Supplemental Methods
Patient consent and preferences
In order to study the effect of timing on expressed preferences, families were randomized into two groups: group one recorded their preferences to receive genomic results at the time of enrollment and group two recorded their preferences immediately prior to their return of results visit. Individuals that were randomized into group one were given the option to change their preferences at the return of results appointment1.
Whole exome and genome sequencing
Blood samples were sent for sequencing at the HudsonAlpha Genomic Services Laboratory ( DNA was isolated from peripheral blood and WES (Nimblegen v3) or WGS was conducted to a mean depth of 71X or 35X, respectively, with 80% of bases covered at 20X. WES was conducted on Illumina HiSeq 2000 or 2500 machines; WGS was done on Illumina HiSeq Xs. Reads were aligned and variants called according to standard protocols2,3. A robust relationship inference algorithm (KING) was used to confirm familial relationships4.
Variant Filtration
Secondary variants were identified in parents as non-reference calls in which there were two or less alternate alleles, a batch allele frequency of ≤10, <40 counts in an internal allele frequency database, and the VQSR Filter class was “PASS”. Variants were also restricted to those that either affected a protein, a splice site, or had a scaled CADD score ≥155. Note that for the ACMG6 and OMIM7 gene lists, a minor allele frequency of ≤1% in 1000 Genomes 8and ExAC 9 was used, while for the carrier gene list, a minor allele frequency ≤25% in 1000 Genomes8 and ExAC9 was used.
Rare variants (minor allele frequency of ≤1% in 1000 Genomes, EVS and ExAC8-10) that have been submitted to the ClinVar database11 as pathogenic or likely pathogenic (or conflicting reports of pathogenicity, but at least one report as pathogenic or likely pathogenic) were also manually reviewed in each family.
Variant classification
Our study began prior to publication of the formal classification system proposed by the ACMG12. However, for this publication, we have assigned ACMG evidence codes to all returned variants. Key annotations (e.g., allele frequencies, PubMed identifiers, and computational inferences of variant effect) used to support the disease relevance of each variant are supplied in Table S1. Only variants found to be P/LP (set by the original classification) were eligible for return as a secondary finding.
Variant validation
WES and WGS were carried out under a research protocol and were not completed within a CAP/CLIA laboratory. All variants found to be medically relevant and returnable were validated by Sanger sequencing in an independent CLIA laboratory (Emory Genetics Laboratory) before being returned to participants, although these validated variant results are not CLIA-compliant as the input DNA was originally isolated in a research laboratory.
Data sharing
ClinVar11 submissions are under the submitter study name: CSER-HudsonAlpha. Sequencing data for the participants who consented to such sharing have been submitted to dbGaP (phs001089.v2.p1)13. As the study continues, additional data will be shared via the mechanisms mentioned.
References
1.Brothers KB, East KM, Kelley WV, et al. Eliciting preferences on secondary findings: the Preferences Instrument for Genomic Secondary Results. Genet Med 2017;19(3): 337-344.
2.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25(14): 1754-1760.
3.DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011;43(5): 491-498.
4.Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics 2010;26(22): 2867-2873.
5.Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 2014;46(3): 310-315.
6.Green RC, Berg JS, Grody WW, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 2013;15(7): 565-574.
7.Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005;33(Database issue): D514-517.
8.Genomes Project C, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature 2015;526(7571): 68-74.
9.Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016;536(7616): 285-291.
10.Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP). 2013.
11.Landrum MJ, Lee JM, Benson M, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 2016;44(D1): D862-868.
12.Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17(5): 405-424.
13.Mailman MD, Feolo M, Jin Y, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 2007;39(10): 1181-1186.
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