Zheng, Jie, Santiago Rodrı́guez, Charles Laurin, et al.. (2016). HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. Bioinformatics. 33(1). 79–86. 4 indexed citations
7.
Larsen, Sofus C., Lars Ängquist, Charles Laurin, et al.. (2016). Association between Maternal Fish Consumption and Gestational Weight Gain: Influence of Molecular Genetic Predisposition to Obesity. PLoS ONE. 11(3). e0150105–e0150105. 5 indexed citations
8.
Laurin, Charles, Dorret I. Boomsma, & Gitta H. Lubke. (2016). The use of vector bootstrapping to improve variable selection precision in Lasso models. Statistical Applications in Genetics and Molecular Biology. 15(4). 305–320. 27 indexed citations
9.
Laurin, Charles, Jouke‐Jan Hottenga, Gonneke Willemsen, Dorret I. Boomsma, & Gitta H. Lubke. (2015). Genetic Analyses Benefit From Using Less Heterogeneous Phenotypes: An Illustration With the Hospital Anxiety and Depression Scale (HADS). Genetic Epidemiology. 39(4). 317–324. 10 indexed citations
10.
Walters, Raymond K., Charles Laurin, & Gitta H. Lubke. (2014). Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models. Twin Research and Human Genetics. 17(4). 272–278. 3 indexed citations
11.
Walters, Raymond K., Charles Laurin, & Gitta H. Lubke. (2014). EpiPen: An R Package to Investigate Two-Locus Epistatic Models. Twin Research and Human Genetics. 17(4). 1 indexed citations
12.
Lubke, Gitta H., Charles Laurin, Najaf Amin, et al.. (2013). Genome-wide analyses of borderline personality features. Molecular Psychiatry. 19(8). 923–929. 42 indexed citations
13.
Lubke, Gitta H., Jouke‐Jan Hottenga, Raymond K. Walters, et al.. (2012). Estimating the Genetic Variance of Major Depressive Disorder Due to All Single Nucleotide Polymorphisms. Biological Psychiatry. 72(8). 707–709. 88 indexed citations
14.
Walters, Raymond K., Charles Laurin, & Gitta H. Lubke. (2012). An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data. Bioinformatics. 28(20). 2615–2623. 13 indexed citations
15.
Cornish, B. L., et al.. (1981). A CLASSIFICATION BASED ON THE ANALYSIS OF 131 CASES. 28 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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