Genome-wide Association Studies Information Research Articles

Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization.

Many studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR). We collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs. From the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012). We identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.

Multi-omics cannot replace sample size in genome-wide association studies.

The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs=0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.

Haptools: a toolkit for admixture and haplotype analysis.

Leveraging local ancestry and haplotype information in genome-wide association studies and downstream analyses can improve the utility of genomics for individuals from diverse and recently admixed ancestries. However, most existing simulation, visualization and variant analysis frameworks are based on variant-level analysis and do not automatically handle these features. We present haptools, an open-source toolkit for performing local ancestry aware and haplotype-based analysis of complex traits. Haptools supports fast simulation of admixed genomes, visualization of admixture tracks, simulation of haplotype- and local ancestry-specific phenotype effects and a variety of file operations and statistics computed in a haplotype-aware manner. Haptools is freely available at https://github.com/cast-genomics/haptools. Detailed documentation is available at https://haptools.readthedocs.io. Supplementary data are available at Bioinformatics online.

The statistical practice of the GTEx Project: from single to multiple tissues

BackgroundThe Genotype‐Tissue Expression (GTEx) Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased individuals, providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross‐tissue and tissue‐specific perspectives. Moreover, transcriptome activities ( e.g., transcript abundance and alternative splicing) can be treated as mediators between genotype and phenotype to achieve phenotypic alteration. Knowing the genotype associated transcriptome status, researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.ResultsIn this article, we first explore the genetic architecture of gene expression traits, and then review recent methods on quantitative trait locus (QTL) and co‐expression network analysis. To further exemplify the usage of associations between genotype and transcriptome status, we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome‐wide association studies (GWASs).ConclusionsThe GTEx Project provides the largest and useful resource to investigate the associations between genotype and transcriptome status. The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.

Across-breed genomic prediction for body weight in Siberian cattle populations

Body weight (BW) is an important heritable phenotype and related to other functional and production traits in cattle. The past decade has seen an increase in emphasis on genome wide association studies (GWAS) for detecting single nucleotide polymorphisms (SNPs) that are associated with quantitative phenotypes. Prediction of phenotypes using across-breed GWAS information [genomic prediction (GP)] is an also important research area but received less attention from the community. Understanding the link between major genes and common ancestors within and between breeds will contribute to a deeper understanding of GP across breeds. The aims of the present study were two-fold: 1) to examine genetic structure and to detect associated SNPs for BW using various single and multiple locus genomic models and 2) genomic prediction of BW using Siberian cattle populations based on across-breed genomic information. The most obvious finding to emerge from this study was the increase in the across-GP accuracy when gene segregation in both related populations was found. These findings have significant implications for the understanding of the way in which common ancestors and/or the presence of quantitative trait loci might affect the accuracy of the GP results.

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids.

Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.

Gene-diet interaction effects on BMI levels in the Singapore Chinese population

BackgroundRecent genome-wide association studies (GWAS) have identified 97 body-mass index (BMI) associated loci. We aimed to evaluate if dietary intake modifies BMI associations at these loci in the Singapore Chinese population.MethodsWe utilized GWAS information from six data subsets from two adult Chinese population (N = 7817). Seventy-eight genotyped or imputed index BMI single nucleotide polymorphisms (SNPs) that passed quality control procedures were available in all datasets. Alternative Healthy Eating Index (AHEI)-2010 score and ten nutrient variables were evaluated. Linear regression analyses between z score transformed BMI (Z-BMI) and dietary factors were performed. Interaction analyses were performed by introducing the interaction term (diet x SNP) in the same regression model. Analysis was carried out in each cohort individually and subsequently meta-analyzed using the inverse-variance weighted method. Analyses were also evaluated with a weighted gene-risk score (wGRS) contructed by BMI index SNPs from recent large-scale GWAS studies.ResultsNominal associations between Z-BMI and AHEI-2010 and some dietary factors were identified (P = 0.047-0.010). The BMI wGRS was robustly associated with Z-BMI (P = 1.55 × 10− 15) but not with any dietary variables. Dietary variables did not significantly interact with the wGRS to modify BMI associations. When interaction analyses were repeated using individual SNPs, a significant association between cholesterol intake and rs4740619 (CCDC171) was identified (β = 0.077, adjPinteraction = 0.043).ConclusionsThe CCDC171 gene locus may interact with cholesterol intake to increase BMI in the Singaporean Chinese population, however most known obesity risk loci were not associated with dietary intake and did not interact with diet to modify BMI levels.

Chapter 31 - Genetics of migraine

Genetics of migraine has recently undergone a major shift, moving in the space of a few years from having only a few known genes for rare Mendelian forms to 47 known common variant loci affecting the susceptibility of the common forms of migraine. This has largely been achieved by rapidly increasing sample sizes for genomewide association studies (GWAS), soon to be followed by the first wave of large-scale exome-sequencing studies. The large number of detected loci, chief among them TRPM8, PRDM16, and LRP1, have enabled a number of in silico analyses, which have shed light on the functional and tissue-level aspects of the common risk variants for migraine, including evidence for involvement of both vascular and neuronal mechanisms. Polygenic risk scores and other measures of genetic variance based on GWAS information are further opening the door to dissecting pharmacogenetics, functional etiology, and comorbidity. Heritability-based analyses are demonstrating strong links between migraine and other neuropsychiatric disorders and brain phenotypes, highlighting genetic links between migraine and major depressive disorder and attention-deficit hyperactivity disorder, among others. These recent successes in migraine genetics are starting to be mature enough to provide robust evidence of specific quantifiable genetic factors in common migraine.

A Scalable Bayesian Method for Integrating Functional Information in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified many complex loci. However, most loci reside in noncoding regions and have unknown biological functions. Integrative analysis that incorporates known functional information into GWASs can help elucidate the underlying biological mechanisms and prioritize important functional variants. Hence, we develop a flexible Bayesian variable selection model with efficient computational techniques for such integrative analysis. Different from previous approaches, our method models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD), thus prioritizing associations based on the quantification of the annotations and allowing for multiple associated variants per locus. Our method dramatically improves both computational speed and posterior sampling convergence by taking advantage of the block-wise LD structures in human genomes. In simulations, our method accurately quantifies the functional enrichment and performs more powerfully for prioritizing the true associations than alternative methods, where the power gain is especially apparent when multiple associated variants in LD reside in the same locus. We applied our method to an in-depth GWAS of age-related macular degeneration with 33,976 individuals and 9,857,286 variants. We find the strongest enrichment for causality among non-synonymous variants (54× more likely to be causal, 1.4× larger effect sizes) and variants in transcription, repressed Polycomb, and enhancer regions, as well as identify five additional candidate loci beyond the 32 known AMD risk loci. In conclusion, our method is shown to efficiently integrate functional information in GWASs, helping identify functional associated-variants and underlying biology.

An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer Phenotypes.

High-throughput genotyping has enabled discovery of genetic variants associated with an increased risk of developing prostate cancer using genome-wide association studies (GWAS). The goal of this study was to associate GWAS information of patients with primary organ–confined and metastatic prostate cancer using gene expression data and to identify molecular networks and biological pathways enriched for genetic susceptibility variants involved in the 2 disease states. The analysis revealed gene signatures for the 2 disease states and a gene signature distinguishing the 2 patient groups. In addition, the analysis revealed molecular networks and biological pathways enriched for genetic susceptibility variants. The discovered pathways include the androgen, apoptosis, and insulinlike growth factor signaling pathways. This analysis established putative functional bridges between GWAS discoveries and the biological pathways involved in primary organ–confined and metastatic prostate cancer.

Exploration and detection of potential regulatory variants in refractive error GWAS.

Refractive error (RE) is a complex multifactorial disease. Genome-wide association studies (GWAS) have provided significant insight into the genetic architecture and identified plenty of robust genetic variations or single nucleotide polymorphisms (SNPs) associated with complex disease. A major current challenge is to convert those resources into causal variants and target genes. We used RegulomeDB and HaploReg to annotate regulatory information onto associated SNPs derived from the two largest RE GWAS, and additional SNPs in linkage disequilibrium (LD) with GWAS significant SNPs. Overall 868 SNPs were investigated, out of which 662 returned RegulomeDB scores of 1 to 6. It was observed that 36 out of those SNPs show strong evidence of regulatory effects with a RegulomeDB score of 1, while only four of them were GWAS significant SNPs (CD55/rs1652333, CNDP2/rs12971120, RDH5/rs3138142 and rs3138144). The results encourage us to explore those putative pathogenic variants, both GWAS significant SNPs as well as the SNPs in LD, for future discernment of functional consequence. This study offers the attractive approach for prioritizing potential functional variants by combining ENCODE data and GWAS information, and provide further insights into the pathogenesis and mechanism and ultimately therapeutics.

Improving therapeutic effectiveness and safety through big healthcare data.

Big healthcare data-electronically recorded longitudinal data generated during the provision and administration of healthcare for millions of patients-have become essential for understanding the effectiveness and safety of therapeutics. They are most effectively used in concert with experimental and laboratory research throughout the life cycle of a drug. Applications range from providing phenotype and health outcomes information in genome-wide association studies to postmarketing studies that assure prescribers of the safety of approved drugs (Figure ). [Figure: see text].

Abstract 2778: Shared genetics of breast cancer risk factors across life stages

Abstract Breast cancer clusters in families and many of its established risk factors are under strong genetic control. Understanding the genetic architecture shared by breast cancer risk factors highlight the causal pathways through which these risk factors affect breast cancer risk. Traditionally, pedigree studies are used to estimate genetic correlations between complex traits but such estimates can be confounded by shared environmental factors. Recent methodological developments make it possible to estimate disease heritability and genetic correlations using data from genome-wide association studies (GWAS). We used GWAS of unrelated individuals to estimate the proportion of variance in breast cancer risk factors that can be explained by GWAS SNPs. We also quantified the shared genetic contribution to all pairs of the following traits: body mass index (BMI), gender-specific BMI, height, age at menarche, age at natural menopause, and childhood body fatness. We obtained GWAS and phenotypic information from a total of 20,769 subjects from the Nurses’ Health Study I &amp; II, Health Professional Follow-up Study, and Physician Health study. Using linear mixed models, we estimated single trait heritability and genetic correlation between two traits. We observed a significant amount of phenotypic variation explained by genotyped SNPs for all traits except age at natural menopause (hg2 = 0.15, se = 0.09). Heritability ranged from 0.18 (se = 0.004) for childhood body fatness to 0.35 (se = 0.05) for age at menarche. We found significant negative genetic correlation between adult BMI and height (corr = -0.20; se = 0.07, pvalue = 0.005), BMI and age at menarche (corr = -0.50; se = 0.07, pvalue = 8.8×10e-8), and age at menarche and childhood body fatness (corr = -0.57, se = 0.12, pvalue = 2.8×10e-6). We observed a positive genetic correlation between adult BMI and childhood body fatness (corr = 0.59, se = 0.12, pvalue = 8.0×10e-8) and such correlation is statistically different from 0 and 1, implying only a partially shared genetic signature between these two traits. The genetics underlying BMI in men and women were highly correlated (corr = 0.84, se = 0.26, pvalue = 9.7×10e-4). In addition, we found no significant shared genetic components between age at natural menopause and any of the traits, suggesting a different genetic architecture underlying the mechanism of menopause in comparison with other breast cancer risk factors. Using genome-wide SNP data in unrelated individuals to estimate heritability helps eliminate possible influence from unmeasured shared environmental factors that is commonly seen in family studies. Our findings improve the understanding of shared genetic architecture of breast cancer risk factors and provide important insights in breast cancer etiology. Citation Format: Chi Gao, Rulla M. Tamimi, Peter Kraft, Sara Lindstrom. Shared genetics of breast cancer risk factors across life stages. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2778. doi:10.1158/1538-7445.AM2015-2778

Integrative genomic analysis for the discovery of biomarkers in prostate cancer.

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

EPA-1747 – Investigation of the interplay between genetic and environmental risk factors for smoking behaviour

Smoking-behaviour is influenced by environmental and genetic risk factors. Established epidemiological risk factors include early age at onset (AaO), depression, positive family history (FH+) of depression/alcohol-dependence, low education, older birth cohort, and male gender. Genomewide-association-studies (GWAS) have identified genetic risk variants for smoking-behaviour. In the present study we investigated correlations between these epidemiological and genetic risk factors and smoking-behaviour in a large population-based German sample. Genetic risk was defined in terms of a polygenic score – the accumulated effect of seven independent genetic risk markers for smoking-behaviour identified through GWAS. The sample comprised 1736 individuals (815 males, 921 females). Dependent variables were: smoking-duration, nicotine-dependence, cigarettes–per-day, ever-smoking , and smoking-cessation . The effect of the epidemiological risk factors, the polygenic risk score, and their combined effect on the smoking-behaviours was tested via linear or logistic regression analyses. The following associations were detected: AaO and birth cohort with smoking-duration (p=0.004; p<0.001); AaO, education and FH+ depression with nicotine-dependence (p=0.002; p=0.092); sex and AaO with cigarettes–per-day (p=0.020; p<0.001); FH+ alcohol dependence with eversmoking (p=0.049); and birth cohort and education with smoking-cessation (p=0.001; p=0.029). The polygenic risk score showed a trend towards association with nicotine-dependence (p=0.113) and cigarettes–per-day (p=0.109). In the combined analyses, the polygenic risk score improved the regression model for nicotine-dependence, cigarettes–per-day , and smoking-cessation . The addition of GWAS information concerning genetic risk factors explained an increased fraction of the smoking behaviours nicotine dependence, CPD, and smoking cessation. Future studies are warranted to elucidate the biological correlates of these genetic risk factors.

Genome-wide association studies on prostate cancer: the end or the beginning?

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men. Genome-wide association studies (GWAS) has been highly successful in discovering susceptibility loci for prostate cancer. Currently, more than twenty GWAS have identified more than fifty common variants associated with susceptibility with PCa. Yet with the increase in loci, voices from the scientific society are calling for more. In this review, we summarize current findings, discuss the common problems troubling current studies and shed light upon possible breakthroughs in the future. GWAS is the beginning of something wonderful. Although we are quite near the end of the beginning, post-GWAS studies are just taking off and future studies are needed extensively. It is believed that in the future GWAS information will be helpful to build a comprehensive system intergraded with PCa prevention, diagnosis, molecular classification, personalized therapy.

An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer

Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly (P <10−5) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.

Novel Integrative Genomics Approach for Associating GWAS Information with Intrinsic Subtypes of Breast Cancer

Genome-wide association studies (GWAS) have achieved great success in identifying common variants associated with increased risk of developing breast cancer. However, GWAS do not typically provide information about the broader context in which genetic variants operate in different subtypes of breast cancer. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs, herein called genetic variants) are associated with different subtypes of breast cancer. Additionally, we sought to identify gene regulator networks and biological pathways enriched for these genetic variants. Using supervised analysis, we identified 201 genes that were significantly associated with the six intrinsic subtypes of breast cancer. The results demonstrate that integrative genomics analysis is a powerful approach for linking GWAS information to distinct disease states and provide insights about the broader context in which genetic variants operate in different subtypes of breast cancer.

An integrative genomics approach for mapping the emerging genetic susceptibility landscape of triple-negative breast cancer

Background Genome-wide association studies (GWAS) have achieved great success in identifying common genetics variants associated with increased risk for developing breast cancer. More recently, advances in next-generation sequencing (NGS) have made possible identification of mutations associated with breast cancer. However, to date, the information generated by GWAS and NGS has not been maximally leveraged and integrated with gene expression data to identify biomarkers associated with the most aggressive subset of breast cancer: the triple-negative breast cancer (TNBC). Here we present results from an integrative genomics approach that combines GWAS and sequence information with gene expression data to identify functionally related genes and biological pathways enriched for expression-associated genetic loci and mutations associated with TNBC using publicly available data.

Using Prior Information from the Medical Literature in GWAS of Oral Cancer Identifies Novel Susceptibility Variant on Chromosome 4 - the AdAPT Method

BackgroundGenome-wide association studies (GWAS) require large sample sizes to obtain adequate statistical power, but it may be possible to increase the power by incorporating complementary data. In this study we investigated the feasibility of automatically retrieving information from the medical literature and leveraging this information in GWAS.MethodsWe developed a method that searches through PubMed abstracts for pre-assigned keywords and key concepts, and uses this information to assign prior probabilities of association for each single nucleotide polymorphism (SNP) with the phenotype of interest - the Adjusting Association Priors with Text (AdAPT) method. Association results from a GWAS can subsequently be ranked in the context of these priors using the Bayes False Discovery Probability (BFDP) framework. We initially tested AdAPT by comparing rankings of known susceptibility alleles in a previous lung cancer GWAS, and subsequently applied it in a two-phase GWAS of oral cancer.ResultsKnown lung cancer susceptibility SNPs were consistently ranked higher by AdAPT BFDPs than by p-values. In the oral cancer GWAS, we sought to replicate the top five SNPs as ranked by AdAPT BFDPs, of which rs991316, located in the ADH gene region of 4q23, displayed a statistically significant association with oral cancer risk in the replication phase (per-rare-allele log additive p-value [ptrend] = 2.5×10−3). The combined OR for having one additional rare allele was 0.83 (95% CI: 0.76–0.90), and this association was independent of previously identified susceptibility SNPs that are associated with overall UADT cancer in this gene region. We also investigated if rs991316 was associated with other cancers of the upper aerodigestive tract (UADT), but no additional association signal was found.ConclusionThis study highlights the potential utility of systematically incorporating prior knowledge from the medical literature in genome-wide analyses using the AdAPT methodology. AdAPT is available online (url: http://services.gate.ac.uk/lld/gwas/service/config).