| Chapter title |
Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations
|
|---|---|
| Chapter number | 9 |
| Book title |
Genome-Wide Association Studies and Genomic Prediction
|
| Published in |
Methods in molecular biology, January 2013
|
| DOI | 10.1007/978-1-62703-447-0_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-446-3, 978-1-62703-447-0
|
| Authors |
Jian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher, Yang J, Lee SH, Goddard ME, Visscher PM, Yang, Jian, Lee, Sang Hong, Goddard, Michael E, Visscher, Peter M, Goddard, Michael E., Visscher, Peter M. |
| Editors |
Cedric Gondro, Julius van der Werf, Ben Hayes |
| Abstract |
Estimating genetic variance is traditionally performed using pedigree analysis. Using high-throughput DNA marker data measured across the entire genome it is now possible to estimate and partition genetic variation from population samples. In this chapter, we introduce methods and a software tool called Genome-wide Complex Trait Analysis (GCTA) to estimate genomic relationships between pairs of conventionally unrelated individuals using genome-wide single nucleotide polymorphism (SNP) data, to estimate variance explained by all SNPs simultaneously on genomic or chromosomal segments or over the whole genome, and to perform a joint and conditional multiple SNPs association analysis using summary statistics from a meta-analysis of genome-wide association studies and linkage disequilibrium between SNPs estimated from a reference sample. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Colombia | 1 | <1% |
| Unknown | 108 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 21% |
| Researcher | 23 | 21% |
| Student > Master | 11 | 10% |
| Student > Bachelor | 7 | 6% |
| Student > Doctoral Student | 6 | 5% |
| Other | 21 | 19% |
| Unknown | 21 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 27% |
| Biochemistry, Genetics and Molecular Biology | 24 | 21% |
| Psychology | 7 | 6% |
| Computer Science | 5 | 4% |
| Medicine and Dentistry | 5 | 4% |
| Other | 13 | 12% |
| Unknown | 28 | 25% |