Skip navigation

You are here: CIDR>Sequencing> Whole Genome


Whole Genome


CIDR offers high (30X) and low pass whole genome services. Please inquire if you have a low pass project in mind.


Next Generation Sequencing Platform

     Illumina NovaSeq sequencers


Library Prep

Our whole genome library prep is PCR-free and utilizes a double –sided SPRI clean up to create a target insert size range of 450-600bp.


Services Include



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.


Please inquire with any questions about specific issues for your proposed study.


In most cases, it is expected that all samples will be collected and phenotyped before applying for the NIH CIDR Program. Possible exceptions should be discussed with CIDR and the NIH supporting institute before applying.




1. 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.


2. 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.


3. 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.


4. 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.


5. 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.


6. 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.












photo of cBot


Illumina cBot