SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations

Integrating External Controls by Regression Calibration for Genome-Wide Association Study

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Analysis in case-control sequencing association studies with different sequencing depths

With the advent of next-generation sequencing, investigators have access to higher quality sequencing data. However, to sequence all samples in a study using next generation sequencing can still be prohibitively expensive. One potential remedy could be to combine next generation sequencing data from cases with publicly available sequencing data for controls, but there could be a systematic difference in quality of sequenced data, such as sequencing depths, between sequenced study cases and publicly available controls. We propose a regression calibration (RC)-based method and a maximum-likelihood method for conducting an association study with such a combined sample by accounting for differential sequencing errors between cases and controls. The methods allow for adjusting for covariates, such as population stratification as confounders. Both methods control type I error and have comparable power to analysis conducted using the true genotype with sufficiently high but different sequencing depths. We show that the RC method allows for analysis using naive variance estimate (closely approximates true variance in practice) and standard software under certain circumstances. We evaluate the performance of the proposed methods using simulation studies and apply our methods to a combined data set of exome sequenced acute lung injury cases and healthy controls from the 1000 Genomes project.

Likelihood-based complex trait association testing for arbitrary depth sequencing data

In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ∼60% when the causal variant is of low frequency (MAF < 0.01).

AVAILABILITY AND IMPLEMENTATION

UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/∼yunmli/UNCcombo/

CONTACT

yunli@med.unc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association analysis using next-generation sequence data from publicly available control groups: the robust variance score statistic

MOTIVATION

Sufficiently powered case-control studies with next-generation sequence (NGS) data remain prohibitively expensive for many investigators. If feasible, a more efficient strategy would be to include publicly available sequenced controls. However, these studies can be confounded by differences in sequencing platform; alignment, single nucleotide polymorphism and variant calling algorithms; read depth; and selection thresholds. Assuming one can match cases and controls on the basis of ethnicity and other potential confounding factors, and one has access to the aligned reads in both groups, we investigate the effect of systematic differences in read depth and selection threshold when comparing allele frequencies between cases and controls. We propose a novel likelihood-based method, the robust variance score (RVS), that substitutes genotype calls by their expected values given observed sequence data.

RESULTS

We show theoretically that the RVS eliminates read depth bias in the estimation of minor allele frequency. We also demonstrate that, using simulated and real NGS data, the RVS method controls Type I error and has comparable power to the 'gold standard' analysis with the true underlying genotypes for both common and rare variants.

AVAILABILITY AND IMPLEMENTATION

An RVS R script and instructions can be found at strug.research.sickkids.ca, and at https://github.com/strug-lab/RVS.

CONTACT

lisa.strug@utoronto.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing

SUMMARY

Despite its great capability to detect rare variant associations, next-generation sequencing is still prohibitively expensive when applied to large samples. In case-control studies, it is thus appealing to sequence only a subset of cases to discover variants and genotype the identified variants in controls and the remaining cases under the reasonable assumption that causal variants are usually enriched among cases. However, this approach leads to inflated type-I error if analyzed naively for rare variant association. Several methods have been proposed in recent literature to control type-I error at the cost of either excluding some sequenced cases or correcting the genotypes of discovered rare variants. All of these approaches thus suffer from certain extent of information loss and thus are underpowered. We propose a novel method (BETASEQ), which corrects inflation of type-I error by supplementing pseudo-variants while keeps the original sequence and genotype data intact. Extensive simulations and real data analysis demonstrate that, in most practical situations, BETASEQ leads to higher testing powers than existing approaches with guaranteed (controlled or conservative) type-I error.

AVAILABILITY AND IMPLEMENTATION

BETASEQ and associated R files, including documentation, examples, are available at http://www.unc.edu/~yunmli/betaseq

Imputation of rare variants in next-generation association studies

The role of rare variants has become a focus in the search for association with complex traits. Imputation is a powerful and cost-efficient tool to access variants that have not been directly typed, but there are several challenges when imputing rare variants, most notably reference panel selection. Extensions to rare variant association tests to incorporate genotype uncertainty from imputation are discussed, as well as the use of imputed low-frequency and rare variants in the study of population isolates.

Population-Sequencing as a Biomarker for Sample Characterization

Sequencing is accepted as the “gold” standard for genetic analysis and continues to be used as a validation and reference tool. The idea of using sequence analysis directly for sample characterization has been met with skepticism. However, herein, utility of direct use of sequencing to identify multiple genomes present in samples is presented and reviewed. All samples and “pure” isolates are populations of genomes. Population-Sequencing is the use of probabilistic matching tools in combination with large volumes of sequence information to identify genomes present, based on DNA analysis across entire genomes to determine genome assignments, to calculate confidence scores of major and minor genome content. Accurate genome identification from mixtures without culture purification steps can achieve phylogenetic classification by direct analysis of millions of DNA fragments. Genome sequencing data of mixtures can function as biomarkers for use to interrogate genetic content of samples and to establish a sample profile, inclusive of major and minor genome components, drill down to identify rare SNP and mutation events, compare relatedness of genetic content between samples, profile-to-profile, and provide a probabilistic or statistical scoring confidence for sample characterization and attribution. The application of Population-Sequencing will facilitate sample characterization and genome identification strategies.

Genetic association of cyclic AMP signaling genes with bipolar disorder

The genetic basis for bipolar disorder (BPD) is complex with the involvement of multiple genes. As it is well established that cyclic adenosine monophosphate (cAMP) signaling regulates behavior, we tested variants in 29 genes that encode components of this signaling pathway for associations with BPD type I (BPD I) and BPD type II (BPD II). A total of 1172 individuals with BPD I, 516 individuals with BPD II and 1728 controls were analyzed. Single SNP (single-nucleotide polymorphism), haplotype and SNP × SNP interactions were examined for association with BPD. Several statistically significant single-SNP associations were observed between BPD I and variants in the PDE10A gene and between BPD II and variants in the DISC1 and GNAS genes. Haplotype analysis supported the conclusion that variation in these genes is associated with BPD. We followed-up PDE10A's association with BPD I by sequencing a 23-kb region in 30 subjects homozygous for seven minor allele risk SNPs and discovered eight additional rare variants (minor allele frequency < 1%). These single-nucleotide variants were genotyped in 999 BPD cases and 801 controls. We obtained a significant association for these variants in the combined sample using multiple methods for rare variant analysis. After using newly developed methods to account for potential bias from sequencing BPD cases only, the results remained significant. In addition, SNP × SNP interaction studies suggested that variants in several cAMP signaling pathway genes interact to increase the risk of BPD. This report is among the first to use multiple rare variant analysis methods following common tagSNPs associations with BPD.