| Title: | Very low-depth whole-genome sequencing in complex trait association studies |
| Journal: | Bioinformatics |
| Published: | 21 Dec 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30576415/ |
| DOI: | https://doi.org/10.1093/bioinformatics/bty1032 |
| URL: | https://academic.oup.com/bioinformatics/article-pdf/35/15/2555/29012528/bty1032.pdf |
| Citations: | 80 (26 in last 2 years) as of 8 Aug 2024 |
Publication 12026
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Abstract
MOTIVATION: Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking.</p>
RESULTS: We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design.</p>
AVAILABILITY AND IMPLEMENTATION: The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype.</p>
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p>
17 Authors
- Arthur Gilly
- Lorraine Southam
- Daniel Suveges
- Karoline Kuchenbaecker
- Rachel Moore
- Giorgio E M Melloni
- Konstantinos Hatzikotoulas
- Aliki-Eleni Farmaki
- Graham Ritchie
- Jeremy Schwartzentruber
- Petr Danecek
- Britt Kilian
- Martin O Pollard
- Xiangyu Ge
- Emmanouil Tsafantakis
- George Dedoussis
- Eleftheria Zeggini
1 Application
| Application ID | Title |
|---|---|
| 13745 | Defining the normal ranges of blood count as a function of demographic variables and genetic analysis of blood count parameters |
Enabling scientific discoveries that improve human health