Michael E. Goddard

98.7k total citations · 20 hit papers
439 papers, 43.6k citations indexed

About

Michael E. Goddard is a scholar working on Genetics, Plant Science and Agronomy and Crop Science. According to data from OpenAlex, Michael E. Goddard has authored 439 papers receiving a total of 43.6k indexed citations (citations by other indexed papers that have themselves been cited), including 370 papers in Genetics, 115 papers in Plant Science and 64 papers in Agronomy and Crop Science. Recurrent topics in Michael E. Goddard's work include Genetic and phenotypic traits in livestock (330 papers), Genetic Mapping and Diversity in Plants and Animals (246 papers) and Genetics and Plant Breeding (91 papers). Michael E. Goddard is often cited by papers focused on Genetic and phenotypic traits in livestock (330 papers), Genetic Mapping and Diversity in Plants and Animals (246 papers) and Genetics and Plant Breeding (91 papers). Michael E. Goddard collaborates with scholars based in Australia, United States and United Kingdom. Michael E. Goddard's co-authors include Ben J. Hayes, Peter M. Visscher, T.H.E. Meuwissen, Jian Yang, Sang Lee, Naomi R. Wray, P.J. Bowman, Amanda J. Chamberlain, Grant W. Montgomery and J.E. Pryce and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Circulation.

In The Last Decade

Michael E. Goddard

426 papers receiving 42.5k citations

Hit Papers

Prediction of Total Genetic Value Using Genome-Wide Dense... 2001 2026 2009 2017 2001 2010 2010 2016 2009 1000 2.0k 3.0k 4.0k 5.0k
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. Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
    2001 5.4k citations Ben J. Hayes, Michael E. Goddard et al. profile →
  2. GCTA: A Tool for Genome-wide Complex Trait Analysis
    2010 4.6k citations Michael E. Goddard, Peter M. Visscher et al. profile →
  3. Common SNPs explain a large proportion of the heritability for human height
    2010 2.8k citations Michael E. Goddard, Peter M. Visscher et al. Nature Genetics profile →
  4. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
    2016 1.6k citations Michael E. Goddard, Naomi R. Wray et al. Nature Genetics profile →
  5. Invited review: Genomic selection in dairy cattle: Progress and challenges
    2009 1.3k citations Ben J. Hayes, P.J. Bowman et al. Journal of Dairy Science profile →
  6. Genomic selection: prediction of accuracy and maximisation of long term response
    2008 811 citations Michael E. Goddard profile →
  7. Mapping genes for complex traits in domestic animals and their use in breeding programmes
    2009 762 citations Michael E. Goddard, Ben J. Hayes profile →
  8. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits
    2012 736 citations Michael E. Goddard, Peter M. Visscher et al. Nature Genetics profile →
  9. Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits
    2008 713 citations Michael E. Goddard, Peter M. Visscher et al. profile →
  10. Estimating Missing Heritability for Disease from Genome-wide Association Studies
    2011 645 citations Naomi R. Wray, Michael E. Goddard et al. profile →
  11. Advantages and pitfalls in the application of mixed-model association methods
    2014 614 citations Michael E. Goddard, Peter M. Visscher et al. Nature Genetics profile →
  12. Genomic selection
    2007 502 citations Michael E. Goddard, Ben J. Hayes profile →
  13. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels
    2012 474 citations Malena Erbe, Ben J. Hayes et al. Journal of Dairy Science profile →
  14. Increased accuracy of artificial selection by using the realized relationship matrix
    2009 473 citations Ben J. Hayes, Peter M. Visscher et al. profile →
  15. Pitfalls of predicting complex traits from SNPs
    2013 465 citations Naomi R. Wray, Ben J. Hayes et al. profile →
  16. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs
    2012 381 citations Michael E. Goddard, Peter M. Visscher et al. Nature Genetics profile →
  17. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
    2012 378 citations Michael E. Goddard, Peter M. Visscher et al. profile →
  18. Genomic selection: A paradigm shift in animal breeding
    2016 339 citations Ben J. Hayes, Michael E. Goddard et al. profile →
  19. Concepts, estimation and interpretation of SNP-based heritability
    2017 267 citations Jian Zeng, Michael E. Goddard et al. Nature Genetics profile →
  20. Improved polygenic prediction by Bayesian multiple regression on summary statistics
    2019 263 citations Jian Zeng, Kathryn E. Kemper et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael E. Goddard Australia 92 34.8k 12.0k 6.9k 4.8k 3.5k 439 43.6k
Ben J. Hayes Australia 79 22.9k 0.7× 11.1k 0.9× 3.3k 0.5× 4.6k 0.9× 2.7k 0.8× 369 28.0k
Peter M. Visscher Australia 95 30.7k 0.9× 4.9k 0.4× 12.5k 1.8× 1.5k 0.3× 2.7k 0.8× 453 47.3k
Leif Andersson Sweden 79 13.8k 0.4× 2.8k 0.2× 6.9k 1.0× 954 0.2× 1.5k 0.4× 377 23.4k
Matthew Stephens United States 66 43.7k 1.3× 15.7k 1.3× 21.8k 3.2× 1.6k 0.3× 2.7k 0.8× 132 75.0k
Chris Haley United Kingdom 59 11.9k 0.3× 4.5k 0.4× 3.0k 0.4× 940 0.2× 1.0k 0.3× 348 15.8k
Shaun Purcell United States 53 22.3k 0.6× 3.3k 0.3× 11.7k 1.7× 947 0.2× 2.7k 0.8× 136 39.1k
Jonathan K. Pritchard United States 92 47.6k 1.4× 15.7k 1.3× 24.9k 3.6× 1.3k 0.3× 2.8k 0.8× 182 77.5k
Eric Banks United States 20 18.3k 0.5× 7.9k 0.7× 18.6k 2.7× 824 0.2× 5.3k 1.5× 28 40.4k
Julian Maller United States 14 17.9k 0.5× 3.2k 0.3× 11.0k 1.6× 972 0.2× 2.7k 0.8× 15 35.6k
William G. Hill United Kingdom 69 16.9k 0.5× 5.0k 0.4× 3.6k 0.5× 1.9k 0.4× 376 0.1× 331 22.5k

Countries citing papers authored by Michael E. Goddard

Since Specialization
Citations

This map shows the geographic impact of Michael E. Goddard'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 Michael E. Goddard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael E. Goddard more than expected).

Fields of papers citing papers by Michael E. Goddard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael E. Goddard. 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 Michael E. Goddard. The network helps show where Michael E. Goddard may publish in the future.

Co-authorship network of co-authors of Michael E. Goddard

This figure shows the co-authorship network connecting the top 25 collaborators of Michael E. Goddard. A scholar is included among the top collaborators of Michael E. Goddard 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 Michael E. Goddard. Michael E. Goddard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Xiang, Ruidong, Edmond J. Breen, Sunduimijid Bolormaa, et al.. (2025). Integrating extensive functional annotations and multiomics of cattle enhances climate resilience prediction and mapping. Proceedings of the National Academy of Sciences. 122(49). e2514736122–e2514736122.
2.
Zeng, Jian, Yang Wu, Zhili Zheng, et al.. (2024). GENOME-WIDE FINE-MAPPING IMPROVES IDENTIFICATION OF CAUSAL VARIANTS. European Neuropsychopharmacology. 87. 12–13. 1 indexed citations
3.
Bolormaa, Sunduimijid, M. Haile‐Mariam, Leah C. Marett, et al.. (2023). Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle. Animal Production Science. 63(11). 1113–1125. 1 indexed citations
4.
Goddard, Michael E., Jennifer E. Cole, Leo‐Pekka Lyytikäinen, et al.. (2022). C-type lectin receptor CLEC4A2 promotes tissue adaptation of macrophages and protects against atherosclerosis. Nature Communications. 13(1). 215–215. 37 indexed citations
5.
Charlesworth, Brian, Michael E. Goddard, Karin Meyer, et al.. (2022). From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill. Nature Genetics. 54(7). 934–939. 4 indexed citations
6.
Xiang, Ruidong, Sunduimijid Bolormaa, Christy J. Vander Jagt, et al.. (2021). Mutant alleles differentially shape fitness and other complex traits in cattle. Communications Biology. 4(1). 9 indexed citations
7.
Xiang, Ruidong, Irene van den Berg, Iona M. MacLeod, et al.. (2019). Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits. Proceedings of the National Academy of Sciences. 116(39). 19398–19408. 102 indexed citations
8.
Goddard, Michael E., Iona M. MacLeod, Kathryn E. Kemper, et al.. (2018). The use of multi-breed reference populations and multi-omic data to maximize accuracy of genomic prediction. Queensland's institutional digital repository (The University of Queensland). 115. 2 indexed citations
9.
Liu, Zengting & Michael E. Goddard. (2018). A SNP MACE model for international genomic evaluation: technical challenges and possible solutions. Proceedings of the World Congress on Genetics Applied to Livestock Production. 393.
10.
Masson-Lecomte, A., Evangelina López de Maturana, Michael E. Goddard, et al.. (2016). Inflammatory-Related Genetic Variants in Non–Muscle-Invasive Bladder Cancer Prognosis: A Multimarker Bayesian Assessment. Cancer Epidemiology Biomarkers & Prevention. 25(7). 1144–1150. 4 indexed citations
11.
Goddard, Michael E., Michael A. Black, Rüdiger Bräuning, et al.. (2016). Copy number variants in the sheep genome detected using multiple approaches. BMC Genomics. 17(1). 441–441. 29 indexed citations
12.
Erbe, Malena, et al.. (2014). Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels (vol 95, pg 4114, 2012). Journal of Dairy Science. 97(10). 1 indexed citations
13.
Jagt, Christy Vander, et al.. (2014). A Gene Expression Atlas From Bovine RNAseq Data. Proceedings of the World Congress on Genetics Applied to Livestock Production. 180. 3 indexed citations
14.
Kemper, Kathryn E., Michael E. Goddard, & Stephen Bishop. (2013). Adaptation of gastrointestinal nematode parasites to host genotype: single locus simulation models. Genetics Selection Evolution. 45(1). 14–14. 15 indexed citations
15.
Bolormaa, Sunduimijid, J.E. Pryce, Kathryn E. Kemper, et al.. (2013). Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies. Genetics Selection Evolution. 45(1). 43–43. 47 indexed citations
16.
Goddard, Michael E., et al.. (2003). Relationships between calving traits in heifers and mature cows in Australia. Bulletin - International Bull Evaluation Service/Interbull bulletin. 102. 14 indexed citations
17.
Haile‐Mariam, M., Michael E. Goddard, & P.J. Bowman. (2001). Estimates of Genetic Parameters for Daily Somatic Cell Count of Australian Dairy Cattle. Journal of Dairy Science. 84(5). 1255–1264. 60 indexed citations
18.
Goddard, Michael E.. (2001). Genetics to improve milk quality. Australian Journal of Dairy Technology. 56(2). 166–170. 5 indexed citations
19.
Goddard, Michael E.. (1998). Advances in dairy cattle breeding research. Bulletin - International Bull Evaluation Service/Interbull bulletin. 37. 5 indexed citations
20.
Wiggans, G.R. & Michael E. Goddard. (1996). A computationally feasible test day model with separate first and later lactation genetic effects. Bulletin - International Bull Evaluation Service/Interbull bulletin. 118. 10 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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