| Chapter title |
Identifying Cryptic Relationships
|
|---|---|
| Chapter number | 4 |
| Book title |
Statistical Human Genetics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-7274-6_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7273-9, 978-1-4939-7274-6
|
| Authors |
Lei Sun, Apostolos Dimitromanolakis, Wei-Min Chen |
| Abstract |
Cryptic relationships such as first-degree relatives often appear in studies that collect population samples, including genome-wide association studies (GWAS) and next-generation sequencing (NGS) analyses. Cryptic relatedness not only increases type 1 error rate of association tests but also affects other analytical aspects of GWAS and NGS such as population stratification via principal component analysis. Here, we discuss three effective methods, as implemented in PREST, PLINK, and KING, to detect and correct for the problem of cryptic relatedness using high-throughput SNP data collected from GWAS and NGS experiments. We provide the analytical and practical details involved using three application examples. |
X Demographics
Geographical breakdown
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|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 33% |
| Professor | 1 | 11% |
| Unspecified | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Researcher | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 33% |
| Agricultural and Biological Sciences | 2 | 22% |
| Unspecified | 1 | 11% |
| Medicine and Dentistry | 1 | 11% |
| Unknown | 2 | 22% |