Standard Genome-wide Association Studies Research Articles

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

On the use of GBLUP and its extension for GWAS with additive and epistatic effects.

Genomic best linear unbiased prediction (GBLUP) is the most widely used model for genome-wide predictions. Interestingly, it is also possible to perform genome-wide association studies (GWAS) based on GBLUP. Although the estimated marker effects in GBLUP are shrunken and the conventional test based on such effects has low power, it was observed that a modified test statistic can be produced and the result of test was identical to a standard GWAS model. Later, a mathematical proof was given for the special case that there is no fixed covariate in GBLUP. Since then, the new approach has been called “GWAS by GBLUP”. Nevertheless, covariates such as environmental and subpopulation effects are very common in GBLUP. Thus, it is necessary to confirm the equivalence in the general case. Recently, the concept was generalized to GWAS for epistatic effects and the new approach was termed rapid epistatic mixed-model association analysis (REMMA) because it greatly improved the computational efficiency. However, the relationship between REMMA and the standard GWAS model has not been investigated. In this study, we first provided a general mathematical proof of the equivalence between “GWAS by GBLUP” and the standard GWAS model for additive effects. Then, we compared REMMA with the standard GWAS model for epistatic effects by a theoretical investigation and by empirical data analyses. We hypothesized that the similarity of the two models is influenced by the relative contribution of additive and epistatic effects to the phenotypic variance, which was verified by empirical and simulation studies.

Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer.

Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10-4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.

GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS

Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki ( https://jpritikin.github.io/gwsem/ ).

Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response

Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 (OR = 0.33 [0.21, 0.51], p = 1.42 × 10−6) and intergenic rs6916777 (Beta = 14.03 [8.47, 19.59], p = 1.25 × 10−6), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease (n = 25 of 190 genes, p = 8.03 × 10−6, FDR-corrected p = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer’s disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p = 2.45 × 10−4). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction.

Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory

An increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”. Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN (https://cran.rproject.org/package=rNeighborGWAS).

Genome-wide association study for age at puberty and resumption of cyclicity in a crossbred dairy cattle population

Fertility is of primary economic importance in dairy cattle and the most common reason for involuntary culling. However, standard fertility traits have very low heritability that renders genetic selection slow and difficult. In this study, we explored fertility from an endocrine standpoint. A total of 1,163 crossbred Holstein-Normande females in a 3-generation familial design were studied for progesterone level measured every 10 d to determine age at puberty (PUB) and commencement of postpartum luteal activity (CPLA). Genetic parameters were estimated using REML with WOMBAT software. The heritability estimates were 0.38 ± 0.10 and 0.16 ± 0.07 for PUB and CPLA, respectively. Moreover, the 2 traits were genetically correlated (0.45 ± 0.23), suggesting a partially common determinism. Because of the family structure, a linkage disequilibrium and linkage analysis approach was preferred over standard genome-wide association study to map genomic regions associated with these traits. Ten quantitative trait loci (QTL) were detected for PUB on chromosomes 1, 3, 11, 13, 14, 21, and 29, whereas 3 QTL were associated with CPLA on chromosomes 21 and 26. Only the QTL on chromosome 21 was common to both traits. Four functional candidate genes (NCOA2, GAS2, OVOL1, and FOSL1) were identified in the detected regions. These findings will contribute to a clearer understanding of fertility determinism and enhance the value of introducing endocrinological data in fertility studies.

Genome-wide association study of patients with a severe major depressive episode treated with electroconvulsive therapy

Although large genome-wide association studies (GWAS) of major depressive disorder (MDD) have identified many significant loci, the SNP-based heritability remains notably low, which might be due to etiological heterogeneity in existing samples. Here, we test the utility of targeting the severe end of the MDD spectrum through genome-wide SNP genotyping of 2725 cases who received electroconvulsive therapy (ECT) for a major depressive episode (MDE) and 4035 controls. A subset of cases (n = 1796) met a narrow case definition (MDE occurring in the context of MDD). Standard GWAS quality control procedures and imputation were conducted. SNP heritability and genetic correlations with other traits were estimated using linkage disequilibrium score regression. Results were compared with MDD cases of mild-moderate severity receiving internet-based cognitive behavioral therapy (iCBT) and summary results from the Psychiatric Genomics Consortium (PGC). The SNP-based heritability was estimated at 29–34% (SE: 6%) for the narrow case definition, considerably higher than the 6.5–8.0% estimate in the most recent PGC MDD study. Our severe MDE cases had smaller genetic correlations with neurodevelopmental disorders and neuroticism than PGC MDD cases but higher genetic risk scores for bipolar disorder than iCBT MDD cases. One genome-wide significant locus was identified (rs114583506, P = 5e−8) in an intron of HLA-B in the major histocompatibility locus on chr6. These results indicate that individuals receiving ECT for an MDE have higher burden of common variant risk loci than individuals with mild-moderate MDD. Furthermore, severe MDE shows stronger relations with other severe adult-onset psychiatric disorders but weaker relations with personality and stress-related traits than mild-moderate MDD. These findings suggest a different genetic architecture at the severest end of the spectrum, and support further study of the severest MDD cases as an extreme phenotype approach to understand the etiology of MDD.

Genome‐wide association study of progression in Alzheimer's disease

AbstractBackgroundThe rate of disease progression is an important factor to consider regarding Alzheimer’s disease (AD), as people with a rapid decline are considerably more likely to require additional care resources. The rate of progression in AD has been found to vary widely between individuals, with numerous factors driving this heterogeneity. In this project, a genome‐wide association study (GWAS) was performed to investigate the presence of a genetic liability to faster progression in patients with AD.MethodsA subset of 543 individuals with AD from the Cardiff MRC centre cohorts that have been assessed at multiple timepoints was used in this analysis. A progression score for each individual was approximated by a random slope from a random effects regression model of Mini Mental State Examination (MMSE) using all available MMSE measurements. The score was subsequently used in a GWAS of progression. Quality control analysis was performed following standard GWAS protocols resulting in 304,448 variants to be included in the analysis.ResultsThe sample was 67.37% female, with mean age at recruitment being 80.2 years. Mean MMSE was 17.03 at first assessment and 11.54 at last assessment, and mean number of assessments was 3.16. After correcting for multiple testing, no variant was found to be significantly associated with progression in this GWAS. Furthermore, apolipoprotein E (ApoE) genotype was not associated with progression.ConclusionPreliminary results show that, after Bonferroni correction for multiple testing, no variant was significantly associated with progression in AD. Suggestive associated variants were found in chromosome 20:5843812 (p‐value = 3.12 × 10−7), 1:62271962 (p‐value = 6.30 × 10−7), 10:15452800 (p‐value = 1.65 × 10−6) and 5:117646945 (p‐value = 2.40 × 10−6). Moreover, ApoE genotype was not found to affect the rate progression, the lowest p‐value in the ApoE region (19:44905791 ‐ 19:44909393) being 0.23. This non‐significant result might indicate that the genetic risk for accelerated cognitive decline within AD is separate from the genetic risk for developing AD. The sample size is one of the main limitations of this study. Replication in a larger dataset is necessary in order to validate these findings and uncover the presence of genetic variants that affect the rate of decline in AD.

Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank.

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

Using SNP Weights Derived From Gene Expression Modules to Improve GWAS Power for Feed Efficiency in Pigs.

The “large p small n” problem has posed a significant challenge in the analysis and interpretation of genome-wide association studies (GWAS). The use of prior information to rank genomic regions and perform SNP selection could increase the power of GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS. RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, and a three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules. Loading values from each gene module were used to assign weights to 49,691 commercial SNP markers, and SNP were selected using a weight threshold, resulting in 10 SNP sets ranging in size from 101 to 955 markers. Weighted GWAS for feed intake in 4,200 pigs was performed separately for each of the 10 SNP sets. A total of 36 unique significant SNP associations were identified across the ten gene modules (SNP sets). For comparison, a standard unweighted GWAS using all 49,691 SNP was performed, and only 2 SNP were significant. None of the SNP from the unweighted analysis resided in known QTL related to swine feed efficiency (feed intake, average daily gain, and feed conversion ratio) compared to 29 (80.6%) in the weighted analyses, with 9 SNP residing in feed intake QTL. These results suggest that the heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS. Hence, the proposed procedure for prioritizing SNP based on gene expression data across multiple tissues provides a promising approach for improving the power of GWAS.

Genome-Wide Association Study of Sleep Disturbances in Depressive Disorders

Sleep disturbance affects about 75% of depressed individuals and is associated with poorer patient outcomes. The genetics in this field is an emerging area of research. Thus far, only core circadian genes have been examined in this context. We expanded on this by performing a genome-wide association study (GWAS) followed by a preplanned hypothesis-driven analysis with 27 genes associated with the biology of sleep. All participants were diagnosed by their referring physician, completed the Beck Depression Inventory (BDI), and the Udvalg for Kliniske Undersogelser Side Effect Rating Scale at baseline. Our phenotype consisted of replies to 3 questions from these questionnaires. From standard GWAS chip data, imputations were performed. Baseline total BDI scores (n = 364) differed significantly between those with and those without sleep problems. We were unable to find any significant GWAS hits although our top hit was for changes in sleep and an intergenic marker near SNX18 (p = 1.06 × 10^–6). None of the markers in our hypothesis-driven analysis remained significant after applying Bonferroni corrections. Our top finding among these genes was for rs13019460 of Neuronal PAS Domain Protein 2 with changes in sleep (p = 0.0009). Overall, both analyses were unable to detect any significant associations in our modest sample though we did find some interesting preliminary associations worth further exploration.

Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression.

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10 × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.

A Genome-Wide Association Study of Sprint Performance in Elite Youth Football Players

Pickering, C, Suraci, B, Semenova, EA, Boulygina, EA, Kostryukova, ES, Kulemin, NA, Borisov, OV, Khabibova, SA, Larin, AK, Pavlenko, AV, Lyubaeva, EV, Popov, DV, Lysenko, EA, Vepkhvadze, TF, Lednev, EM, Leońska-Duniec, A, Pająk, B, Chycki, J, Moska, W, Lulińska-Kuklik, E, Dornowski, M, Maszczyk, A, Bradley, B, Kana-ah, A, Cięszczyk, P, Generozov, EV, and Ahmetov, II. A genome-wide association study of sprint performance in elite youth football players. J Strength Cond Res 33(9): 2344-2351, 2019-Sprint speed is an important component of football performance, with teams often placing a high value on sprint and acceleration ability. The aim of this study was to undertake the first genome-wide association study to identify genetic variants associated with sprint test performance in elite youth football players and to further validate the obtained results in additional studies. Using micro-array data (600 K-1.14 M single nucleotide polymorphisms [SNPs]) of 1,206 subjects, we identified 12 SNPs with suggestive significance after passing replication criteria. The polymorphism rs55743914 located in the PTPRK gene was found as the most significant for 5-m sprint test (p = 7.7 × 10). Seven of the discovered SNPs were also associated with sprint test performance in a cohort of 126 Polish women, and 4 were associated with power athlete status in a cohort of 399 elite Russian athletes. Six SNPs were associated with muscle fiber type in a cohort of 96 Russian subjects. We also examined genotype distributions and possible associations for 16 SNPs previously linked with sprint performance. Four SNPs (AGT rs699, HSD17B14 rs7247312, IGF2 rs680, and IL6 rs1800795) were associated with sprint test performance in this cohort. In addition, the G alleles of 2 SNPs in ADRB2 (rs1042713 & rs1042714) were significantly over-represented in these players compared with British and European controls. These results suggest that there is a genetic influence on sprint test performance in footballers, and identifies some of the genetic variants that help explain this influence.

Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.

Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset.

A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene

AbstractStandard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Integrated Genome Wide Association Study (GWAS) Identifies SNPs Associated with Outcome in Pediatric AML

Acute myeloid leukemia (AML) treatment response remains poorly understood. Although multiple studies have focused on understanding the transcriptomic and epigenetic landscape of AML, a genome-wide analysis of SNPs in pediatric AML has not yet been investigated in depth. Thus, we sought to identify genetic variants predictive of AML response, relapse, and survival in pediatric AML patients. For this study, we generated genome-wide SNP data patients (n=160) treated on the multicenter AML02 clinical trial (ClinicalTrials.gov Identifier: NCT00136084) using Infinium Omni 2.5M Exome Beadchip. Standard GWAS QC procedure was followed in order to remove SNPs with call rate < 95%, monomorphic SNPs, SNPs with MAF<5% and samples with call rate<95%. Following QC, a risk-adjusted multi-outcome integrative GWAS was performed to identify SNPs associated with minimal residual disease (MRD) following induction I, relapse-free survival (RFS) and overall survival (OS). We performed a risk-adjusted analysis to identify 21 SNPs mapping to 14 genes at an endpoint-integrative p value <2x10-5. Table 1 provides list of genes with SNPs significantly associated with MRD, RFS, OS as well as in the integrated analysis at <2x10-5. Of interest multiple SNPs in DICER1, which is a key enzyme required for the biogenesis of microRNAs and small interfering RNAs were significantly associated with clinical outcome with promise integrated analysis at p = 0.000011, supported by associations with MRD, RFS and OS at p <0.002 (Figure 1A). DICER1 is over-expressed in AML with its expression under the influence of hematopoietic transcript factor, GATA1. RAI14, a retinoic acid induced 14 is a prognostic marker of poor response in solid tumors and has been associated with development of drug resistance. Multiple SNPs in RAI14 were significantly associated with clinical endpoints. Figure 1B shows RAI14 SNP rs336474 with C allele significantly associated with better RFS (p= 0.027) and OS (p=0.007), with an integrated p= 0.000004. SNP in upstream of RBFOX1, a RNA binding fox-1 homolog 1 and within intron of GRIN2A, glutamate ionotropic receptor NMDA type subunit 2A were significantly associated with MRD, RFS and OS (all p<0.005) and integrated p =0.00001 (Figure 1C). SNPs within genes involved in pyrimidine metabolism such as UPP2, a uridine phosphorylase; tumor suppressor genes such as JPH3, which codes for junctophilin; LILRB4 which encodes for a Leukocyte Immunoglobulin Like Receptor B4, that regulates inflammatory responses and cytotoxicity; HACE1 a potential tumor suppressor involved in the solid tumors pathophysiology; ANK2, an ankyrin family protein with role in cell proliferation and motility; BIRC8, which is implicated in CML disease progression etc. In conclusion, our results demonstrate significance of genome-wide investigation of SNPs to identify novel and clinically relevant SNPs of prognostic significance in childhood AML. We will present the in depth results of our integrated GWAS analysis as well as validation in independent patient cohorts. In summary, our results constitute one of the first integrated GWAS analyses to identify SNPs of prognostic significance in pediatric AML.Acknowledgments: We are thankful for funding from NIH R01-CA139246 and ALSAC. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Discovery of rare ancestry-specific variants in the fetal genome that confer risk of preterm premature rupture of membranes (PPROM) and preterm birth

BackgroundPreterm premature rupture of membranes (PPROM) is the leading identifiable cause of preterm birth, a complication that is more common in African Americans. Attempts to identify genetic loci associated with preterm birth using genome-wide association studies (GWAS) have only been successful with large numbers of cases and controls, and there has yet to be a convincing genetic association to explain racial/ethnic disparities. Indeed, the search for ancestry-specific variants associated with preterm birth has led to the conclusion that spontaneous preterm birth could be the consequence of multiple rare variants. The hypothesis that preterm birth is due to rare genetic variants that would go undetected in standard GWAS has been explored in the present study. The detection and validation of these rare variants present challenges because of the low allele frequency. However, some success in the identification of fetal loci/genes associated with preterm birth using whole genome sequencing and whole exome sequencing (WES) has recently been reported. While encouraging, this is currently an expensive technology, and methods to leverage the sequencing data to quickly identify and cost-effectively validate variants are needed.MethodsWe developed a WES data analysis strategy based on neonatal genomic DNA from PPROM cases and term controls that was unencumbered by preselection of candidate genes, and capable of identifying variants in African Americans worthy of focused evaluation to establish statistically significant associations.ResultsWe describe this approach and the identification of damaging nonsense variants of African ancestry in the DEFB1 and MBL2 genes that encode anti-microbial proteins that presumably defend the fetal membranes from infectious agents. Our approach also enabled us to rule out a likely contribution of a predicted damaging nonsense variant in the METTL7B gene.ConclusionsOur findings support the notion that multiple rare population-specific variants in the fetal genome contribute to preterm birth associated with PPROM.

Comparison of methods for transcriptome imputation through application to two common complex diseases

Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn’s disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn’s disease and type 1 diabetes detects 53 significant expression—Crohn’s disease associations and 154 significant expression—type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes.