CIDR offers a variety of Sequencing Products. Specifics are listed below.
|Product||Library Prep||Capture||Sequencer||Data Generation Target|
|Whole Genome||PCR and PCR Free with double-sided SPRI clean-up||NA||NovaSeq6000||Average of 30X coverage with >99% concordance|
|Whole Exome||Low-Input||CIDR Custom Exome*||NovaSeq6000||Coverage of
|Custom Targeted||Low-Input||Custom design from: Twist,
IDT, Amplicon platforms
|Genotyping by Sequencing – low pass whole genome||Low-Input||N/A||NovaSeq6000||Coverage of
* CIDR Custom Exome is a Twist custom product that includes additional clinically relevant regions based on RefSeq and OMIM as well as Mitochondrial capture.
Genotyping by Sequencing
Two recent developments allow the utilization of low pass human whole genome sequencing (lpWGS) data for “genotyping by sequencing”. Imputation methods have been developed which optimize the speed and accuracy of genotype estimation using low-pass whole genome sequencing (lpWGS) and library preparation assays are available at much higher throughput and lower cost. Compared to SNP arrays with pre-selected variant sites which often target specific populations, lpWGS is unbiased in terms of variant sites, limited only by the sites observed in increasingly comprehensive population reference panels. Indeed, studies have shown that lpWGS can increase the power of GWAS1 and has more advantages than arrays in non-European populations2. The emergence of both new reference panels derived from large-scale diverse deep whole genome sequencing (WGS) data and more computationally efficient algorithms designed for imputation and phasing of lpWGS is now making lpWGS possible as a better alternative for SNP arrays1, 3, 4
CIDR has completed a pilot study to validate the practical implementation of this “Genotyping by Sequencing” service, presented at the Advances in Genome Biology and Technology General Meeting.
1. Li, J.H., Mazur, C.A., Berisa, T., Pickrell, J.K. (2021). Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Res. 31, 529-537.
2. Martin, A.R., Atkinson, E.G., Chapman, S.B., Stevenson, A., Stroud, R.E., Abebe, T., Akena, D., Alemayehu, M., Ashaba, F.K., Atwoli, L. (2021). Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. The American Journal of Human Genetics 108, 656-668.
3. Rubinacci, S., Ribeiro, D.M., Hofmeister, R.J., Delaneau, O. (2021). Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat. Genet. 53, 120-126.
4. Davies, R.W., Kucka, M., Su, D., Shi, S., Flanagan, M., Cunniff, C.M., Chan, Y.F., Myers, S. (2021). Rapid genotype imputation from sequence with reference panels. Nat. Genet. 53, 1104-1111.
Low pass whole genome sequencing at CIDR
For projects with large numbers of available samples, whole genome sequencing each sample at 2-8X depth instead of the standard 30X would produce sequence data on more samples given fixed yield/output. Although low pass sequencing reduces the certainty of each call, this method has advantages for some study designs. It has been proposed or employed by studies with many different aims including complex trait associations with rare and less common variants that would be missed on available genotyping arrays [1,2,6], building reference panels for imputation [1,3,4], variant discovery [3,4] and population genetics studies [5,6].
Applicants can use CIDR sequencing and genotyping services in designing the study. In general, CIDR would expect to run a standard Illumina genotyping array on all samples sequenced. CIDR and investigator technical replicates would be sequenced. Initial sample and variant QC would be based on multi-sample calling and comparison to array genotypes. We may be able to support additional project-specific calling and imputation methods prior to data release. Release would include array genotypes, BAM files, multi-sample VCF files, QC reports and variant annotation etc. Consulting and/ or assistance with posting to dbGaP may also be possible.
- Li Y, Sidore C, Kang HM, Boehnke M, Abecasis GR (2011) Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 2011 Jun;21(6):940-51
- Pasaniuc B1, Rohland N, McLaren PJ, Garimella K, Zaitlen N, Li H, Gupta N, Neale BM, Daly MJ, Sklar P, Sullivan PF, Bergen S, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Purcell SM, Haas DW, Liang L, Sunyaev S, Patterson N, de Bakker PI, Reich D, Price AL (2012) Extremely low-coverage sequencing and imputation increases power for genome-wide association studies Nat Genet. 2012 May 20;44(6):631-5
- The 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA et al (2012) An integrated map of genetic variation from 1,092 human genomes Nature 2012 Nov 1;491(7422):56-65
- 1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA et al (2010) A map of human genome variation from population-scale sequencing Nature 2010 Oct 28;467(7319):1061-73
- Alex Buerkle, Gompert Z (2013) Population genomics based on low coverage sequencing: how low should we go? Mol Ecol 2013 Jun;22(11):3028-35
- Flannick J, Korn JM, Fontanillas P, Grant GB, Banks E, Depristo MA, Altshuler D (2012) Efficiency and power as a function of sequence coverage, SNP array density, and imputation PLoS Comput Biol 2012;8(7):e1002604