Statistical Approaches for Next-Generation Sequencing Data

During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) technology provides the instrument to look closely at these rare variants with precision and efficiency. However, new approaches for both the storage and analysis of sequencing data are in imminent needs. In this thesis, we introduce three methods that could be utilized in the management and analysis of sequencing data. In Chapter 1, we propose a novel and simple algorithm for compressing sequencing data that leverages on the scarcity of rare variant data, which enables the storage and analysis of sequencing data efficiently in current hardware environment. We also provide a C++ implementation that supports direct and parallel loading of the compressed format without requiring extra time for decompression. Chapter 2 and 3 focus on the association analysis of sequencing data in population-based design. In Chapter 2, we present a statistical methodology that allows the identification of genetic outliers to obtain a genetically homogeneous subpopulation, which reduces the false positives due to population substructure. Our approach is computationally efficient that can be applied to all the genetic loci in the data and does not require pruning of variants in linkage disequilibrium (LD). In Chapter 3, we propose a general analysis framework in which thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multi-loci analysis, which has focused on the dimension reduction of data, the proposed approach profits from the availability of large numbers of genetic loci. Thus it will be especially relevant for whole-genome sequencing studies which commonly record several thousand loci per gene

Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data

Background: As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results: Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. Conclusions: The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary

A comparative analysis of family-based and population-based association tests using whole genome sequence data

The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/

WISARD: workbench for integrated superfast association studies for related datasets

Background: A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used. Results: We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts. Conclusions: Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value. Electronic supplementary material The online version of this article (10.1186/s12920-018-0345-y) contains supplementary material, which is available to authorized users

A Genome-Wide Linkage Study for Chronic Obstructive Pulmonary Disease in a Dutch Genetic Isolate Identifies Novel Rare Candidate Variants

Chronic obstructive pulmonary disease (COPD) is a complex and heritable disease, associated with multiple genetic variants. Specific familial types of COPD may be explained by rare variants, which have not been widely studied. We aimed to discover rare genetic variants underlying COPD through a genome-wide linkage scan. Affected-only analysis was performed using the 6K Illumina Linkage IV Panel in 142 cases clustered in 27 families from a genetic isolate, the Erasmus Rucphen Family (ERF) study. Potential causal variants were identified by searching for shared rare variants in the exome-sequence data of the affected members of the families contributing most to the linkage peak. The identified rare variants were then tested for association with COPD in a large meta-analysis of several cohorts. Significant evidence for linkage was observed on chromosomes 15q14-15q25 [logarithm of the odds (LOD) score = 5.52], 11p15.4-11q14.1 (LOD = 3.71) and 5q14.3-5q33.2 (LOD = 3.49). In the chromosome 15 peak, that harbors the known COPD locus for nicotinic receptors, and in the chromosome 5 peak we could not identify shared variants. In the chromosome 11 locus, we identified four rare (minor allele frequency (MAF) <0.02), predicted pathogenic, missense variants. These were shared among the affected family members. The identified variants localize to genes including neuroblast differentiation-associated protein (AHNAK), previously associated with blood biomarkers in COPD, phospholipase C Beta 3 (PLCB3), shown to increase airway hyper-responsiveness, solute carrier family 22-A11 (SLC22A11), involved in amino acid metabolism and ion transport, and metallothionein-like protein 5 (MTL5), involved in nicotinate and nicotinamide metabolism. Association of SLC22A11 and MTL5 variants were confirmed in the meta-analysis of 9,888 cases and 27,060 controls. In conclusion, we have identified novel rare variants in plausible genes related to COPD. Further studies utilizing large sample whole-genome sequencing should further confirm the associations at chromosome 11 and investigate the chromosome 15 and 5 linked regions

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group

Genome-wide assessment of gene-by-smoking interactions in COPD

Cigarette smoke exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) and its interactions with genetic variants could affect lung function. However, few gene-smoking interactions have been reported. In this report, we evaluated the effects of gene-smoking interactions on lung function using Korea Associated Resource (KARE) data with the spirometric variables—forced expiratory volume in 1 s (FEV1). We found that variations in FEV1 were different among smoking status. Thus, we considered a linear mixed model for association analysis under heteroscedasticity according to smoking status. We found a previously identified locus near SOX9 on chromosome 17 to be the most significant based on a joint test of the main and interaction effects of smoking. Smoking interactions were replicated with Gene-Environment of Interaction and phenotype (GENIE), Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung), and COPDGene studies. We found that individuals with minor alleles, rs17765644, rs17178251, rs11870732, and rs4793541, tended to have lower FEV1 values, and lung function decreased much faster with age for smokers. There have been very few reports to replicate a common variant gene-smoking interaction, and our results revealed that statistical models for gene-smoking interaction analyses should be carefully selected