Statistical analysis strategies for association studies involving rare variants

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

A modified association test for rare and common variants based on affected sib-pair design

Current genome-wide association analysis has identified a great number of rare and common variants associated with common complex traits, however, more effective approaches for detecting associations between rare and common variants with common diseases are still demanded. Approaches for detecting rare variant association analysis will compromise the power when detecting the effects of rare and common variants simultaneously. In this paper, we extend an existing method of testing for rare variant association based on affected sib pairs (TOW-sib) and propose a variable weight test for rare and common variants association based on affected sib pairs (abbreviated as VW-TOWsib). The VW-TOWsib can be used to achieve the purpose of detecting the association of rare and common variants with complex diseases. Simulation results in various scenarios show that our proposed method is more powerful than existing methods for detecting effects of rare and common variants. At the same time, the VW-TOWsib also performs well as a method for rare variant association analysis.

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico

Background

The development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses.

Results

We conducted a large-scale simulation of randomly selected low-frequency protein-coding regions using twelve different balanced samples with an equal number of cases and controls as well as twenty-one unbalanced sample scenarios. We further explored statistical performance of different minor allele frequency thresholds and a range of genetic effect sizes. Our simulation results demonstrate that using an unbalanced study design has an overall higher type I error rate for both burden and dispersion tests compared with a balanced study design. Regression has an overall higher type I error with balanced cases and controls, while SKAT has higher type I error for unbalanced case-control scenarios. We also found that both type I error and power were driven by the number of cases in addition to the case to control ratio under large control group scenarios. Based on our power simulations, we observed that a SKAT analysis with case numbers larger than 200 for unbalanced case-control models yielded over 90% power with relatively well controlled type I error. To achieve similar power in regression, over 500 cases are needed. Moreover, SKAT showed higher power to detect associations in unbalanced case-control scenarios than regression.

Conclusions

Our results provide important insights into rare variant association study designs by providing a landscape of type I error and statistical power for a wide range of sample sizes. These results can serve as a benchmark for making decisions about study design for rare variant analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2591-6) contains supplementary material, which is available to authorized users.

Association score testing for rare variants and binary traits in family data with shared controls

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

The Role of ERRFI1+808T/G Polymorphism in Diabetic Nephropathy

Nephropathy is a common diabetes complication. ERRFI1 gene which participates in various cellular pathways has been proposed as a candidate gene in diabetic nephropathy. This study aimed to investigate the role of +808T/G polymorphism (rs377349) in ERRFI1 gene in diabetic nephropathy. In this case-control study, patients including diabetes with nephropathy (DN=104), type 2 diabetes without nephropathy (DM=100), and healthy controls (HC=106) were included. DNA was extracted from blood, and genotyping of the +808T/G polymorphism was carried out using PCR-RFLP technique. The differences for genotype and allele frequencies for +808T/G polymorphism in ERRFI1 gene between DN vs. HC and DN+DM vs. HC were significant (P<0.05) while no significant difference between DN and DM was observed. The allele frequencies were significantly different in DN vs. HC and DN+DM vs. HC in males but not in females. G allele of +808T/G polymorphism in ERRFI1 gene has no significant role in development and progression of diabetic nephropathy in diabetes patients while it is a risk allele for developing diabetes in Iranian population.

Genome-Wide Association Analyses in the Model Rhizobium Ensifer meliloti

Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.

BRCA1/2-negative, high-risk breast cancers (BRCAX) for Asian women: genetic susceptibility loci and their potential impacts

"BRCAX" refers breast cancers occurring in women with a family history predictive of being a BRCA1/2 mutation carrier, but BRCA1/2 genetic screening has failed to find causal mutations. In this study, we report the findings of the genetic architecture of BRCAX with novel and redefined candidate loci and their potential impacts on preventive strategy. We performed a genome-wide association study involving 1,469 BRCAX cases from the Korean Hereditary Breast Cancer study, and high-risk breast cancer cases (1,482 Asians and 9,902 Europeans) from the Breast Cancer Association Consortium. We also evaluated the previously reported susceptibility loci for their roles in the high-risk breast cancers. We have identified three novel loci (PDE7B, UBL3, and a new independent marker in CDKN2B-AS1) associated with BRCAX, and replicated previously reported SNPs (24 of 92) and moderate/high-penetrance (seven of 23) genes for Korean BRCAX. For the novel candidate loci, evidence supported their roles in regulatory function. We estimated that the common low-penetrance loci might explain a substantial part of high-risk breast cancer (39.4% for Koreans and 24.0% for Europeans). Our study findings suggest that common genetic markers with lower penetrance constitute a part of susceptibility to high-risk breast cancers, with potential implications for a more comprehensive genetic screening test.

A subregion-based burden test for simultaneous identification of susceptibility loci and subregions within

In rare variant association studies, aggregating rare and/or low frequency variants, may increase statistical power for detection of the underlying susceptibility gene or region. However, it is unclear which variants, or class of them, in a gene contribute most to the association. We proposed a subregion-based burden test (REBET) to simultaneously select susceptibility genes and identify important underlying subregions. The subregions are predefined by shared common biologic characteristics, such as the protein domain or functional impact. Based on a subset-based approach considering local correlations between combinations of test statistics of subregions, REBET is able to properly control the type I error rate while adjusting for multiple comparisons in a computationally efficient manner. Simulation studies show that REBET can achieve power competitive to alternative methods when rare variants cluster within subregions. In two case studies, REBET is able to identify known disease susceptibility genes, and more importantly pinpoint the unreported most susceptible subregions, which represent protein domains essential for gene function. R package REBET is available at https://dceg.cancer.gov/tools/analysis/rebet.

Methods and results from the genome-wide association group at GAW20

BACKGROUND

This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions.

RESULTS

The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal.

CONCLUSIONS

This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.

Functional annotation of genomic variants in studies of late-onset Alzheimer's disease

Motivation

Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.

Results

In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%).

Availability and implementation

Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeting Neuroplasticity, Cardiovascular, and Cognitive-Associated Genomic Variants in Familial Alzheimer's Disease

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2–12.7, P = 1.83 × 10⁻³) and ~ 7.6 years (95% CI 3.3–11.8, P = 8.69 × 10⁻⁴), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD.

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease

Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L) as candidate genes for familial LOAD.

Association analysis of multiple traits by an approach of combining P values

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.

Advances in the Genetics of Hypertension: The Effect of Rare Variants

Worldwide, hypertension still represents a serious health burden with nine million people dying as a consequence of hypertension-related complications. Essential hypertension is a complex trait supported by multifactorial genetic inheritance together with environmental factors. The heritability of blood pressure (BP) is estimated to be 30-50%. A great effort was made to find genetic variants affecting BP levels through Genome-Wide Association Studies (GWAS). This approach relies on the "common disease-common variant" hypothesis and led to the identification of multiple genetic variants which explain, in aggregate, only 2-3% of the genetic variance of hypertension. Part of the missing genetic information could be caused by variants too rare to be detected by GWAS. The use of exome chips and Next-Generation Sequencing facilitated the discovery of causative variants. Here, we report the advances in the detection of novel rare variants, genes, and/or pathways through the most promising approaches, and the recent statistical tests that have emerged to handle rare variants. We also discuss the need to further support rare novel variants with replication studies within larger consortia and with deeper functional studies to better understand how new genes might improve patient care and the stratification of the response to antihypertensive treatments.

Genetic architecture: the shape of the genetic contribution to human traits and disease

Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

Association analysis of rare and common variants with multiple traits based on variable reduction method

Pleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.

Genetics of neurodegenerative diseases: an overview

Genetic factors are central to the etiology of neurodegeneration, both as monogenic causes of heritable disease and as modifiers of susceptibility to complex, sporadic disorders. Over the last two decades, the identification of disease genes and risk loci has led to some of the greatest advances in medicine and invaluable insights into pathogenic mechanisms and disease pathways. Large-scale research efforts, novel study designs, and advances in methodology are rapidly expanding our understanding of the genome and the genetic architecture of neurodegenerative disease. Here, we review major developments in the field to date, highlighting overarching historic trends and general insights. Monogenic neurodegenerative diseases are discussed from the perspectives of both rare Mendelian forms of common disorders, such as Alzheimer disease and Parkinson disease, and heterogeneous heritable conditions, including ataxias and spastic paraplegias. Next, we summarize the experiences from investigations of complex neurodegenerative disorders, including genomewide association studies. In the final section, we reflect upon the limitations of current findings and outline important future directions. Genetics plays an essential role in translational research, ultimately aiming to develop novel disease-modifying therapies for neurodegenerative disorders. We anticipate that individual genetic profiling will also be increasingly relevant in a clinical context, with implications for patient care in line with the proposed ideal of personalized medicine.

Resequencing Epithelial Sodium Channel Genes Identifies Rare Variants Associated With Blood Pressure Salt-Sensitivity: The GenSalt Study

BACKGROUND

A resequencing study of renal epithelial sodium channel (ENaC) genes was conducted to identify rare variants associated with blood pressure (BP) salt-sensitivity.

METHODS

The Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study was conducted among 1,906 participants who underwent a 7-day low-sodium followed by a 7-day high-sodium feeding-study. The 300 most salt-sensitive and 300 most salt-resistant GenSalt participants were selected for the resequencing study. Three ENaC genes (SCNN1A, SCNN1B, and SCNN1G) were resequenced using capillary-based sequencing methods. Traditional burden tests were utilized to examine association between rare variants and BP salt-sensitivity. Associations of low-frequency and common variants were tested using single-marker analyses.

RESULTS

Carriers of SCNN1A rare variants had a 0.52 [95% confidence interval (CI): 0.32-0.85] decreased odds of BP salt-sensitivity compared with noncarriers. Neither SCNN1B nor SCNN1G associated with salt-sensitivity of BP in rare variant analyses (P = 0.65 and 0.48, respectively). In single-marker analyses, 3 independent common variants in SCNN1A, rs11614164, rs4764586, and rs3741914, associated with salt-sensitivity after Bonferroni correction (P = 4.4 × 10-4, 1.1 × 10-8, and 1.3 × 10-3). Each copy of the minor allele of rs4764586 was associated with a 1.36-fold (95% CI: 1.23-1.52) increased odds of salt-sensitivity, whereas each copy of the minor allele of rs11614164 and rs3741914 was associated with 0.68-fold (95% CI: 0.55-0.84) and 0.69-fold (95% CI: 0.54-0.86) decreased odds of salt-sensitivity, respectively.

CONCLUSIONS

This study demonstrated for the first time a relationship between rare variants in the ENaC pathway and BP salt-sensitivity. Future replication and functional studies are needed to confirm the findings in this study.

CLINICAL TRIAL REGISTRY

Trial Number NCT00721721.

Assessing Rare Variation in Complex Traits

While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A  range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.

Genomic determinants of long-term cardiometabolic complications in childhood acute lymphoblastic leukemia survivors

BACKGROUND

While cure rates for childhood acute lymphoblastic leukemia (cALL) now exceed 80%, over 60% of survivors will face treatment-related long-term sequelae, including cardiometabolic complications such as obesity, insulin resistance, dyslipidemia and hypertension. Although genetic susceptibility contributes to the development of these problems, there are very few studies that have so far addressed this issue in a cALL survivorship context.

METHODS

In this study, we aimed at evaluating the associations between common and rare genetic variants and long-term cardiometabolic complications in survivors of cALL. We examined the cardiometabolic profile and performed whole-exome sequencing in 209 cALL survivors from the PETALE cohort. Variants associated with cardiometabolic outcomes were identified using PLINK (common) or SKAT (common and rare) and a logistic regression was used to evaluate their impact in multivariate models.

RESULTS

Our results showed that rare and common variants in the BAD and FCRL3 genes were associated (p<0.05) with an extreme cardiometabolic phenotype (3 or more cardiometabolic risk factors). Common variants in OGFOD3 and APOB as well as rare and common BAD variants were significantly (p<0.05) associated with dyslipidemia. Common BAD and SERPINA6 variants were associated (p<0.05) with obesity and insulin resistance, respectively.

CONCLUSIONS

In summary, we identified genetic susceptibility loci as contributing factors to the development of late treatment-related cardiometabolic complications in cALL survivors. These biomarkers could be used as early detection strategies to identify susceptible individuals and implement appropriate measures and follow-up to prevent the development of risk factors in this high-risk population.

SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population

Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

The screening of the functional microRNA binding site SNPs in sporadic colorectal cancer genes

Sporadic colorectal cancer (sCRC) is one of the most commonly diagnosed cancers worldwide, but few genetic markers have been identified and used for its early detection. MicroRNAs are diverse cellular regulators in cancer pathogenesis that bind to the 3'-untranslated region (3'-UTR) of their target mRNAs, and variants within the miRNA target sites on sCRC-related genes may influence its pathogenesis. To investigate this possibility, we used a bioinformatical method to screen SNPs for putative changes in miRNA recognition sites within the 3'-UTR of sCRC-related genes. The rs11466537 single nucleotide polymorphism was predicted to modify the regulation of hsa-miR-1193 on the Transforming Growth Factor β Receptor II (TGFBR2) gene. Additionally, luciferase reporter assays indicated that hsa-miR-1193 bound the T allele more strongly than the A allele of rs11466537 (with A being the less frequent variant), and real time-polymerase chain reaction and western blot analysis showed that TGFBR2 is significantly repressed by hsa-miR-1193. Furthermore, overexpression of hsa-miR-1193 promoted HT-29 cell proliferation, while the loss of hsa-miR-1193 inhibited the process. Finally, the rs11466537 genotyping result revealed that the frequency of A allele carriers was 1.5% in the control blood samples, but 0 in the sCRC patients' normal colon tissue samples. Our results demonstrated that hsa-miR-1193 may be involved in sCRC tumourigenesis at least in part by suppression of TGFBR2, and the A allele of rs11466537 disturbed the regulation of hsa-miR-1193 on TGFBR2.

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

BACKGROUND

Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial.

METHODS

DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes.

RESULTS

Many statistically significant CV (p < 1 × 10^-8) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2, HPN-AS1, and SIRPD appear to be novel (q < 0.1).

DISCUSSION

Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant (p < 1 × 10^-8) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings.

Resequencing Study Identifies Rare Renin-Angiotensin-Aldosterone System Variants Associated With Blood Pressure Salt-Sensitivity: The GenSalt Study

BACKGROUND

The role of rare variants in blood pressure (BP) salt-sensitivity is unknown. We conducted a resequencing study of the renin-angiotensin-aldosterone system (RAAS) to identify rare variants associated with BP salt-sensitivity among participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study.

METHODS

The GenSalt study was conducted among 1,906 participants who underwent a 7-day low-sodium (51.3 mmol sodium/day) followed by a 7-day high-sodium feeding study (307.8 mmol sodium/day). The 300 most salt-sensitive and 300 most salt-resistant GenSalt participants were selected for the resequencing study. Seven RAAS genes were resequenced using capillary-based sequencing methods. Rare variants were tested for association with BP salt-sensitivity using traditional burden tests. Single-marker analyses were employed to test associations of low-frequency and common variants.

RESULTS

Aggregate rare variant analysis revealed an association of the RAAS pathway with BP salt-sensitivity. Carriers of rare RAAS variants had a 1.55-fold [95% confidence interval (CI): 1.15, 2.10] higher odds of salt-sensitivity compared to noncarriers (P = 0.004), a finding which was significant after Bonferroni correction. A nominal association of the APLN gene with salt-sensitivity was also identified, with rare APLN variants conferring a 2.22-fold (95% CI: 1.05, 6.58) higher odds of salt-sensitivity (P = 0.03). Single-marker analyses did not identify variant-BP salt-sensitivity associations after Bonferroni adjustment. A nominal association of a low-frequency, missense RENBP variant was identified. Each minor allele of rs78377269 conferred a 2.21-fold (95% CI: 1.10, 4.42) increased odds of salt-sensitivity (P = 0.03).

CONCLUSIONS

This study presents of the first evidence of a contribution of rare RAAS variants to BP salt-sensitivity. Clinical Trial RegistryTrial Number: NCT00721721.

On the association analysis of CNV data: a fast and robust family-based association method

BACKGROUND

Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest.

RESULTS

For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples.

CONCLUSIONS

We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers.

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

Whole exome sequencing implicates eye development, the unfolded protein response and plasma membrane homeostasis in primary open-angle glaucoma

PURPOSE

To identify biological processes associated with POAG and its subtypes, high-tension (HTG) and normal-tension glaucoma (NTG), by analyzing rare potentially damaging genetic variants.

METHODS

A total of 122 and 65 unrelated HTG and NTG participants, respectively, with early onset advanced POAG, 103 non-glaucoma controls and 993 unscreened ethnicity-matched controls were included in this study. Study participants without myocilin disease-causing variants and non-glaucoma controls were subjected to whole exome sequencing on an Illumina HiSeq2000. Exomes of participants were sequenced on an Illumina HiSeq2000. Qualifying variants were rare in the general population (MAF < 0.001) and potentially functionally damaging (nonsense, frameshift, splice or predicted pathogenic using SIFT or Polyphen2 software). Genes showing enrichment of qualifying variants in cases were selected for pathway and network analysis using InnateDB.

RESULTS

POAG cases showed enrichment of rare variants in camera-type eye development genes (p = 1.40×10-7, corrected p = 3.28×10-4). Implicated eye development genes were related to neuronal or retinal development. HTG cases were significantly enriched for key regulators in the unfolded protein response (UPR) (p = 7.72×10-5, corrected p = 0.013). The UPR is known to be involved in myocilin-related glaucoma; our results suggest the UPR has a role in non-myocilin causes of HTG. NTG cases showed enrichment in ion channel transport processes (p = 1.05×10-4, corrected p = 0.027) including calcium, chloride and phospholipid transporters involved in plasma membrane homeostasis. Network analysis also revealed enrichment of the MHC Class I antigen presentation pathway in HTG, and the EGFR1 and cell-cycle pathways in both HTG and NTG.

CONCLUSION

This study suggests that mutations in eye development genes are enriched in POAG. HTG can result from aberrant responses to protein misfolding which may be amenable to molecular chaperone therapy. NTG is associated with impaired plasma membrane homeostasis increasing susceptibility to apoptosis.

Opportunities and challenges of whole-genome and -exome sequencing

Recent advances in the development of sequencing technologies provide researchers with unprecedented possibilities for genetic analyses. In this review, we will discuss the history of genetic studies and the progress driven by next-generation sequencing (NGS), using complex inflammatory bowel diseases as an example. We focus on the opportunities, but also challenges that researchers are facing when working with NGS data to unravel the genetic causes underlying diseases.

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

A model-free test for detecting disease association signals with multiple genetic variants and covariates

Discoveries and analyses of genetic variants at a gene or exome based on high-throughput sequencing technology are increasingly feasible. Although many well-known association tests have already been proposed in literature for testing whether a group of variants in a target region is associated with a disease of interest, however, the analytic challenges still remain profound. The power performance of these tests generally depends on the sample size, numbers of causal and neutral variants, variant frequency, effect size, and direction. Some of these factors are not easily controllable in practical applications. Further complications arise from missing genotype, population stratification or misspecification of the working model. Previous studies showed that many model-based tests might create false positive results or decrease power when there was population stratification effect or missing genotype and simple imputation was used. Here, we demonstrate by simulations that type I errors of the well-known model-based tests are often inflated as well, even the working model deviates slightly from the true model. We propose a model-free test and show this test to be almost uniformly most powerful among the competing tests under very general simulation conditions with covariates. This test does not require genotype data to be complete and hence difficult imputation can be avoided. We also discuss how to adjust for the effect of population stratification based on principal components, and use a Shanghai Breast Cancer Study to demonstrate application of the new test.

On Robust Association Testing for Quantitative Traits and Rare Variants

With the advance of sequencing technologies, it has become a routine practice to test for association between a quantitative trait and a set of rare variants (RVs). While a number of RV association tests have been proposed, there is a dearth of studies on the robustness of RV association testing for nonnormal distributed traits, e.g., due to skewness, which is ubiquitous in cohort studies. By extensive simulations, we demonstrate that commonly used RV tests, including sequence kernel association test (SKAT) and optimal unified SKAT (SKAT-O), are not robust to heavy-tailed or right-skewed trait distributions with inflated type I error rates; in contrast, the adaptive sum of powered score (aSPU) test is much more robust. Here we further propose a robust version of the aSPU test, called aSPUr. We conduct extensive simulations to evaluate the power of the tests, finding that for a larger number of RVs, aSPU is often more powerful than SKAT and SKAT-O, owing to its high data-adaptivity. We also compare different tests by conducting association analysis of triglyceride levels using the NHLBI ESP whole-exome sequencing data. The QQ plots for SKAT and SKAT-O were severely inflated (λ = 1.89 and 1.78, respectively), while those for aSPU and aSPUr behaved normally. Due to its relatively high robustness to outliers and high power of the aSPU test, we recommend its use complementary to SKAT and SKAT-O. If there is evidence of inflated type I error rate from the aSPU test, we would recommend the use of the more robust, but less powerful, aSPUr test.

A new structural approach to genomic discovery of disease: example of adult-onset diabetes

This paper reports on an investigation of disease discovery from genomic data, by methods which depart substantially from customary practices found in the investigation of genome-wide association studies. Such data in general are composed of the genomic content from two contrasting phenotypes, e.g., disease versus control populations, and the analysis proceeds under the hypothesis that populational dissimilarities might reveal disease risk alleles. The proposed suite of new methods is in part based on information theory (Shannon in Bell Syst Tech J 27:379-423, 1948a; Bell Syst Tech J 27:623-656, 1948b; Jaynes in Phys Rev 106:620-630, 1957), and strong evidence will be given of the effectiveness of this new approach. The methodology extends naturally and successfully to predicting genomic disposition to disease arising from large collections of weakly contributing genomic loci. Evidence will be advanced that the example of adult-onset diabetes ("type 2 diabetes") is such a candidate disease, and in this case, probably for the first time, it can be demonstrated that disease prediction is possible. Another novel element of this study is the search and identification of potential beneficial genomic loci that may counter a disease. The generality of the methodology suggests that it might extend to other diseases.

PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables

Imputation is widely used for obtaining information about rare variants. However, one issue concerning imputation is the low accuracy of imputed rare variants as the inaccurate imputed rare variants may distort the results of region-based association tests. Therefore, we developed a pre-collapsing imputation method (PreCimp) to improve the accuracy of imputation by using collapsed variables. Briefly, collapsed variables are generated using rare variants in the reference panel, and a new reference panel is constructed by inserting pre-collapsed variables into the original reference panel. Following imputation analysis provides the imputed genotypes of the collapsed variables. We demonstrated the performance of PreCimp on 5,349 genotyped samples using a Korean population specific reference panel including 848 samples of exome sequencing, Affymetrix 5.0, and exome chip. PreCimp outperformed a traditional post-collapsing method that collapses imputed variants after single rare variant imputation analysis. Compared with the results of post-collapsing method, PreCimp approach was shown to relatively increase imputation accuracy about 3.4-6.3% when dosage r² is between 0.6 and 0.8, 10.9-16.1% when dosage r² is between 0.4 and 0.6, and 21.4 ∼ 129.4% when dosage r² is below 0.4.

Transmission and decorrelation methods for detecting rare variants using sequencing data from related individuals

BACKGROUND

Advances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods to detect associations with rare variants are developed for unrelated individuals; however, several methods exist that utilize family studies and could have better power to detect such associations.

METHODS

Using whole genome sequencing data and simulated phenotypes provided by the organizers of the Genetic Analysis Workshop 19 (GAW19), we compared family-based methods that test for associations between rare and common variants with a quantitative trait. This was done using 2 fairly novel methods: family-based association test for rare variants (FBAT-RV), which is a transmission-based method that utilizes the transmission of genetic information from parent to offspring; and Minimum p value Optimized Nuisance parameter Score Test Extended to Relatives (MONSTER), which is a decorrelation method that instead attempts to adjust for relatedness using a regression-based method. We also considered family-based association test linear combination (FBAT-LC) and FBAT-Min P, which are slightly older methods that do not allow for the weighting of rare or common variants, but contrast some of the limitations of FBAT-RV.

RESULTS

MONSTER had much higher overall power than FBAT-RV and FBAT-Min P. Interestingly, FBAT-LC had similar overall power as MONSTER. MONSTER had the highest power for a gene accounting for a larger percent of the phenotypic variance, whereas MONSTER and FBAT-LC both had the highest power for a gene accounting for moderate variance. FBAT-LC had the highest power for a gene accounting for the least variance.

CONCLUSIONS

Based on the simulated data from GAW19, MONSTER and FBAT-LC were the most powerful of the methods assessed. However, there are limitations to each of these methods that should be carefully considered when conducting an analysis of rare variants in related individuals. This emphasizes the need for methods that can incorporate the advantages of each of these methods into 1 family-based association test for rare variants.

The genetic architecture of resistance to virus infection in Drosophila

Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host-pathogen co-evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major-effect loci. Sigma virus resistance involves more genes - we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co-evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major-effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near-Mendelian trait.

Gene-set association tests for next-generation sequencing data

MOTIVATION

Recently, many methods have been developed for conducting rare-variant association studies for sequencing data. These methods have primarily been based on gene-level associations but have not been proven to be as effective as expected. Gene-set-level tests have shown great advantages over gene-level tests in terms of power and robustness, because complex diseases are often caused by multiple genes that comprise of biological gene sets.

RESULTS

Here, we propose several novel gene-set tests that employ rapid and efficient dimensionality reduction. The performance of these tests was investigated using extensive simulations and application to 1058 whole-exome sequences from a Korean population. We identified some known pathways and novel pathways whose rare or common variants are associated with elevated liver enzymes and replicated the results in an independent cohort.

AVAILABILITY AND IMPLEMENTATION

Source R code for our algorithm is freely available at http://statgen.snu.ac.kr/software/QTest

CONTACT

tspark@stats.snu.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Targeted deep resequencing of ALOX5 and ALOX5AP in patients with diabetes and association of rare variants with leukotriene pathways

The aim of the present study was to investigate a possible association between the accumulation of rare coding variants in the genes for arachidonate 5-lipoxygenase (ALOX5) and ALOX5-activating protein (ALOX5AP), and corresponding production of leukotrienes (LTs) in patients with type 2 diabetes mellitus (T2DM) receiving acetylsalicylic therapy. Twenty exons and corresponding introns of the selected genes were resequenced in 303 DNA samples from patients with T2DM using pooled polymerase chain reaction amplification and next-generation sequencing, using an Illumina HiSeq 2000 sequencing system. The observed non-synonymous variants were further confirmed by individual genotyping of DNA samples comprising of all individuals from the original discovery pools. The association between the investigated phenotypes was based on LTB4 and LTE4 concentrations, and the accumulation of rare missense variants (genetic burden) in investigated genes was evaluated using statistical collapsing tests. A total of 10 exonic variants were identified for each resequenced gene, including 5 missense and 5 synonymous variants. The rare missense variants did not exhibit statistically significant differences in the accumulation pattern between the patients with low and high LTs concentrations. As the present study only included patients with T2DM, it is unclear whether the absence of observed association between the accumulation of rare missense variants in investigated genes and LT production is associated with diabetic populations only or may also be applied to other populations.