Testing Untyped Alleles (TUNA)—applications to genome-wide association studies

Neutrophilia in severe asthma is reduced in Ormdl3 overexpressing mice

Genome-wide association studies have linked the ORM (yeast)-like protein isoform 3 (ORMDL3) to asthma severity. Although ORMDL3 is a member of a family that negatively regulates serine palmitoyltransferase (SPT) and thus biosynthesis of sphingolipids, it is still unclear whether ORMDL3 and altered sphingolipid synthesis are causally related to non-Th2 severe asthma associated with a predominant neutrophil inflammation and high interleukin-17 (IL-17) levels. Here, we examined the effects of ORMDL3 overexpression in a preclinical mouse model of allergic lung inflammation that is predominantly neutrophilic and recapitulates many of the clinical features of severe human asthma. ORMDL3 overexpression reduced lung and circulating levels of dihydrosphingosine, the product of SPT. However, the most prominent effect on sphingolipid levels was reduction of circulating S1P. The LPS/OVA challenge increased markers of Th17 inflammation with a predominant infiltration of neutrophils into the lung. A significant decrease of neutrophil infiltration was observed in the Ormdl3 transgenic mice challenged with LPS/OVA compared to the wild type and concomitant decrease in IL-17, that plays a key role in the pathogenesis of neutrophilic asthma. LPS decreased survival of murine neutrophils, which was prevented by co-treatment with S1P. Moreover, S1P potentiated LPS-induced chemotaxis of neutrophil, suggesting that S1P can regulate neutrophil survival and recruitment following LPS airway inflammation. Our findings reveal a novel connection between ORMDL3 overexpression, circulating levels of S1P, IL-17 and neutrophil recruitment into the lung, and questions the potential involvement of ORMDL3 in the pathology, leading to development of severe neutrophilic asthma.

Single-base editing of rs12603332 on chromosome 17q21 with a cytosine base editor regulates ORMDL3 and ATF6α expression

BACKGROUND

Genetic association studies have demonstrated that the SNP rs12603332 located on chromosome 17q21 is highly associated with the risk of the development of asthma.

METHODS

To determine whether SNP rs1260332 is functional in regulating levels of ORMDL3 expression, we used a Cytosine Base Editor (CBE) plasmid DNA or a CBE mRNA to edit the rs12603332 C risk allele to the T non-risk allele in a human lymphocyte cell line (i.e., Jurkat cells) and in primary human CD4 T cells that carry the C risk alleles.

RESULTS

Jurkat cells with the rs12603332 C risk allele expressed significantly higher levels of ORMDL3 mRNA, as well as the ORMDL3 regulated gene ATF6α as assessed by qPCR compared to Jurkat clones with the T non-risk allele. In primary human CD4 T cells, we edited 90 ± 3% of the rs12603332-C risk allele to the T non-risk allele and observed a reduction in ORMDL3 and ATF6α expression. Bioinformatic analysis predicted that the non-risk allele rs12603332-T could be the central element of the E-box binding motif (CANNTG) recognized by the E47 transcription factor. An EMSA assay confirmed the bioinformatics prediction demonstrating that a rs12603332-T containing probe bound to the transcription factor E47 in vitro.

CONCLUSIONS

SNP rs12603332 is functional in regulating the expression of ORMDL3 as well as ORMDL3 regulated gene ATF6α expression. In addition, we demonstrate the use of CBE technology in functionally interrogating asthma-associated SNPs using studies of primary human CD4 cells.

ADRB2 haplotypes and asthma exacerbations in children and young adults: An individual participant data meta-analysis

BACKGROUND

The polymorphism Arg16 in β2 -adrenergic receptor (ADRB2) gene has been associated with an increased risk of exacerbations in asthmatic children treated with long-acting β2 -agonists (LABA). However, it remains unclear whether this increased risk is mainly attributed to this single variant or the combined effect of the haplotypes of polymorphisms at codons 16 and 27.

OBJECTIVE

We assessed whether the haplotype analysis could explain the association between the polymorphisms at codons 16 (Arg16Gly) and 27 (Gln27Glu) in ADRB2 and risk of asthma exacerbations in patients treated with inhaled corticosteroids (ICS) plus LABA.

METHODS

The study was undertaken using data from 10 independent studies (n = 5903) participating in the multi-ethnic Pharmacogenomics in Childhood Asthma (PiCA) consortium. Asthma exacerbations were defined as asthma-related use of oral corticosteroids or hospitalizations/emergency department visits in the past 6 or 12 months prior to the study visit/enrolment. The association between the haplotypes and the risk of asthma exacerbations was performed per study using haplo.stats package adjusted for age and sex. Results were meta-analysed using the inverse variance weighting method assuming random-effects.

RESULTS

In subjects treated with ICS and LABA (n = 832, age: 3-21 years), Arg16/Gln27 versus Gly16/Glu27 (OR: 1.40, 95% CI: 1.05-1.87, I2  = 0.0%) and Arg16/Gln27 versus Gly16/Gln27 (OR: 1.43, 95% CI: 1.05-1.94, I2  = 0.0%), but not Gly16/Gln27 versus Gly16/Glu27 (OR: 0.99, 95% CI: 0.71-1.39, I2  = 0.0%), were significantly associated with an increased risk of asthma exacerbations. The sensitivity analyses indicated no significant association between the ADRB2 haplotypes and asthma exacerbations in the other treatment categories, namely as-required short-acting β2 -agonists (n = 973), ICS monotherapy (n = 2623), ICS plus leukotriene receptor antagonists (LTRA; n = 338), or ICS plus LABA plus LTRA (n = 686).

CONCLUSION AND CLINICAL RELEVANCE

The ADRB2 Arg16 haplotype, presumably mainly driven by the Arg16, increased the risk of asthma exacerbations in patients treated with ICS plus LABA. This finding could be beneficial in ADRB2 genotype-guided treatment which might improve clinical outcomes in asthmatic patients.

Machine learning based disease prediction from genotype data

Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.

ORMDL3 overexpression facilitates FcεRI-mediated transcription of proinflammatory cytokines and thapsigargin-mediated PERK phosphorylation in RBL-2H3 cells

Introduction

The chromosomal region 17q21 harbors the human orosomucoid‐like 3 (ORMDL3) gene and has been linked to asthma and other inflammatory diseases. ORMDL3 is involved in the unfolded protein response (UPR), lipid metabolism, and inflammatory reactions. We investigated the effects of ORMDL3 overexpression in RBL‐2H3 cells to determine the contribution of ORMDL3 to inflammatory disease development.

Methods

We generated ORMDL3 stably overexpressing RBL‐2H3 cells to assess degranulation, transcriptional upregulation of interleukin‐4 (IL‐4), tumor necrosis factor‐α (TNF‐α), monocyte chemoattractant protein‐1 (MCP‐1), and mitogen‐activated protein kinase (MAPK) phosphorylation via FcεRI. In addition, we examined the effects of ORMDL3 overexpression on thapsigargin (TG)‐mediated proinflammatory cytokine transcription and UPR by monitoring MAPK, protein kinase‐like endoplasmic reticulum kinase (PERK), and inositol‐requiring enzyme 1 (IRE1) phosphorylation.

Results

Overexpression of ORMDL3 enhanced IL‐4, TNF‐α, and MCP‐1 expression after FcεRI cross‐linking, whereas the sphingosine‐1‐phosphate (S1P) agonist FTY720 suppressed this enhancement. There was no significant difference in degranulation and MAPK phosphorylation via FcεRI‐mediated activation between vector‐transfected and ORMDL3‐overexpressing cells. ORMDL3 overexpression accelerated TG‐mediated PERK phosphorylation, while MAPK phosphorylation and proinflammatory cytokine expression showed no significant changes in ORMDL3‐overexpressing cells.

Conclusions

Our findings suggest that ORMDL3 plays an important role in regulating proinflammatory cytokine expression via the S1P pathway and selectively affects the UPR pathway in mast cells.

Innate IL-23/Type 17 immune responses mediate the effect of the 17q21 locus on childhood asthma

BACKGROUND

Several childhood asthma risk loci that relate to immune function have been identified by genome-wide association studies (GWAS), but the underlying mechanisms remain unknown.

OBJECTIVE

Here, we examined whether perturbed innate immune responses mediate the association between known genetic risk variants and development of childhood asthma.

METHODS

Peripheral blood mononuclear cells from 336 six-month-old infants from the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC₂₀₀₀ ) cohort were stimulated in vitro with six different innate ligands (LPS, CpG, poly(I:C), R848, HDMAPP and aluminium hydroxide together with low levels of LPS) followed by quantification of 18 released cytokines and chemokines 40 h after the stimulations. The innate immune response profiles were decomposed by principal component (PC) analysis, and PC1-5 were used in mediation analyses of the effect of 25 known genetic risk variants on childhood asthma until age 7.

RESULTS

The effects of two variants from the 17q21 locus (rs7216389, rs2305480) on asthma and exacerbation risk were significantly mediated by immune parameters induced in response to ligands mimicking intracellular colonization; bacterial DNA (CpG) and double-stranded viral RNA (poly(I:C)). The Th17 and innate lymphoid cell type 3-amplifying cytokine IL-23 was the most prominent cytokine involved.

CONCLUSION

The 17q21 effect on childhood asthma and exacerbations was partly mediated by deregulation of IL-23 in response to intracellular microbial ligands, which may suggest ineffective clearance of intracellular pathogens in the lungs.

Basophil activation test: Assay precision and BI 1002494 SYK inhibition in healthy and mild asthmatics

BACKGROUND

Application of basophil activation test (BAT) in clinical trials requires assay validity. Whether assay variability differs between healthy and asthmatic subjects is mostly unknown. This study compares basophil stimulation using blood from healthy and asthmatic subjects with or without inhibition of spleen tyrosine kinase (SYK).

METHODS

Whole blood of healthy and mild asthmatic subjects was stimulated with anti-dinitrophenyl (DNP) IgE/DNP bovine serum albumin and anti-IgE. Basophil activation was detected by CD63 and CD203c expression. CD63 expression levels were compared with serum IgE levels. Three operators repeated experiments with three subjects each from both groups at 3 days to observe assay precision. The effect of the SYK inhibitor BI 1002494 was assessed in BAT for both healthy and asthmatic subjects.

RESULTS

BAT was reproducible in both groups. Acceptance criteria of <25% CV were mostly fulfilled. Stimulation with anti-DNP (p < 0.001, r = -0.80) but not anti-IgE (p = 0.74, r = 0.05) was related to serum IgE with levels > 200 IU/ml limiting anti-DNP stimulation. BI 1002494 IC₅₀ values were 497 nM and 1080 nM in healthy and 287 nM and 683 nM in asthmatics for anti-DNP and anti-IgE stimulation, respectively.

CONCLUSION

BAT, performed with blood from healthy or asthmatic subjects, is a robust test for the measurement of a physiological response in clinical trials. Blood from asthmatic donors with serum IgE > 200 IU/ml is less feasible when using anti-DNP stimulation. SYK inhibition was not affected by disease status.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies.

A Review of Integrative Imputation for Multi-Omics Datasets

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.

Genome-wide interaction study of early-life smoking exposure on time-to-asthma onset in childhood

BACKGROUND

Asthma, a heterogeneous disease with variable age of onset, results from the interplay between genetic and environmental factors. Early-life tobacco smoke (ELTS) exposure is a major asthma risk factor. Only a few genetic loci have been reported to interact with ELTS exposure in asthma.

OBJECTIVE

Our aim was to identify new loci interacting with ELTS exposure on time-to-asthma onset (TAO) in childhood.

METHODS

We conducted genome-wide interaction analyses of ELTS exposure on time-to-asthma onset in childhood in five European-ancestry studies (totalling 8273 subjects) using Cox proportional-hazard model. The results of all five genome-wide analyses were meta-analysed.

RESULTS

The 13q21 locus showed genome-wide significant interaction with ELTS exposure (P = 4.3 × 10-8 for rs7334050 within KLHL1 with consistent results across the five studies). Suggestive interactions (P < 5 × 10-6 ) were found at three other loci: 20p12 (rs13037508 within MACROD2; P = 4.9 × 10-7 ), 14q22 (rs7493885 near NIN; P = 2.9 × 10-6 ) and 2p22 (rs232542 near CYP1B1; P = 4.1 × 10-6 ). Functional annotations and the literature showed that the lead SNPs at these four loci influence DNA methylation in the blood and are located nearby CpG sites reported to be associated with exposure to tobacco smoke components, which strongly support our findings.

CONCLUSIONS AND CLINICAL RELEVANCE

We identified novel candidate genes interacting with ELTS exposure on time-to-asthma onset in childhood. These genes have plausible biological relevance related to tobacco smoke exposure. Further epigenetic and functional studies are needed to confirm these findings and to shed light on the underlying mechanisms.

Innate immunity in allergy

Innate immune system quickly responds to invasion of microbes and foreign substances through the extracellular and intracellular sensing receptors, which recognize distinctive molecular and structural patterns. The recognition of innate immune receptors leads to the induction of inflammatory and adaptive immune responses by activating downstream signaling pathways. Allergy is an immune-related disease and results from a hypersensitive immune response to harmless substances in the environment. However, less is known about the activation of innate immunity during exposure to allergens. New insights into the innate immune system by sensors and their signaling cascades provide us with more important clues and a framework for understanding allergy disorders. In this review, we will focus on recent advances in the innate immune sensing system.

Sparse Convolutional Denoising Autoencoders for Genotype Imputation

Genotype imputation, where missing genotypes can be computationally imputed, is an essential tool in genomic analysis ranging from genome wide associations to phenotype prediction. Traditional genotype imputation methods are typically based on haplotype-clustering algorithms, hidden Markov models (HMMs), and statistical inference. Deep learning-based methods have been recently reported to suitably address the missing data problems in various fields. To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA) to impute missing genotypes. We constructed the SCDA model using a convolutional layer that can extract various correlation or linkage patterns in the genotype data and applying a sparse weight matrix resulted from the L₁ regularization to handle high dimensional data. We comprehensively evaluated the performance of the SCDA model in different scenarios for genotype imputation on the yeast and human genotype data, respectively. Our results showed that SCDA has strong robustness and significantly outperforms popular reference-free imputation methods. This study thus points to another novel application of deep learning models for missing data imputation in genomic studies.

Endosome and Golgi-associated degradation (EGAD) of membrane proteins regulates sphingolipid metabolism

Cellular homeostasis requires the ubiquitin-dependent degradation of membrane proteins. This was assumed to be mediated exclusively either by endoplasmic reticulum-associated degradation (ERAD) or by endosomal sorting complexes required for transport (ESCRT)-dependent lysosomal degradation. We identified in Saccharomyces cerevisiae an additional pathway that selectively extracts membrane proteins at Golgi and endosomes for degradation by cytosolic proteasomes. One endogenous substrate of this endosome and Golgi-associated degradation pathway (EGAD) is the ER-resident membrane protein Orm2, a negative regulator of sphingolipid biosynthesis. Orm2 degradation is initiated by phosphorylation, which triggers its ER export. Once on Golgi and endosomes, Orm2 is poly-ubiquitinated by the membrane-embedded "Defective in SREBP cleavage" (Dsc) ubiquitin ligase complex. Cdc48/VCP then extracts ubiquitinated Orm2 from membranes, which is tightly coupled to the proteasomal degradation of Orm2. Thereby, EGAD prevents the accumulation of Orm2 at the ER and in post-ER compartments and promotes the controlled de-repression of sphingolipid biosynthesis. Thus, the selective degradation of membrane proteins by EGAD contributes to proteostasis and lipid homeostasis in eukaryotic cells.

Association of CARD10 rs6000782 and TNF rs1799724 variants with paediatric-onset autoimmune hepatitis

Although the pathogenesis of paediatric-onset autoimmune hepatitis (pAIH) remains incompletely understood, genetic variants and environmental factors are known to be involved. Caspase recruitment domain family member 10 (CARD10) is a scaffold protein that participates in a complex pathway activating nuclear factor kappa-B (NFκB) and tumour necrosis factor alpha (TNF-α). This study aimed to investigate the association of CARD10 rs6000782 (g.37928186A > C) and TNF gene promoter rs1799724 (c.-1037C > T) variants with pAIH susceptibility in a cohort of Egyptian children. The research was also extended to assess the relationship of these variants with levels of NFκB-p65 and TNF-α. Fifty-six pAIH patients and 44 age- and sex-matched healthy controls were included. Variant genotyping was performed by polymerase chain reaction (PCR). Serum NFκB-p65 and TNF-α levels were measured using enzyme-linked immunosorbent assays (ELISAs). rs6000782 C and rs1799724 T alleles, separate or in combination, were significantly increased in pAIH patients compared to controls. Serum levels of NFκB-p65 and TNF-α were higher in pAIH differentiating both groups. Moreover, the recessive model of rs6000782 revealed a significant association with the levels of both NFκB-p65 and TNF-α. In conclusion, rs6000782 and rs1799724 variants are potential genetic risk factors for pAIH predisposition, with the former affecting NFκB-p65 and TNF-α levels. Overall, the inflammatory cascade was associated with the degree of liver cell destruction. Clinically, screening and genetic counselling are recommended for relatives of pAIH patients.

Genetic risk factors for the development of pulmonary disease identified by genome-wide association

Chronic respiratory diseases are a major cause of morbidity and mortality. Asthma and chronic obstructive pulmonary disease (COPD) combined affect over 500 million people worldwide. While environmental factors are important in disease progression, asthma and COPD have long been known to be heritable with genetic components playing an important role in the risk of developing disease. Identification of genetic variation contributing to disease progression is important for a number of reasons including identification of risk alleles, understanding underlying disease mechanisms and development of novel therapies. Genome-wide association studies (GWAS) have been successful in identifying many loci associated with lung function, COPD and asthma. In recent years, meta-analyses and improved imputation have facilitated the growth of GWAS in terms of numbers of subjects and the number of single nucleotide polymorphisms (SNP) that can be interrogated. As a consequence, there has been a significant increase in the number of signals associated with asthma, COPD and lung function. SNP that have shown association with lung function reassuringly show a significant overlap with SNP associated with COPD giving a glimpse at pathways that may be involved in COPD mechanisms including genes in, for example, developmental pathways. In asthma, association signals are often in or near genes involved in both adaptive and innate immune response pathways, epithelial cell homeostasis and airway structural changes. The challenges now are translating these genetic signals into a new understanding of lung biology, understanding how variants impact health and disease and how they may provide opportunities for therapeutic intervention.

Genotype Imputation from Large Reference Panels

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

The potential of anti-infectives and immunomodulators as therapies for asthma and asthma exacerbations

Asthma is responsible for approximately 25,000 deaths annually in Europe despite available medicines that maintain asthma control and reduce asthma exacerbations. Better treatments are urgently needed for the control of chronic asthma and reduction in asthma exacerbations, the major cause of asthma mortality. Much research spanning >20 years shows a strong association between microorganisms including pathogens in asthma onset, severity and exacerbation, yet with the exception of antibiotics, few treatments are available that specifically target the offending pathogens. Recent insights into the microbiome suggest that modulating commensal organisms within the gut or lung may also be a possible way to treat/prevent asthma. The European Academy of Allergy & Clinical Immunology Task Force on Anti-infectives in Asthma was initiated to investigate the potential of anti-infectives and immunomodulators in asthma. This review provides a concise summary of the current literature and aimed to identify and address key questions that concern the use of anti-infectives and both microbe- and host-based immunomodulators and their feasibility for use in asthma.

The Molecular Revolution in Cutaneous Biology: The Era of Genome-Wide Association Studies and Statistical, Big Data, and Computational Topics

The investigation of biological systems involving all organs of the body including the skin is in era of big data. This requires heavy-duty computational tools, and novel statistical methods. Microarrays have allowed the interrogation of thousands of common genetic markers in thousands of individuals from the same population (termed genome wide association studies or GWAS) to reveal common variation associated with disease or phenotype. These markers are usually single nucleotide polymorphisms (SNPs) that are relatively common in the population. In the case of dermatological diseases such as alopecia areata, vitiligo, psoriasis and atopic dermatitis, common variants have been identified that are associated with disease, and these provide insights into biological pathways and reveal possible novel drug targets. Other skin phenotypes such as acne, color and skin cancers are also being investigated with GWAS. Analyses of such large GWAS datasets require a consideration of a number of statistical issues including the testing of multiple markers, population substructure, and ultimately a requirement for replication. There are also issues regarding the missing heritability of disease that cannot be entirely explained with current GWAS approaches. Next generation sequencing technologies such as exome and genome sequencing of similar patient cohorts will reveal additional variants contributing to disease susceptibility. However, the data generated with these approaches will be orders of magnitude greater than that those generated with arrays, with concomitant challenges in the identification of disease causing variants.

ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase

ORMDL proteins are believed to be negative regulators of serine palmitoyltransferase (SPT), which catalyzes the first and rate limiting step in sphingolipid (SL) de novo synthesis. Several single-nucleotide polymorphisms (SNPs) that are close to the ORMDL3 locus have been reported to increase ORMDL3 expression and to be associated with an elevated risk for early childhood asthma; however, the direct effect of ORMDL3 expression on SPT activity and its link to asthma remains elusive. In this study, we investigated whether ORMDL3 expression is associated with changes in SPT activity and total SL levels. Ormdl3-knockout (Ormdl3^-/-) and transgenic (Ormdl3^Tg/wt) mice were generated to study the effect of ORMDL3 on total SL levels in plasma and tissues. Cellular SPT activity was measured in mouse embryonic fibroblasts from Ormdl3^-/- mice, as well as in HEK293 cells in which ORMDL3 was overexpressed and silenced. Furthermore, we analyzed the association of the reported ORMDL3 asthma SNPs with plasma sphingoid bases in a population-based cohort of 971 individuals. Total C₁₈-long chain bases were not significantly altered in the plasma and tissues of Ormdl3^-/- mice, whereas C₁₈-sphinganine showed a small and significant increase in plasma, lung, and liver tissues. Mouse embryonic fibroblast cells from Ormdl3^-/- mice did not show an altered SPT activity compared with Ormdl3^+/- and Ormdl3^+/+ mice. Overexpression or knockdown of ORMDL3 in HEK293 cells did not alter SPT activity; however, parallel knockdown of all 3 ORMDL isoforms increased enzyme activity significantly. A significant association of the annotated ORMDL3 asthma SNPs with plasma long-chain sphingoid base levels could not be confirmed. ORMDL3 expression levels seem not to be directly associated with changes in SPT activity. ORMDL3 might influence de novo sphingolipid metabolism downstream of SPT.-Zhakupova, A., Debeuf, N., Krols, M., Toussaint, W., Vanhoutte, L., Alecu, I., Kutalik, Z., Vollenweider, P., Ernst, D., von Eckardstein, A., Lambrecht, B. N., Janssens, S., Hornemann, T. ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase.

Proper Use of Allele-Specific Expression Improves Statistical Power for cis-eQTL Mapping with RNA-Seq Data

Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into cis- and trans-acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish cis-eQTLs from trans-eQTLs and, more importantly, enhance cis-eQTL mapping. However, assessing the cis-effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which cannot be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated cis-effect and reduce the power for detecting a cis-eQTL. In this article, we provide a maximum-likelihood framework for cis-eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project.

Genetic risk factors for the development of allergic disease identified by genome-wide association

An increasing proportion of the worldwide population is affected by allergic diseases such as allergic rhinitis (AR), atopic dermatitis (AD) and allergic asthma and improved treatment options are needed particularly for severe, refractory disease. Allergic diseases are complex and development involves both environmental and genetic factors. Although the existence of a genetic component for allergy was first described almost 100 years ago, progress in gene identification has been hindered by lack of high throughput technologies to investigate genetic variation in large numbers of subjects. The development of Genome-Wide Association Studies (GWAS), a hypothesis-free method of interrogating large numbers of common variants spanning the entire genome in disease and non-disease subjects has revolutionised our understanding of the genetics of allergic disease. Susceptibility genes for asthma, AR and AD have now been identified with confidence, suggesting there are common and distinct genetic loci associated with these diseases, providing novel insights into potential disease pathways and mechanisms. Genes involved in both adaptive and innate immune mechanisms have been identified, notably including multiple genes involved in epithelial function/secretion, suggesting that the airway epithelium may be particularly important in asthma. Interestingly, concordance/discordance between the genetic factors driving allergic traits such as IgE levels and disease states such as asthma have further supported the accumulating evidence for heterogeneity in these diseases. While GWAS have been useful and continue to identify novel genes for allergic diseases through increased sample sizes and phenotype refinement, future approaches will integrate analyses of rare variants, epigenetic mechanisms and eQTL approaches, leading to greater insight into the genetic basis of these diseases. Gene identification will improve our understanding of disease mechanisms and generate potential therapeutic opportunities.

Jackknife-based gene-gene interactiontests for untyped SNPs

BACKGROUND

Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy.

RESULTS

In this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest.

CONCLUSIONS

As demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error.

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts

Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts.

Results: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources.

Availability and implementation: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix.

Contact:dlee4@vcu.edu

Supplementary information:Supplementary Data are available at Bioinformatics online.

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information

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An eQTL-based gene–gene co-regulation network was constructed.

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We adopted a random walk with restart (RWR) algorithm to mine for Alzheimer-disease related genes.

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The integrated HPRD PPI and GGCRN network had faster convergence than using HPRD PPI alone.

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The integrated network also revealed new disease-related genes.

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein–protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene–gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Dusp5 negatively regulates IL-33-mediated eosinophil survival and function

Mitogen-activated protein kinase (MAPK) activation controls diverse cellular functions including cellular survival, proliferation, and apoptosis. Tuning of MAPK activation is counter-regulated by a family of dual-specificity phosphatases (DUSPs). IL-33 is a recently described cytokine that initiates Th2 immune responses through binding to a heterodimeric IL-33Rα (ST2L)/IL-1α accessory protein (IL-1RAcP) receptor that coordinates activation of ERK and NF-κB pathways. We demonstrate here that DUSP5 is expressed in eosinophils, is upregulated following IL-33 stimulation and regulates IL-33 signaling. Dusp5(-/-) mice have prolonged eosinophil survival and enhanced eosinophil effector functions following infection with the helminth Nippostrongylus brasiliensis. IL-33-activated Dusp5(-/-) eosinophils exhibit increased cellular ERK1/2 activation and BCL-XL expression that results in enhanced eosinophil survival. In addition, Dusp5(-/-) eosinophils demonstrate enhanced IL-33-mediated activation and effector functions. Together, these data support a role for DUSP5 as a novel negative regulator of IL-33-dependent eosinophil function and survival.

Genetic variation in HTR4 and lung function: GWAS follow-up in mouse

Human genome-wide association studies (GWASs) have identified numerous associations between single nucleotide polymorphisms (SNPs) and pulmonary function. Proving that there is a causal relationship between GWAS SNPs, many of which are noncoding and without known functional impact, and these traits has been elusive. Furthermore, noncoding GWAS-identified SNPs may exert trans-regulatory effects rather than impact the proximal gene. Noncoding variants in 5-hydroxytryptamine (serotonin) receptor 4 (HTR4) are associated with pulmonary function in human GWASs. To gain insight into whether this association is causal, we tested whether Htr4-null mice have altered pulmonary function. We found that HTR4-deficient mice have 12% higher baseline lung resistance and also increased methacholine-induced airway hyperresponsiveness (AHR) as measured by lung resistance (27%), tissue resistance (48%), and tissue elastance (30%). Furthermore, Htr4-null mice were more sensitive to serotonin-induced AHR. In models of exposure to bacterial lipopolysaccharide, bleomycin, and allergic airway inflammation induced by house dust mites, pulmonary function and cytokine profiles in Htr4-null mice differed little from their wild-type controls. The findings of altered baseline lung function and increased AHR in Htr4-null mice support a causal relationship between genetic variation in HTR4 and pulmonary function identified in human GWAS.

Early life environmental predictors of asthma age-of-onset

Prevention strategies that delay the onset of asthma may improve clinical outcomes. To identify early life environmental exposures associated with asthma age-of-onset and potential genetic modifiers of these exposures, we studied 1085 subjects with physician-diagnosed asthma and disease onset at or after age two. Subjects reported retrospectively on their exposure to 17 environmental factors before the age of two. The presence of individual or combinations of these early life exposures was then tested for association with variation in asthma age-of-onset. For exposures significantly associated with age-of-onset, we tested if 26 single nucleotide polymorphisms (SNP) with an established association with allergic disease significantly modified the effect of the exposure. Five environmental exposures were significantly associated with variation in asthma age-of-onset after correction for multiple testing: carpet at home (P = 6 × 10⁻⁵), a serious chest illness (P = 10⁻⁴), father a cigarette smoker (P = 6 × 10⁻⁴) and direct exposure to father's smoking (P = 3 × 10⁻⁴). Individuals with early childhood asthma onset, between the ages of two and six, were 1.4-fold (CI 1.1–1.9) more likely to report having lived in a house with carpet and 2.1-fold (CI 1.3–3.5) more likely to report suffering a serious chest illness before the age of two, than asthmatics with later disease onset. We further found these individual risks to increase to 3.2-fold (CI 1.7–6.0) if carpet exposure and suffering a serious chest illness co-occurred before age two. Paternal smoking exposures were less likely to be reported by asthmatics with early when compared to later disease onset (OR 0.5, CI 0.3–0.7). There were no significant SNP interactions with these environmental exposures after correction for multiple testing. Our results suggest that disease onset in individuals at a high-risk of developing asthma can potentially be delayed by avoiding exposure to carpet at home and preventing serious chest illnesses during the first 2 years of life.

A new genotype imputation method with tolerance to high missing rate and rare variants

We report a novel algorithm, iBLUP, to impute missing genotypes by simultaneously and comprehensively using identity by descent and linkage disequilibrium information. The simulation studies showed that the algorithm exhibited drastically tolerance to high missing rate, especially for rare variants than other common imputation methods, e.g. BEAGLE and fastPHASE. At a missing rate of 70%, the accuracy of BEAGLE and fastPHASE dropped to 0.82 and 0.74 respectively while iBLUP retained an accuracy of 0.95. For minor allele, the accuracy of BEAGLE and fastPHASE decreased to -0.1 and 0.03, while iBLUP still had an accuracy of 0.61.We implemented the algorithm in a publicly available software package also named iBLUP. The application of iBLUP for processing real sequencing data in an outbred pig population was demonstrated.

Imputation without doing imputation: a new method for the detection of non-genotyped causal variants

Genome-wide association studies allow detection of non-genotyped disease-causing variants through testing of nearby genotyped SNPs. This approach may fail when there are no genotyped SNPs in strong LD with the causal variant. Several genotyped SNPs in weak LD with the causal variant may, however, considered together, provide equivalent information. This observation motivates popular but computationally intensive approaches based on imputation or haplotyping. Here we present a new method and accompanying software designed for this scenario. Our approach proceeds by selecting, for each genotyped "anchor" SNP, a nearby genotyped "partner" SNP, chosen via a specific algorithm we have developed. These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test. In simulations, our method captures much of the signal captured by imputation, while taking a fraction of the time and disc space, and generating a smaller number of false-positives. We apply our method to a case/control study of severe malaria genotyped using the Affymetrix 500K array. Previous analysis showed that fine-scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome-wide significance levels. Our method also increases the signal of association from P ≈ 2 × 10⁻⁶ to P ≈ 6 × 10⁻¹¹. Our method thus, in some cases, eliminates the need for more complex methods such as sequencing and imputation, and provides a useful additional test that may be used to identify genetic regions of interest.

Statistical testing of shared genetic control for potentially related traits

Integration of data from genome‐wide single nucleotide polymorphism (SNP) association studies of different traits should allow researchers to disentangle the genetics of potentially related traits within individually associated regions. Formal statistical colocalisation testing of individual regions requires selection of a set of SNPs summarising the association in a region. We show that the SNP selection method greatly affects type 1 error rates, with published studies having used methods expected to result in substantially inflated type 1 error rates. We show that either avoiding variable selection and instead testing the most informative principal components or integrating over variable selection using Bayesian model averaging can help control type 1 error rates. Application to data from Graves' disease and Hashimoto's thyroiditis reveals a common genetic signature across seven regions shared between the diseases, and indicates that in five of six regions associated with Graves' disease and not Hashimoto's thyroiditis, this more likely reflects genuine absence of association with the latter rather than lack of power. Our examination, by simulation, of the performance of colocalisation tests and associated software will foster more widespread adoption of formal colocalisation testing. Given the increasing availability of large expression and genetic association datasets from disease‐relevant tissue and purified cell populations, coupled with identification of regulatory sequences by projects such as ENCODE, colocalisation analysis has the potential to reveal both shared genetic signatures of related traits and causal disease genes and tissues.

Effects of geohelminth infection and age on the associations between allergen-specific IgE, skin test reactivity and wheeze: a case-control study

Background

Most childhood asthma in poor populations in Latin America is not associated with aeroallergen sensitization, an observation that could be explained by the attenuation of atopy by chronic helminth infections or effects of age.

Objective

To explore the effects of geohelminth infections and age on atopy, wheeze, and the association between atopy and wheeze.

Methods

A case-control study was done in 376 subjects (149 cases and 227 controls) aged 7–19 years living in rural communities in Ecuador. Wheeze cases, identified from a large cross-sectional survey, had recent wheeze and controls were a random sample of those without wheeze. Atopy was measured by the presence of allergen-specific IgE (asIgE) and skin prick test (SPT) responses to house dust mite and cockroach. Geohelminth infections were measured in stools and anti-Ascaris IgE in plasma.

Results

The fraction of recent wheeze attributable to anti-Ascaris IgE was 45.9%, while those for SPT and asIgE were 10.0% and 10.5% respectively. The association between atopy and wheeze was greater in adolescents than children. Although Anti-Ascaris IgE was strongly associated with wheeze (adj. OR 2.24 (95% CI 1.33–3.78, P = 0.003) and with asIgE (adj. OR 5.34, 95% CI 2.49–11.45, P < 0.001), the association with wheeze was independent of asIgE. There was some evidence that the association between atopy and wheeze was greater in uninfected subjects compared with those with active geohelminth infections.

Conclusions and clinical relevance

Atopy to house dust mite and cockroach explained few wheeze cases in our study population, while the presence of anti-Ascaris IgE was an important risk factor. Our data provided only limited evidence that active geohelminth infections attenuated the association between atopy and wheeze in endemic areas or that age modified this association. The role of allergic sensitization to Ascaris in the development of wheeze, independent of atopy, requires further investigation.

Genotype imputation in a coalescent model with infinitely-many-sites mutation

Empirical studies have identified population-genetic factors as important determinants of the properties of genotype-imputation accuracy in imputation-based disease association studies. Here, we develop a simple coalescent model of three sequences that we use to explore the theoretical basis for the influence of these factors on genotype-imputation accuracy, under the assumption of infinitely-many-sites mutation. Employing a demographic model in which two populations diverged at a given time in the past, we derive the approximate expectation and variance of imputation accuracy in a study sequence sampled from one of the two populations, choosing between two reference sequences, one sampled from the same population as the study sequence and the other sampled from the other population. We show that, under this model, imputation accuracy-as measured by the proportion of polymorphic sites that are imputed correctly in the study sequence-increases in expectation with the mutation rate, the proportion of the markers in a chromosomal region that are genotyped, and the time to divergence between the study and reference populations. Each of these effects derives largely from an increase in information available for determining the reference sequence that is genetically most similar to the sequence targeted for imputation. We analyze as a function of divergence time the expected gain in imputation accuracy in the target using a reference sequence from the same population as the target rather than from the other population. Together with a growing body of empirical investigations of genotype imputation in diverse human populations, our modeling framework lays a foundation for extending imputation techniques to novel populations that have not yet been extensively examined.

Family-based association tests using genotype data with uncertainty

Family-based association studies have been widely used to identify association between diseases and genetic markers. It is known that genotyping uncertainty is inherent in both directly genotyped or sequenced DNA variations and imputed data in silico. The uncertainty can lead to genotyping errors and missingness and can negatively impact the power and Type I error rates of family-based association studies even if the uncertainty is independent of disease status. Compared with studies using unrelated subjects, there are very few methods that address the issue of genotyping uncertainty for family-based designs. The limited attempts have mostly been made to correct the bias caused by genotyping errors. Without properly addressing the issue, the conventional testing strategy, i.e. family-based association tests using called genotypes, can yield invalid statistical inferences. Here, we propose a new test to address the challenges in analyzing case-parents data by using calls with high accuracy and modeling genotype-specific call rates. Our simulations show that compared with the conventional strategy and an alternative test, our new test has an improved performance in the presence of substantial uncertainty and has a similar performance when the uncertainty level is low. We also demonstrate the advantages of our new method by applying it to imputed markers from a genome-wide case-parents association study.

Genotype imputation for genome-wide association studies

In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases. Genotype imputation has been used widely in the analysis of GWA studies to boost power, fine-map associations and facilitate the combination of results across studies using meta-analysis. This Review describes the details of several different statistical methods for imputing genotypes, illustrates and discusses the factors that influence imputation performance, and reviews methods that can be used to assess imputation performance and test association at imputed SNPs.

GATES: a rapid and powerful gene-based association test using extended Simes procedure

The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.

Analysis of untyped SNPs: maximum likelihood and imputation methods

Analysis of untyped single nucleotide polymorphisms (SNPs) can facilitate the localization of disease-causing variants and permit meta-analysis of association studies with different genotyping platforms. We present two approaches for using the linkage disequilibrium structure of an external reference panel to infer the unknown value of an untyped SNP from the observed genotypes of typed SNPs. The maximum-likelihood approach integrates the prediction of untyped genotypes and estimation of association parameters into a single framework and yields consistent and efficient estimators of genetic effects and gene-environment interactions with proper variance estimators. The imputation approach is a two-stage strategy, which first imputes the untyped genotypes by either the most likely genotypes or the expected genotype counts and then uses the imputed values in a downstream association analysis. The latter approach has proper control of type I error in single-SNP tests with possible covariate adjustments even when the reference panel is misspecified; however, type I error may not be properly controlled in testing multiple-SNP effects or gene-environment interactions. In general, imputation yields biased estimators of genetic effects and gene-environment interactions, and the variances are underestimated. We conduct extensive simulation studies to compare the bias, type I error, power, and confidence interval coverage between the maximum likelihood and imputation approaches in the analysis of single-SNP effects, multiple-SNP effects, and gene-environment interactions under cross-sectional and case-control designs. In addition, we provide an illustration with genome-wide data from the Wellcome Trust Case-Control Consortium (WTCCC) [2007].

Expanding the range of ZNF804A variants conferring risk of psychosis

A trio of genome-wide association studies recently reported sequence variants at three loci to be significantly associated with schizophrenia. No sequence polymorphism had been unequivocally (P<5 × 10(-8)) associated with schizophrenia earlier. However, one variant, rs1344706[T], had come very close. This polymorphism, located in an intron of ZNF804A, was reported to associate with schizophrenia with a P-value of 1.6 × 10(-7), and with psychosis (schizophrenia plus bipolar disorder) with a P-value of 1.0 × 10(-8). In this study, using 5164 schizophrenia cases and 20,709 controls, we replicated the association with schizophrenia (odds ratio OR = 1.08, P = 0.0029) and, by adding bipolar disorder patients, we also confirmed the association with psychosis (added N = 609, OR = 1.09, P = 0.00065). Furthermore, as it has been proposed that variants such as rs1344706[T]-common and with low relative risk-may also serve to identify regions harboring less common, higher-risk susceptibility alleles, we searched ZNF804A for large copy number variants (CNVs) in 4235 psychosis patients, 1173 patients with other psychiatric disorders and 39,481 controls. We identified two CNVs including at least part of ZNF804A in psychosis patients and no ZNF804A CNVs in controls (P = 0.013 for association with psychosis). In addition, we found a ZNF804A CNV in an anxiety patient (P = 0.0016 for association with the larger set of psychiatric disorders).

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta-analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome-wide SNP data or smaller amounts of data typical in fine-mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies.

Bayesian epistasis association mapping via SNP imputation

Genetic mutations may interact to increase the risk of human complex diseases. Mapping of multiple interacting disease loci in the human genome has recently shown promise in detecting genes with little main effects. The power of interaction association mapping, however, can be greatly influenced by the set of single nucleotide polymorphism (SNP) genotyped in a case-control study. Previous imputation methods only focus on imputation of individual SNPs without considering their joint distribution of possible interactions. We present a new method that simultaneously detects multilocus interaction associations and imputes missing SNPs from a full Bayesian model. Our method treats both the case-control sample and the reference data as random observations. The output of our method is the posterior probabilities of SNPs for their marginal and interacting associations with the disease. Using simulations, we show that the method produces accurate and robust imputation with little overfitting problems. We further show that, with the type I error rate maintained at a common level, SNP imputation can consistently and sometimes substantially improve the power of detecting disease interaction associations. We use a data set of inflammatory bowel disease to demonstrate the application of our method.

Methods: genetic epidemiology

Given the potential benefits of gene identification in psychiatry, genetic epidemiology has become a mainstream discipline within the field. This article discusses the main tools for gene discovery. The focus is on the designs and analytic approaches for each of these methods. Because most gene discovery has now moved to genetic association studies, and most recently to genome-wide association studies, the focus is on methods for this design. Also highlighted are the current challenges of genetic epidemiology as a prelude to future approaches that may be applied to psychiatric disorders in the coming years.

USING LINEAR PREDICTORS TO IMPUTE ALLELE FREQUENCIES FROM SUMMARY OR POOLED GENOTYPE DATA

Recently-developed genotype imputation methods are a powerful tool for detecting untyped genetic variants that affect disease susceptibility in genetic association studies. However, existing imputation methods require individual-level genotype data, whereas in practice it is often the case that only summary data are available. For example this may occur because, for reasons of privacy or politics, only summary data are made available to the research community at large; or because only summary data are collected, as in DNA pooling experiments. In this article, we introduce a new statistical method that can accurately infer the frequencies of untyped genetic variants in these settings, and indeed substantially improve frequency estimates at typed variants in pooling experiments where observations are noisy. Our approach, which predicts each allele frequency using a linear combination of observed frequencies, is statistically straight-forward, and related to a long history of the use of linear methods for estimating missing values (e.g. Kriging). The main statistical novelty is our approach to regularizing the covariance matrix estimates, and the resulting linear predictors, which is based on methods from population genetics. We find that, besides being both fast and flexible - allowing new problems to be tackled that cannot be handled by existing imputation approaches purpose-built for the genetic context - these linear methods are also very accurate. Indeed, imputation accuracy using this approach is similar to that obtained by state-of-the art imputation methods that use individual-level data, but at a fraction of the computational cost.

Fast and robust association tests for untyped SNPs in case-control studies

Genome-wide association studies (GWASs) aim to genotype enough single nucleotide polymorphisms (SNPs) to effectively capture common genetic variants across the genome. Even though the number of SNPs genotyped in such studies can exceed a million, there is still interest in testing association with SNPs that were not genotyped in the study sample. Analyses of such untyped SNPs can assist in signal localization, permit cross-platform integration of samples from separate studies, and can improve power - especially for rarer SNPs. External information on a larger collection of SNPs from an appropriate reference panel, comprising both SNPs typed in the sample and the untyped SNPs we wish to test for association, is necessary for an untyped variant analysis to proceed. Linkage disequilibrium patterns observed in the reference panel are then used to infer the likely genotype at the untyped SNPs in the study sample. We propose here a novel statistical approach for testing untyped SNPs in case-control GWAS, based on an efficient score function derived from a prospective likelihood, that automatically accounts for the variability in the process of estimating the untyped variant. Computationally efficient methods of phasing can be used without affecting the validity of the test, and simple measures of haplotype sharing can be used to infer genotypes at the untyped SNPs, making our approach computationally much faster than existing approaches for untyped analysis. At the same time, we show, using simulated data, that our approach often has performance nearly equivalent to hidden Markov methods of untyped analysis. The software package 'untyped' is available to implement our approach.

Introduction to linkage disequilibrium, the HapMap, and imputation

Throughout the human genome, a correlation structure exists across genetic variation of different loci. Such a correlation structure means that knowing the genotype at one locus might provide information about the genotype at a second locus. This correlation between variation at different loci is termed linkage disequilibrium (LD). LD has implications in numerous avenues of genetic research. This article discusses the importance of LD in genetics, touching on both population genetics and association studies. It then introduces the seminal collaborative scientific endeavor to map LD in the human genome--the International HapMap Project--and its relevance for imputation.

Gene, region and pathway level analyses in whole-genome studies

In the setting of genome-wide association studies, we propose a method for assigning a measure of significance to pre-defined sets of markers in the genome. The sets can be genes, conserved regions, or groups of genes such as pathways. Using the proposed methods and algorithms, evidence for association between a particular functional unit and a disease status can be obtained not just by the presence of a strong signal from a SNP within it, but also by the combination of several simultaneous weaker signals that are not strongly correlated. This approach has several advantages. First, moderately strong signals from different SNPs are combined to obtain a much stronger signal for the set, therefore increasing power. Second, in combination with methods that provide information on untyped markers, it leads to results that can be readily combined across studies and platforms that might use different SNPs. Third, the results are easy to interpret, since they refer to functional sets of markers that are likely to behave as a unit in their phenotypic effect. Finally, the availability of gene-level P-values for association is the first step in developing methods that integrate information from pathways and networks with genome-wide association data, and these can lead to a better understanding of the complex traits genetic architecture. The power of the approach is investigated in simulated and real datasets. Novel Crohn's disease associations are found using the WTCCC data.

Utilizing genotype imputation for the augmentation of sequence data

BACKGROUND

In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.

METHODOLOGY

A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project.

CONCLUSIONS

Imputation of genetic variation based on a proportion of sequenced samples is feasible. Our results indicate the following sequencing study design guidelines which take advantage of the recent advances in genotype imputation methodology: Select the largest and most diverse reference panel for sequencing and genotype as many "anchor" markers as possible.

Shades of gray: a comparison of linkage disequilibrium between Hutterites and Europeans

Founder or isolated populations have advantages for genetic studies due to decreased genetic and environmental heterogeneity. However, whereas longer-range linkage disequilibrium (LD) in these populations is expected to facilitate gene localization, extensive LD may actually limit the ability for gene discovery. The North American Hutterite population is one of the best characterized young founder populations and members of this isolate have been the subjects of our studies of complex traits, including fertility, asthma and cardiovascular disease, for >20 years. Here, we directly assess the patterns and extent of global LD using single nucleotide polymorphism genotypes with minor allele frequencies (MAFs) > or =5% from the Affymetrix GeneChip Mapping 500 K array in 60 relatively unrelated Hutterites and 60 unrelated Europeans (HapMap CEU). Although LD among some marker pairs extends further in the Hutterites than in Europeans, the pattern of LD and MAF are surprisingly similar. These results indicate that (1) identifying disease genes should be no more difficult in the Hutterites than in outbred European populations, (2) the same common susceptibility alleles for complex diseases should be present in the Hutterites and outbred European populations, and (3) imputation algorithms based on HapMap CEU should be applicable to the Hutterites.