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BMC Proceedings

Open Access

Collapsing singletons may boost signal for associating rare variants in sequencing study

BMC Proceedings20148(Suppl 1):S50

https://doi.org/10.1186/1753-6561-8-S1-S50

Published: 17 June 2014

Abstract

Advances in next-generation sequencing technology have made it possible to comprehensively interrogate the entire spectrum of genomic variations including rare variants. They may help capture the remaining genetic heritability which has not been fully explained by previous genome-wide association studies. Here we performed a gene-based genome-wide scan to identify hypertension susceptibility loci in analysis of a whole genome sequencing cohort of 103 unrelated individuals. We found that collapsing singletons may boost signals for associating rare variants and identified SETX statistically significant by a genome-wide gene-based threshold (p value <5.0 × 10−6). The function of SETX in hypertension may be worthy of further investigation.

Background

Assuming the "common disease-common variant" hypothesis, genome-wide association studies (GWAS) have successfully identified hundreds of common variants that contribute to human traits and diseases [1]. These common variants, however, account for only a small fraction of disease or trait heritability. On the other hand, rare variants, having minor allele frequencies (MAFs) between 0.1% and 1%, may be functionally relevant and causal for a larger proportion of inheritable variability [2, 3].

Recent advances in next-generation sequencing (NGS) technology have made it technically and economically feasible to capture the full spectrum of genomic variation. NGS provides a powerful tool for systematic exploration of common and rare variants in the entire genome, even in large population-scale studies [4]. However, pinpointing causal variants remains a major challenge, particularly for associating rare variants with complex traits [5]. There is a substantial need for computational methods that allow for efficient association analysis of rare variants. Several powerful approaches tailored for rare-variant association studies have been proposed [69]. These tests offer us a powerful tool to investigate rare variants in the entire genome.

Methods

Data set

The Genetic Analysis Workshop 18 (GAW18) provided a whole genome sequencing data set for a hypertension cohort of 483 individuals. These samples were sequenced by Complete Genomics with approximately 60× coverage, and odd-numbered autosomes data were made available for analysis. After quality control, 464 individuals and 24 million single-nucleotide polymorphisms (SNPs) remained. Of those SNPs, more than 51% had MAFs <1%, which was the focus of our analysis in this article. The longitudinal hypertension phenotypes were provided for up to 4 time points. Because our analysis was focused on binary traits, we treated individuals diagnosed with hypertension in any of the 4 times as cases. We extracted 103 genetically unrelated individuals with both phenotype data and sequencing data. We found 39 unaffected controls and 64 cases affected by hypertension.

Preprocessing

The variants were stored in VCF files. We preprocessed them as follows. To end up with rare variants, we filtered out SNPs that were present in dbSNP132 or MAFs >1%. We also filtered out SNPs with a genotype missing rate >5%. The remaining missing genotypes were resampled from nonmissing individuals.
X i = 1 , i f a n y x i , j = 1 0 , o t h e r w i s e

Next, we grouped variants into sets based on RefSeq gene annotations [10], requiring SNPs lie between the RefSeq transcript start site and transcript end site. SNPs outside gene boundaries were not analyzed. In total, 10,148 genes from odd-numbered chromosomes were used. Finally, we collapsed singletons within each gene. A singleton was a variant being observed only once among all the samples. The rationale of collapsing singletons was that the distribution of singletons as 1 variable may reflect the association between genotype and phenotype. Hence, we created 1 supervariant for each gene by combining all the singletons within it using the following rules: for each sample, (a) the genotype was set to be 1, if there was at least 1 variant observed; (b) otherwise, the genotype was set to be 0.

Rare-variant association tests

We employed 3 recently published rare association tests, qMSAT [7], C-alpha [8], and CMC [9]. The qMSAT is a quality-weighted multivariate score association test that can utilize genotype quality information. However, genotype quality score information was not available in the GAW18 raw VCF files. Without utilizing quality information, the qMSAT test was equivalent to the linear sequence kernel association test (SKAT) [6], Sum of Squared U statistic test (SSU) [11], and C-alpha. The C-alpha test compared the assumed binomial distribution of rare variants in cases versus controls via a homogeneity test. CMC, a combined multivariate and collapsing method, collapsed variants in subgroups according to allele frequencies and combines these subgroups using Hotelling's T 2 test. For these 3 tests, we used permutation to evaluate association significance. Because permutation was computationally expensive, we utilized a 2-step strategy in searching and testing candidate loci. Specifically, we first used 1000 permutations, from which we can identify candidates with estimated p value <0.001. Then for these candidates we conducted 106 permutations so as to know if any loci were significant at a genome-wide gene-based threshold (0.05/10,000 = 5.0 × 10−6) using a Bonferroni assumption.

Results

After the preprocessing step, we obtained approximately 2.2 million rare variants, which were assigned to 10,148 genes for testing. We then performed the 3 tests, qMSAT, C-alpha, and CMC, using R (http://www.r-project.org). The qMSAT, C-alpha, and CMC identified 10, 6, and 7 genes with an estimated p value <0.001, respectively (Table 1). Only SETX revealed significance for all of the 3 methods. Using 106 permutations, qMSAT, C-alpha, and CMC yielded more precise p values of 2.0 × 10−6, 1.0 × 10−6, and 6.0 × 10−6, respectively, for SETX (Table 2). The CMC p value was slightly higher than the genome-wide gene-based threshold, which was possibly a result of its lower power compared to qMSAT and C-alpha [7].
Table 1

Genes with p <0.001 from at least 1 method using 103 permutations

Chr

Gene

# Variants (singletons)

qMSAT

C-alpha

CMC

chr1

NUP210L

674(221)

Y

  

chr1

USP1

51(19)

Y

Y

 

chr7

CUL1

348(114)

Y

  

chr9

RAB14

88(32)

Y

  

chr9

SETX

380(135)

Y

Y

Y

chr11

FLJ39051

44(11)

Y

  

chr11

GDPD5

338(104)

Y

  

chr19

GRIN3B

24(8)

Y

  

chr19

LOC100505495

249(78)

Y

Y

 

chr19

PSG5

111(26)

Y

 

Y

chr5

CXXC5

96(35)

 

Y

 

chr15

RCCD1

32(13)

 

Y

 

chr17

WSCD1

183(70)

 

Y

 

chr17

MLLT6

92(33)

  

Y

chr1

ATF6

576(173)

  

Y

chr7

ZNF775

69(21)

  

Y

chr19

LOC100128252

45(13)

  

Y

chr5

LOC728342

495(146)

  

Y

Table 2

Functional annotation and test of the rare variants in SETX

  

pValue | OR | 95% CI

pValue*

Regions

# Variants (singletons)

Fisher's exact test on supervariant

qMSAT

C-alpha

CMC

SETX (CDS + UTR + INTRON)

380(135)

3.7 × 10−6

8.8

[3.12, 27.43]

2.0 × 10−6

1.0 × 10−6

6.0 × 10−6

CDS

14(8)

1.000

1.0

[0.18, 6.94]

1.000

0.544

0.837

UTR

6(4)

0.632

0.6

[0.04, 8.60]

1.000

0.662

0.990

INTRON

360(123)

8.8 × 10−7

9.5

[3.43, 28.70]

<1.0 × 10−6

<1.0 × 10−6

<1.0 × 10−6

*p Values were calculated using 106 permutations.

Supervariant was defined by collapsing all the singletons.

SETX locates in chr9:135,136,827-135,230,372 and is a relatively large gene among all the human genes. The length of SETX (93,545 base pairs [bp]) is far greater than the median number (17,970 bp) of all the genes (p value <2.2 × 10−16, one-sided Wilcoxon signed rank test). Although it contains 26 exons, the total length of coding regions is only 8,034, suggesting that SETX includes large intronic regions. To pinpoint causal regions, we divided the 380 variants of SETX into 3 groups based on its functional annotations. Specifically, we applied ANNOVAR [12] to annotate the variants of SETX and grouped them into coding sequence regions (CDSs), untranslated regions (UTRs), and intronic regions (INTRONs) (see Table 2). We observed that a majority of rare variants were, indeed, from the intronic region. We tested these 3 regions using the same tests with 106 permutations. We found that the UTR group and the CDS group were far from being significant, suggesting that they may be irrelevant. Another possible reason may be that there are very few variants in these categories. Because the INTRON group became more significant than the whole gene-based tests after excluding the variants from these 2 groups, we may conclude that causal variants locate in the intronic region of SETX.

We then sought to elucidate why and where the signal came from. To this end, several in-depth analyses for SETX were performed. First, the Fisher's Exact test was conducted on the super feature we created by collapsing singletons. We found that, by collapsing all the 135 singletons on SETX, it achieved a very significant p value (3.7 × 10−6), together with OR = 8.8 and 95% CI = [3.12, 27.43] (see Table 2). This explained why SETX could be detectable under such a small sample size. We obtained more significance when testing the super feature with only singletons within the intronic regions (p value = 8.8 × 10−7, OR = 9.5, and 95% CI = [3.43, 28.70]), which was consistent with the results from 3 rare variant association tests. Second, we checked each rare variant and singleton individually by performing the same test. It turned out that none of them were significant, when the minimum p value was merely 0.14. This demonstrated that the significance of SETX was very unlikely a result of technical artifact, such as systematic sequencing error or imputation bias, because the new feature was a combination of hundreds of singletons. It also highlighted that collapsing singletons may increase power when studying association of rare variants using a relatively small sample size. Third, we took a closer examination of allele frequencies of the 380 rare variants located in SETX. Of the rare variants, 92 could be found in the 1000 Genomes Project (2012 February release, http://www.1000genomes.org/). We found their allele frequencies in general population were extremely low (mean frequency = 0.0004 for 92 rare variants), indicating that these variants were so rare that they may collectively have a composite effect of OR = 8.8 while missed in previous studies.

Finally, to further remove possible confounding effect of population stratification, we performed a principal component analysis on 100,000 randomly selected common variants with no missing value and MAF >0.1. Logistic regression test was then conducted on the created super feature for SETX, together with the first 10 principal components as covariates. We found that the super feature remained significant, with a p value = 6.7 × 10−5, while the 10 principal components were not.

The protein encoded by SETX contains a DNA-RNA helicase domain at its C-terminal, suggesting its involvement in both DNA and RNA processing. Mutations in SETX have been reported to be associated with ataxia-ocular apraxia-2 (AOA2) [13] and an autosomal dominant form of juvenile amyotrophic lateral sclerosis (ALS4) [10, 14]. However, the function of SETX and its role in hypertension remains unclear and may be worthy of further investigation.

Discussion

We performed 3 rare-variant association tests for the analysis of a whole genome sequencing data set to identify susceptibility genes in hypertension. We grouped and collapsed rare variants in a gene-based manner for 2 reasons: (a) the deleteriousness of variants could come from protein-coding sequence changes or noncoding intronic regions that contain regulatory elements. (b) Based on the previous simulation study [7], the power of the analysis could be as low as 0.2 (sample size <200). By collapsing singletons, one may benefit from increasing power. This idea was essentially similar as those burden tests for rare copy number variation in GWAS and de novo mutations in sequencing study. Indeed, we found that the signal was mainly from the intronic regions of SETX in a collective manner of those singletons.

The analysis can be extended and improved in several ways. First, it was shown in qMSAT [7] that incorporation of genotype call qualities directly in association tests can increase power. If raw reads are available, we may call variants and obtain genotype quality information at the same time, using variant calling toolkits [15, 16] to further increase our analysis power. Second, we only analyzed rare variants. The association test could be also performed by combining both rare and common variants. Third, we only included 103 unrelated individuals. We may consider adding more samples to increase power. Finally, we focused on only genic regions using conventional gene annotation, which make up little more than 1% of the genome. The recent annotation made by the ENCODE consortium has included more than 70,000 "promoter" regions and nearly 400,000 "enhancer" regions that regulate expression of distant genes, which account for roughly 80% of the genome [17]. We may utilize this new knowledge in future analysis.

Declarations

Acknowledgements

The authors would like to thank the Genetic Analysis Workshop 18 (GAW18) for preparing and providing the whole genome sequencing data set. We also thank Dr. Pingzhao Hu for the comments and suggestions on the analysis. We thank the 2 reviewers and Dr. Heather Cordell for their comments and advice, which helped to improve the presentation of the paper.

The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575.

This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.

Authors’ Affiliations

(1)
Department of Computer Science, New Jersey Institute of Technology, University Heights Newark

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Copyright

© Wang and Wei; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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