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Rare-variant collapsing analyses for complex traits: guidelines and applications

Nature Reviews Genetics volume 20, pages 747–759 (2019)Cite this article

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Abstract

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.

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Fig. 1: Outline of the standard collapsing analysis approach.
Fig. 2: Characterizing where the disease risk signal resides.

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Acknowledgements

The authors thank T. Hayeck for creating the figure in Box 2.

Author information

Authors and Affiliations

  1. Institute for Genomic Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY, USA

    Gundula Povysil, Joseph Hostyk, Vimla Aggarwal & David B. Goldstein

  2. Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK

    Slavé Petrovski

  3. Department of Medicine, The University of Melbourne, Austin Health and Royal Melbourne Hospital, Melbourne, Victoria, Australia

    Slavé Petrovski

  4. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA

    Andrew S. Allen

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Contributions

G.P., S.P. and J.H. researched data for the article. G.P., S.P. and D.B.G. wrote the article. G.P., S.P., J.H., A.S.A. and D.B.G. reviewed/edited the manuscript before submission. All authors contributed to discussing the content of the article.

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Correspondence to David B. Goldstein.

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Nature Reviews Genetics thanks S. Lee, X. Lin and B. Neale for their contribution to the peer review of this work.

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Related links

Bravo/TOPMed: https://bravo.sph.umich.edu/freeze5/hg38/

CADD: https://cadd.gs.washington.edu/

CCR: https://www.rebrand.ly/ccrregions

ExAC: http://exac.broadinstitute.org/

Digenic analysis tool: https://github.com/igm-team/Digenic

gnomAD: http://gnomad.broadinstitute.org/

LOFTEE: https://github.com/konradjk/loftee

MTR: http://mtr-viewer.mdhs.unimelb.edu.au/

PolyPhen-2: http://genetics.bwh.harvard.edu/pph2/

PrimateAI: https://github.com/Illumina/PrimateAI

SIFT: https://sift.bii.a-star.edu.sg/

SpliceAI: https://github.com/illumina/SpliceAI

subRVIS: http://www.subrvis.org/

TraP: http://trap-score.org/

UK Biobank: https://www.ukbiobank.ac.uk/

Glossary

Penetrant alleles

Alleles highly associated with a trait; the more penetrant the allele, the higher the percentage of individuals with that allele who also express a disease phenotype.

Deleterious variation

Genetic variation that is predicted to disrupt gene function and therefore lead to reduced fitness.

Allelic heterogeneity

The presence of different pathogenic variants in the same gene or at the same chromosome locus that all lead to the same or to very similar phenotypes.

Causal allele

A functional allele that increases disease risk.

Haploinsufficient disease genes

Disease-associated genes for which a single functional copy is insufficient to maintain normal function. Therefore, loss-of-function alleles are pathogenic even when heterozygous.

Background variation

Usually benign variants in the general population that are unconnected to the disease.

Bidirectional effects

Effects within a given gene, wherein some variants increase risk of disease, while others reduce risk.

Transition/transversion ratio

(Ti/Tv). Ratio of the number of transitions (interchanges of two-ring purines (A to G or vice versa) or of one-ring pyrimidines (C to T or vice versa)) to the number of transversions (interchanges of purine for pyrimidine bases).

Index samples

Individual samples or patients who are the focus of a study.

Consanguineous populations

Populations in which marriages between people who are second cousins or closer are common.

Bottlenecked populations

Populations that have gone through a severe and abrupt reduction in their number of individuals, which often leads to reduced genetic diversity.

Population stratification

Also known as population structure. Presence of a difference in allele frequencies due to systematic differences in ancestry between cases and controls.

Phred quality

(QUAL). The Phred-scaled posterior probability that all samples in a call set consist of homozygous reference alleles.

Genotype Phred quality

(GQ). Represents the Phred-scaled confidence that the genotype assignment is correct for a given sample.

Quality by depth

(QD). The Phred quality (QUAL) score normalized by allele depth for a variant.

Mapping quality

(MQ). Estimation of the overall mapping quality of reads supporting a variant call.

Variant quality score log-odds

(VQSLOD). A score, produced by the Genome Analysis Toolkit’s variant quality score recalibration, that represents the log-odds ratio of a variant being true versus being false under the trained Gaussian mixture model.

Trio sequencing

Procedure in which the index patient and both parents are sequenced in order to identify causative variants in the patient.

Phase

Defined as alleles that belong to the same parental haplotype and therefore affect the same copy of a gene; variants that are not in phase are on different haplotypes and therefore affect both copies of a gene.

Compound heterozygous

Presence of two different mutant alleles in a particular gene that affect both copies of the gene because they are not in phase.

Diagnostic yield

Rate of discovered diagnostic variants within a collection of cases being tested.

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Povysil, G., Petrovski, S., Hostyk, J. et al. Rare-variant collapsing analyses for complex traits: guidelines and applications. Nat Rev Genet 20, 747–759 (2019). https://doi.org/10.1038/s41576-019-0177-4

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