Michael E. Goddard
Impact in
- Genetics top 0.01%
- Genetic and phenotypic traits in livestock
- Genetic Mapping and Diversity in Plants and Animals
- Genetic Associations and Epidemiology
- Genetic diversity and population structure
- Agronomy and Crop Science top 0.05%
- Reproductive Physiology in Livestock
Papers in
- Genetics 318
- Genetic and phenotypic traits in livestock 275
- Genetic Mapping and Diversity in Plants and Animals 188
- Genetic Associations and Epidemiology 60
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- Genetics and Plant Breeding 60
- Co-authors
- Ben J. Hayes (94 shared papers)Peter M. Visscher (68 shared papers)Jian Yang (31 shared papers)T.H.E. Meuwissen (16 shared papers)Sang Lee (15 shared papers)Naomi R. Wray (40 shared papers)P.J. Bowman (25 shared papers)Amanda J. Chamberlain (47 shared papers)
- Journals
- Journal of Dairy Science (42 papers)Genetics Selection Evolution (25 papers)BMC Genomics (14 papers)Genetics (13 papers)Journal of Animal Science (13 papers)
- Partner nations
- AustraliaUnited StatesUnited Kingdom
In The Last Decade
Michael E. Goddard
380 papers receiving 39.3k citations
Michael E. Goddard's Hit Papers
Peers
Comparison fields: 5 of 201
- Genetics 29.4k
- Agronomy and Crop Science 3.9k
- Animal Science and Zoology 2.8k
- Plant Science 9.1k
- Small Animals 1.4k
Countries citing papers authored by Michael E. Goddard
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
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-authors
The 25 scholars most cited alongside Michael E. Goddard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 397 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps Hit paper breakdown → | 2001 | 5944 |
| 2 | GCTA: A Tool for Genome-wide Complex Trait Analysis Hit paper breakdown → | 2010 | 4808 |
| 3 | Common SNPs explain a large proportion of the heritability for human height Hit paper breakdown → | 2010 | 2998 |
| 4 | Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets Hit paper breakdown → | 2016 | 1714 |
| 5 | Invited review: Genomic selection in dairy cattle: Progress and challenges Hit paper breakdown → | 2009 | 1363 |
| 6 | Mapping genes for complex traits in domestic animals and their use in breeding programmes Hit paper breakdown → | 2009 | 815 |
| 7 | Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits Hit paper breakdown → | 2012 | 768 |
| 8 | Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits Hit paper breakdown → | 2008 | 749 |
| 9 | Estimating Missing Heritability for Disease from Genome-wide Association Studies Hit paper breakdown → | 2011 | 671 |
| 10 | Advantages and pitfalls in the application of mixed-model association methods Hit paper breakdown → | 2014 | 642 |
| 11 | Genomic selection Hit paper breakdown → | 2007 | 554 |
| 12 | Increased accuracy of artificial selection by using the realized relationship matrix Hit paper breakdown → | 2009 | 497 |
| 13 | 2007 | 490 | |
| 14 | Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels Hit paper breakdown → | 2012 | 484 |
| 15 | Pitfalls of predicting complex traits from SNPs Hit paper breakdown → | 2013 | 477 |
| 16 | 2008 | 406 | |
| 17 | Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs Hit paper breakdown → | 2012 | 398 |
| 18 | Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood Hit paper breakdown → | 2012 | 393 |
| 19 | 2011 | 342 | |
| 20 | Concepts, estimation and interpretation of SNP-based heritability Hit paper breakdown → | 2017 | 305 |
About Michael E. Goddard
Michael E. Goddard is a scholar working on Genetics, Plant Science, Agronomy and Crop Science, Animal Science and Zoology and Molecular Biology, having authored 397 papers that have together received 40.5k indexed citations. Recurring topics across this work include Genetic and phenotypic traits in livestock (275 papers), Genetic Mapping and Diversity in Plants and Animals (188 papers), Genetics and Plant Breeding (60 papers), Genetic Associations and Epidemiology (60 papers), Reproductive Physiology in Livestock (42 papers), Ruminant Nutrition and Digestive Physiology (22 papers), Effects of Environmental Stressors on Livestock (22 papers) and Cancer-related molecular mechanisms research (21 papers). The work is most often cited by research in Genetics (29.4k citations), Agronomy and Crop Science (3.9k citations), Animal Science and Zoology (2.8k citations), Plant Science (9.1k citations) and Small Animals (1.4k citations). Michael E. Goddard has collaborated with scholars based in Australia, United States and United Kingdom. Frequent co-authors include Ben J. Hayes, Peter M. Visscher, Jian Yang, T.H.E. Meuwissen, Sang Lee, Naomi R. Wray, P.J. Bowman, Amanda J. Chamberlain, Grant W. Montgomery and Pamela A. F. Madden. Their work appears in journals such as Journal of Dairy Science, Genetics Selection Evolution, BMC Genomics, Genetics and Journal of Animal Science.
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.