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Opportunities and challenges for the use of common controls in sequencing studies

Nature Reviews Genetics volume 23, pages 665–679 (2022)Cite this article

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Abstract

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

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Fig. 1: Where to use common controls?
Fig. 2: Types of control samples.
Fig. 3: Types of bias that could affect common control analyses.
Fig. 4: Common control analysis workflow and example infrastructure with AnVIL.

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Acknowledgements

This work was supported by the Genome Sequencing Program (R35HG011293 to A.E.H. and C.R.G.; U01HG009080 to A.E.H., A.G.I., C.R.G. and M.A.R.; and U24HG008956 to S.B.). The Genome Sequencing Program is funded by the National Institute of Health (NIH) National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI) and the National Eye Institute (NEI). G.L.W. received support for this work from NHGRI (R35HG011944).

Author information

Authors and Affiliations

  1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Genevieve L. Wojcik

  2. Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA

    Jessica Murphy, Christopher R. Gignoux & Audrey E. Hendricks

  3. Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA

    Jessica Murphy & Audrey E. Hendricks

  4. Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, USA

    Jacob L. Edelson & Manuel A. Rivas

  5. Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Christopher R. Gignoux & Audrey E. Hendricks

  6. Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Christopher R. Gignoux & Audrey E. Hendricks

  7. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

    Alexander G. Ioannidis

  8. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA

    Alexander G. Ioannidis

  9. Metabolism Program, Broad Institute, Cambridge, MA, USA

    Alisa Manning

  10. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Alisa Manning

  11. Department of Statistics, Rutgers University, Piscataway, NJ, USA

    Steven Buyske

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  1. Genevieve L. Wojcik
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Contributions

G.L.W., J.M., J.L.E. and A.E.H. researched the literature. G.L.W., J.M., A.G.I., S.B. and A.E.H. provided substantial contributions to discussion of the content. G.L.W., J.M., S.B. and A.E.H. wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Audrey E. Hendricks.

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Competing interests

C.R.G. owns stock in 23and Me. M.A.R. is a scientific founder of Broadwing Bio, a consultant for MazeTx, and is currently on leave at HiBio. The other authors declare no competing interests.

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Related links

1000 Genomes Project: https://www.internationalgenome.org

All of Us: https://www.researchallofus.org/

AnVIL: https://anvilproject.org

BioData Catalyst: https://biodatacatalyst.nhlbi.nih.gov

CCDG: https://ccdg.rutgers.edu

CSVS: http://csvs.babelomics.org/

dbGaP: https://www.ncbi.nlm.nih.gov/gap/

dbGaP ALFA: https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/

EGA: https://ega-archive.org

Estonian Biobank: https://genomics.ut.ee/en/content/estonian-biobank

FinnGen: https://finngen.gitbook.io/documentation/data-download

GenomeAsia 100K: https://browser.genomeasia100k.org

gnomAD v.2.1: https://gnomad.broadinstitute.org/downloads

gnomAD v.3.1: https://gnomad.broadinstitute.org/downloads

H3Africa: https://catalog.h3africa.org

HGDP: https://www.internationalgenome.org/data-portal/data-collection/hgdp

INTERVAL: https://www.intervalstudy.org.uk

jMorp: https://jmorp.megabank.tohoku.ac.jp/202109/downloads/

Researcher Workbench: https://www.researchallofus.org/data-tools/workbench/

SGDP: https://cloud.google.com/life-sciences/docs/resources/public-datasets/simons

SISu v4.1: https://sisuproject.fi

Taiwan Biobank: https://taiwanview.twbiobank.org.tw/browse38

TOPMed: https://topmed.nhlbi.nih.gov

TOPMed Bravo: https://bravo.sph.umich.edu/freeze8/hg38/

UK Biobank: https://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=263

Glossary

Monogenic

A condition influenced by one genetic locus.

Oligogenic

A condition influenced by a few genetic loci.

Polygenic

A condition influenced by a large number of genetic loci.

Allele frequencies

The rates of genetic variant types in a specified population.

Common controls

Controls used for multiple studies.

Bias

Systematic error (as opposed to error due to chance processes), whether caused by statistical methods, differences between sampled individuals and the population they nominally represent, differences between cases and controls in ascertainment or sample processing, or other issues.

Confounding

A spurious association or lack of association caused by a third variable that is related to both the predictor variable (for example, allele frequency) and the outcome (for example, case status).

Internal controls

Controls that were ascertained, sequenced and processed together with the case sample. By contrast, external common controls were recruited, sequenced and processed separately, often using different technology from the case sample.

Biobanks

Collections of both biological samples (particularly DNA) and health information from individuals generally assembled from a region or a health system.

Harmonization

The formation of a single cohesive data set from two or more separate data sets by standardizing scales, definitions, quality control and other processing.

Batch effects

Differences between groups induced by processing over different times, places or technologies unrelated to biological causes.

Quality control

A process where low-quality data or observations are identified and improved or removed from further analysis.

Statistical power

The probability of rejecting the null hypothesis when it is false.

Ascertained cases

Participants of a study who are recruited to have a known disease, outcome or condition of interest.

Ascertained controls

Participants of a study who are recruited to not have a known disease, outcome or condition of interest.

Convenience sample

A sample drawn from an easily accessible, but often not representative, cohort.

Population controls

A control group sampled from a population but possibly lacking information regarding the condition of interest, with the result that some of the population controls will likely have the condition of interest.

Admixed

A term to denote the mixture of genetic ancestries from two or more divergent groups.

Population stratification

The presence of subpopulations with differing allele frequencies in a study; a source of confounding if phenotypes also vary by subpopulation.

False positives

Test results that are statistically significant even though there is no real association. By contrast, a false negative is a test result that is not statistically significant even though there is a real association.

Fine-scale ancestry

Genetic differentiation at a regional level (such as subcontinental), as opposed to continental-level ancestry.

Metadata

A high-level description of a data set, often including details of the cohort and of data generation.

Local ancestry

The genetic ancestry of a particular chromosomal region on a haplotype level.

Minor allele frequency

(MAF). For a genetic variant with two alleles, the frequency, in a specified population, of the less frequent allele.

In silico validation

Secondary quality control analysis of genotype calls, often of top association results, that passed the initial harmonization process to ensure that differences in processing do not drive important association signals.

Partial replication

Repeating association analysis reusing some data from the discovery analysis (for example, discovery cases and new external common controls).

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Wojcik, G.L., Murphy, J., Edelson, J.L. et al. Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 23, 665–679 (2022). https://doi.org/10.1038/s41576-022-00487-4

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