Sequence-based Association Studies Research Articles

MetaSeq: privacy preserving meta-analysis of sequencing-based association studies.

Human genetics recently transitioned from GWAS to studies based on NGS data. For GWAS, small effects dictated large sample sizes, typically made possible through meta-analysis by exchanging summary statistics across consortia. NGS studies groupwise-test for association of multiple potentially-causal alleles along each gene. They are subject to similar power constraints and therefore likely to resort to meta-analysis as well. The problem arises when considering privacy of the genetic information during the data-exchange process. Many scoring schemes for NGS association rely on the frequency of each variant thus requiring the exchange of identity of the sequenced variant. As such variants are often rare, potentially revealing the identity of their carriers and jeopardizing privacy. We have thus developed MetaSeq, a protocol for meta-analysis of genome-wide sequencing data by multiple collaborating parties, scoring association for rare variants pooled per gene across all parties. We tackle the challenge of tallying frequency counts of rare, sequenced alleles, for metaanalysis of sequencing data without disclosing the allele identity and counts, thereby protecting sample identity. This apparent paradoxical exchange of information is achieved through cryptographic means. The key idea is that parties encrypt identity of genes and variants. When they transfer information about frequency counts in cases and controls, the exchanged data does not convey the identity of a mutation and therefore does not expose carrier identity. The exchange relies on a 3rd party, trusted to follow the protocol although not trusted to learn about the raw data. We show applicability of this method to publicly available exome-sequencing data from multiple studies, simulating phenotypic information for powerful meta-analysis. The MetaSeq software is publicly available as open source.

Family-Based Association Studies for Next-Generation Sequencing

An individual's disease risk is determined by the compounded action of both common variants, inherited from remote ancestors, that segregated within the population and rare variants, inherited from recent ancestors, that segregated mainly within pedigrees. Next-generation sequencing (NGS) technologies generate high-dimensional data that allow a nearly complete evaluation of genetic variation. Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies. To meet the analytical challenges raised by NGS, we propose a general framework for sequence-based association studies that can use various types of family and unrelated-individual data sampled from any population structure and a universal procedure that can transform any population-based association test statistic for use in family-based association tests. We develop family-based functional principal-component analysis (FPCA) with or without smoothing, a generalized T(2), combined multivariate and collapsing (CMC) method, and single-marker association test statistics. Through intensive simulations, we demonstrate that the family-based smoothed FPCA (SFPCA) has the correct type I error rates and much more power to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants, and (4) variants with opposite directions of effect from other population-based or family-based association analysis methods. The proposed statistics are applied to two data sets with pedigree structures. The results show that the smoothed FPCA has a much smaller p value than other statistics.

Statistical Challenges in Sequence-Based Association Studies with Population- and Family-Based Designs

Over the past few years, association analysis has become the primary tool for finding genes that underlie complex traits. Both population-based and family-based designs are commonly used designs in genetic association studies. Recent technological advances in exome and whole genome sequencing afford the next generation of sequence-based association studies. We review here recent developments in statistical methodology and remaining challenges related to sequence-based association studies with both population-based and family-based designs.

Lack of support for association between the copy number variants in the FCGR locus and schizophrenia: A case control study

The missing heritability of polygenic schizophrenia after genome-wide association studies (GWAS) can be potentially accounted for by the fact that most dynamic multiallelic copy number variants (CNVs) overlap segmental duplications (SDs). The FCGR locus covers this category of complex CNVs and it has long been postulated to harbor variants conferring the risk of schizophrenia. However, such association remains unproven. We used a case-control design to investigate CNV-based association with the disease. Data were obtained from 598 unrelated schizophrenia patients and 959 normal controls of Han ancestry from Shanghai. A total of four copy number (CN) probes in the FCGR locus were detected using TaqMan® Copy Number Assay. SPSS version 16.0 was used for the statistical analyses. And the frequency distributions of target CN in FCGR locus were very similar between controls and cases, whereas the CNV frequency differed markedly among different target CN analyzed in the two cohorts. When compared with the predominant two copies per diploid genome, a distinct non-protein-coding CN deletion region containing regulatory sequences was detected by probe Hs04194069_cn. Taken together, we found no evidence of association of target CNVs in the FCGR locus with schizophrenia. However, our negative findings suggest that more detailed next generation sequencing-based association studies are needed to fully evaluate the contribution of this category of complex CNVs to the disease.

A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data

For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies

Variants identified in recent genome-wide association studies based on the common-disease common-variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare-variant analysis and refined this design framework for future large-scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood-based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing-based association studies from a cost-efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two-stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two-stage design is a cost-efficient alternative to the one-stage cross-sectional design or traditional two-stage design. We then discussed the analysis strategies for this extreme two-stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme-based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare-variants in the same gene region are analyzed jointly.

Mapping copy number variation by population-scale genome sequencing

SummaryGenomic structural variants (SVs) are abundant in humans, differing from other variation classes in extent, origin, and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (i.e., copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analyzing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Biomarkers in cancer: is ‘omices’ the way to go

Biomarkers in cancer: is ‘omices’ the way to go

DbDNV: a resource of duplicated gene nucleotide variants in human genome

Gene duplications are scattered widely throughout the human genome. A single-base difference located in nearly identical duplicated segments may be misjudged as a single nucleotide polymorphism (SNP) from individuals. This imperfection is undistinguishable in current genotyping methods. As the next-generation sequencing technologies become more popular for sequence-based association studies, numerous ambiguous SNPs are rapidly accumulated. Thus, analyzing duplication variations in the reference genome to assist in preventing false positive SNPs is imperative. We have identified >10% of human genes associated with duplicated gene loci (DGL). Through meticulous sequence alignments of DGL, we systematically designated 1 236 956 variations as duplicated gene nucleotide variants (DNVs). The DNV database (dbDNV) (http://goods.ibms.sinica.edu.tw/DNVs/) has been established to promote more accurate variation annotation. Aside from the flat file download, users can explore the gene-related duplications and the associated DNVs by DGL and DNV searches, respectively. In addition, the dbDNV contains 304 110 DNV-coupled SNPs. From DNV-coupled SNP search, users observe which SNP records are also variants among duplicates. This is useful while ∼58% of exonic SNPs in DGL are DNV-coupled. Because of high accumulation of ambiguous SNPs, we suggest that annotating SNPs with DNVs possibilities should improve association studies of these variants with human diseases.

A statistical method for the detection of variants from next-generation resequencing of DNA pools

Motivation: Next-generation sequencing technologies have enabled the sequencing of several human genomes in their entirety. However, the routine resequencing of complete genomes remains infeasible. The massive capacity of next-generation sequencers can be harnessed for sequencing specific genomic regions in hundreds to thousands of individuals. Sequencing-based association studies are currently limited by the low level of multiplexing offered by sequencing platforms. Pooled sequencing represents a cost-effective approach for studying rare variants in large populations. To utilize the power of DNA pooling, it is important to accurately identify sequence variants from pooled sequencing data. Detection of rare variants from pooled sequencing represents a different challenge than detection of variants from individual sequencing.Results: We describe a novel statistical approach, CRISP [Comprehensive Read analysis for Identification of Single Nucleotide Polymorphisms (SNPs) from Pooled sequencing] that is able to identify both rare and common variants by using two approaches: (i) comparing the distribution of allele counts across multiple pools using contingency tables and (ii) evaluating the probability of observing multiple non-reference base calls due to sequencing errors alone. Information about the distribution of reads between the forward and reverse strands and the size of the pools is also incorporated within this framework to filter out false variants. Validation of CRISP on two separate pooled sequencing datasets generated using the Illumina Genome Analyzer demonstrates that it can detect 80–85% of SNPs identified using individual sequencing while achieving a low false discovery rate (3–5%). Comparison with previous methods for pooled SNP detection demonstrates the significantly lower false positive and false negative rates for CRISP.Availability: Implementation of this method is available at http://polymorphism.scripps.edu/∼vbansal/software/CRISP/Contact: vbansal@scripps.edu

Evaluation of next generation sequencing platforms for population targeted sequencing studies

Human sequence generated from three next-generation sequencing platforms reveals systematic variability in sequence coverage due to local sequence characteristics.

The Predicted Impact of Coding Single Nucleotide Polymorphisms Database

Nonsynonymous single nucleotide polymorphisms (nsSNP) have the potential to affect the structure or function of expressed proteins and are, therefore, likely to represent modifiers of inherited susceptibility. We have classified and catalogued the predicted functionality of nsSNPs in genes relevant to the biology of cancer to facilitate sequence-based association studies. Candidate genes were identified using targeted search terms and pathways to interrogate the Gene Ontology Consortium database, Kyoto Encyclopedia of Genes and Genomes database, Iobion's Interaction Explorer PathwayAssist Program, National Center for Biotechnology Information Entrez Gene database, and CancerGene database. A total of 9,537 validated nsSNPs located within annotated genes were retrieved from National Center for Biotechnology Information dbSNP Build 123. Filtering this list and linking it to 7,080 candidate genes yielded 3,666 validated nsSNPs with minor allele frequencies > or =0.01 in Caucasian populations. The functional effect of nsSNPs in genes with a single mRNA transcript was predicted using three computational tools-Grantham matrix, Polymorphism Phenotyping, and Sorting Intolerant from Tolerant algorithms. The resultant pool of 3,009 fully annotated nsSNPs is accessible from the Predicted Impact of Coding SNPs database at http://www.icr.ac.uk/cancgen/molgen/MolPopGen_PICS_database.htm. Predicted Impact of Coding SNPs is an ongoing project that will continue to curate and release data on the putative functionality of coding SNPs.