Title |
Population genetic analysis of bi-allelic structural variants from low-coverage sequence data with an expectation-maximization algorithm
|
---|---|
Published in |
BMC Bioinformatics, May 2014
|
DOI | 10.1186/1471-2105-15-163 |
Pubmed ID | |
Authors |
José Ignacio Lucas-Lledó, David Vicente-Salvador, Cristina Aguado, Mario Cáceres |
Abstract |
Population genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 25% |
Poland | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 75% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 2 | 5% |
United States | 1 | 3% |
Singapore | 1 | 3% |
Unknown | 34 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 10 | 26% |
Researcher | 8 | 21% |
Student > Master | 4 | 11% |
Student > Bachelor | 3 | 8% |
Student > Postgraduate | 3 | 8% |
Other | 6 | 16% |
Unknown | 4 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 16 | 42% |
Biochemistry, Genetics and Molecular Biology | 10 | 26% |
Computer Science | 4 | 11% |
Environmental Science | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 2 | 5% |
Unknown | 4 | 11% |