Phenome-wide association study of TTR and RBP4 genes in 361,194 individuals reveals novel insights in the genetics of hereditary and senile systemic amyloidoses

Mapping Intimacies ◽

10.1101/19001537 ◽

2019 ◽

Author(s):

Antonella De Lillo ◽

Flavio De Angelis ◽

Marco Di Girolamo ◽

Marco Luigetti ◽

Sabrina Frusconi ◽

...

Keyword(s):

Association Study ◽

Multiple Testing ◽

Large Scale ◽

Phenotypic Traits ◽

Multiple Testing Correction ◽

Gene Variation ◽

Senile Systemic Amyloidosis ◽

Phenotypic Spectrum ◽

Tunnel Syndrome ◽

Coding Variants

ABSTRACTTransthyretin (TTR) gene has a causal role in a hereditary form of amyloidosis (ATTRm) and is potentially involved in the risk of senile systemic amyloidosis (SSA). To understand the genetics of ATTRm and SSA, we conducted a phenome-wide association study of TTR gene in 361,194 participants of European descent testing coding and non-coding variants. Among the 382 clinically-relevant phenotypes tested, TTR non-coding variants were associated with 26 phenotypic traits after multiple testing correction. These included signs related to both ATTRm and SSA such as chronic ischaemic heart disease (rs140226130, p=2.00×10−6), heart failure (rs73956431, p=2.74×10−6), atrial fibrillation (rs10163755, p=4.63×10−6), dysphagia (rs2949506, p=3.95×10−6), intestine diseases (rs970866, p=7.14×10−6) and anxiety (rs554521234, p=8.85×10−6). Consistent results were observed for TTR disease-causing mutation Val122Ile (rs76992529) with respect to carpal tunnel syndrome (p=6.41×10−6) and mononeuropathies of upper limbs (p=1.22×10−5). Sex differences were also observed in line with ATTRm and SSA epidemiology. Additionally, we explored possible modifier genes related to TTR function, observing convergent associations of RBP4 variants with the clinical phenotypes associated with TTR locus. In conclusion, we provide novel insights regarding the molecular basis of ATTRm and SSA based on large-scale cohort, expanding our understanding of the phenotypic spectrum associated with TTR gene variation.

Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽

10.3390/genes13010087 ◽

2021 ◽

Vol 13 (1) ◽

pp. 87

Author(s):

Sean M. Burnard ◽

Rodney A. Lea ◽

Miles Benton ◽

David Eccles ◽

Daniel W. Kennedy ◽

...

Keyword(s):

Multiple Sclerosis ◽

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Elastic Net ◽

Genome Wide Association Studies ◽

Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics

10.1101/148627 ◽

2017 ◽

Cited By ~ 4

Author(s):

Jie Zheng ◽

Tom G. Richardson ◽

Louise A. C. Millard ◽

Gibran Hemani ◽

Christopher Raistrick ◽

...

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Genome Wide Association Study ◽

Phenotypic Correlation ◽

Summary Statistics ◽

Multiple Testing Correction ◽

Individual Level ◽

Phenotypic Correlations ◽

Complex Human Traits

AbstractBackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.ResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.ConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.AvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

10.1101/107987 ◽

2017 ◽

Cited By ~ 6

Author(s):

Neda Jahanshad ◽

Habib Ganjgahi ◽

Janita Bralten ◽

Anouk den Braber ◽

Joshua Faskowitz ◽

...

Keyword(s):

White Matter ◽

Multiple Testing ◽

Large Scale ◽

Diffusion Tensor ◽

Candidate Snps ◽

Nucleotide Polymorphisms ◽

Multiple Testing Correction ◽

Healthy Human ◽

Multiple Granularity ◽

Single Cohort

Abstract:Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

POEAS: Automated Plant Phenomic Analysis Using Plant Ontology

Bioinformatics and Biology Insights ◽

10.4137/bbi.s19057 ◽

2014 ◽

Vol 8 ◽

pp. BBI.S19057 ◽

Cited By ~ 3

Author(s):

Khader Shameer ◽

Mahantesha Bn Naika ◽

Oommen K. Mathew ◽

Ramanathan Sowdhamini

Keyword(s):

Multiple Testing ◽

Large Scale ◽

Statistical Significance ◽

Enrichment Analysis ◽

Multiple Testing Correction ◽

Background Population ◽

Complementary Resource ◽

Plant Ontology ◽

Plant Genes ◽

Cellular Components

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas .

Association between the pregnancy exposome and fetal growth

International Journal of Epidemiology ◽

10.1093/ije/dyaa017 ◽

2020 ◽

Vol 49 (2) ◽

pp. 572-586 ◽

Cited By ~ 5

Author(s):

Lydiane Agier ◽

Xavier Basagaña ◽

Carles Hernandez-Ferrer ◽

Léa Maitre ◽

Ibon Tamayo Uria ◽

...

Keyword(s):

Birth Weight ◽

Fetal Growth ◽

Multiple Testing ◽

Large Scale ◽

Fine Particulate Matter ◽

Health Concern ◽

Selective Reporting ◽

Multiple Testing Correction ◽

Single Exposure ◽

Environmental Models

Abstract Background Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes. Methods Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother–child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure–exposure and sex–exposure interactions. Results The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified. Conclusions This first large-scale exposome study of fetal growth simultaneously considered >100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.

Exome wide association study on Albuminuria identifies a novel rare variant inCUBNand additional genes, in 33985 Europeans with and without diabetes

10.1101/355990 ◽

2018 ◽

Author(s):

Tarunveer S. Ahluwalia ◽

Christina-Alexendra Schulz ◽

Johannes Waage ◽

Tea Skaaby ◽

Niina Sandholm ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Association Study ◽

Genetic Architecture ◽

European Ancestry ◽

Common Variants ◽

Multiple Testing Correction ◽

Meta Analyses ◽

Coding Variants ◽

Prevention Of Diabetes

AbstractIdentifying rare coding variants associated with albuminuria may open new avenues for preventing chronic kidney disease (CKD) and end-stage renal disease which are highly prevalent in patients with diabetes. Efforts to identify genetic susceptibility variants for albuminuria have so far been limited with the majority of studies focusing on common variants.We performed an exome-wide association study to identify coding variants in a two phase (discovery and replication) approach, totaling to 33,985 individuals of European ancestry (15,872 with and 18,113 without diabetes) and further testing in Greenlanders (n = 2,605). We identify a rare (MAF: 0.8%) missense (A1690V) variant inCUBN(rs141640975, β=0.27, p=1.3 × 10−11) associated with albuminuria as a continuous measure in the combined European meta-analyses. Presence of each rare allele of the variant was associated with a 6.4% increase in albuminuria. The rareCUBNvariant had 3 times stronger effect in individuals with diabetes compared to those without(pinteraction:5.4 × 10−4, βDM: 0.69, βnonDM:0.20) in the discovery meta-analyses. Geneaggregate tests based on rare and common variants identify three additional genes associated with albuminuria(HES1, CDC73, andGRM5)after multiple testing correction (P_bonferroni<2.7 × 10−6).The current study identifies a rare coding variant in theCUBNlocus and other potential genes associated with albuminuria in individuals with and without diabetes. These genes have been implicated in renal and cardiovascular dysfunction. These findings provide new insights into the genetic architecture of albuminuria and highlight novel target genes and pathways for prevention of diabetes-related kidney disease.Significance statementIncreased albuminuria is a key manifestation of major health burdens, including chronic kidney disease and/or cardiovascular disease. Although being partially heritable, there is a lack of knowledge on rare genetic variants that contribute to albuminuria. The current study describes the discovery and validation, of a new rare gene mutation (~1%) in theCUBNgene which associates with increased albuminuria. Its effect multiplies 3 folds among diabetes cases compared to non diabetic individuals. The study further uncovers 3 additional genes modulating albuminuria levels in humans. Thus the current study findings provide new insights into the genetic architecture of albuminuria and highlight novel genes/pathways for prevention of diabetes related kidney disease.

Phenomic Impact of Genetically-Determined Euthyroid Function and Molecular Differences between Thyroid Disorders

Journal of Clinical Medicine ◽

10.3390/jcm7100296 ◽

2018 ◽

Vol 7 (10) ◽

pp. 296 ◽

Cited By ~ 10

Author(s):

Silvia Ravera ◽

Nancy Carrasco ◽

Joel Gelernter ◽

Renato Polimanti

Keyword(s):

Multiple Testing ◽

Molecular Mechanisms ◽

Causal Effect ◽

Individual Variability ◽

Whole Body ◽

Phenotypic Traits ◽

Thyroid Disorders ◽

Nucleotide Exchange ◽

Multiple Testing Correction ◽

Genetically Determined

Background: The thyroid plays a key role in development and homeostasis, but it has been difficult to establish causality with diseases and phenotypic traits because of several potential confounders. Methods: To determine the causal effect of euthyroid function, we conducted a two-sample Mendelian randomization study of euthyroid thyrotropin (TSH) and free thyroxine (FT4) levels with respect to 2419 traits assessed in 337,199 individuals from UK Biobank. Additionally, we investigated the molecular differences between hypothyroidism and hyperthyroidism using genome-wide data. Results: After multiple testing correction, sixteen traits appear to be affected by genetically-determined euthyroid TSH, including multiple thyroid-related traits, e.g., hypothyroidism (p = 2.39 × 10−17), height (p = 2.76 × 10−10), body fat distribution (impedance of whole body, p = 4.43 × 10−8), pulse rate (p = 2.84 × 10−8), female infertility (p = 4.91 × 10−6), and hearing aid use (p = 7.10 × 10−5). Moreover, we found a consistent genetic correlation between hypothyroidism and hyperthyroidism (rg = 0.45, p = 5.45 × 10−6) with several immune pathways shared between these diseases. Two molecular pathways survived multiple testing correction for specificity to hyperthyroidism, JAK/STAT signaling (p = 1.02 × 10−6) and Rac guanyl-nucleotide exchange factor activity (p = 4.39 × 10−6). Conclusion: Our data shed new light on the inter-individual variability of euthyroid function and the molecular mechanisms of the two thyroid disorders investigated.

Associations between untargeted plasma metabolomic signatures and gut microbiota composition in the Milieu Intérieur population of healthy adults

British Journal Of Nutrition ◽

10.1017/s0007114520004870 ◽

2020 ◽

pp. 1-11

Author(s):

Valentin Partula ◽

Mélanie Deschasaux-Tanguy ◽

Stanislas Mondot ◽

Agnès Victor-Bala ◽

Nadia Bouchemal ◽

...

Keyword(s):

Gut Microbiota ◽

Multiple Testing ◽

Large Scale ◽

Linear Models ◽

Healthy Adults ◽

Rrna Gene ◽

Microbiota Composition ◽

Multiple Testing Correction ◽

Negatively Associated ◽

Gut Microbiota Composition

Abstract Host–microbial co-metabolism products are being increasingly recognised to play important roles in physiological processes. However, studies undertaking a comprehensive approach to consider host–microbial metabolic relationships remain scarce. Metabolomic analysis yielding detailed information regarding metabolites found in a given biological compartment holds promise for such an approach. This work aimed to explore the associations between host plasma metabolomic signatures and gut microbiota composition in healthy adults of the Milieu Intérieur study. For 846 subjects, gut microbiota composition was profiled through sequencing of the 16S rRNA gene in stools. Metabolomic signatures were generated through proton NMR analysis of plasma. The associations between metabolomic variables and α- and β-diversity indexes and relative taxa abundances were tested using multi-adjusted partial Spearman correlations, permutational ANOVA and multivariate associations with linear models, respectively. A multiple testing correction was applied (Benjamini–Hochberg, 10 % false discovery rate). Microbial richness was negatively associated with lipid-related signals and positively associated with amino acids, choline, creatinine, glucose and citrate (−0·133 ≤ Spearman’s ρ ≤ 0·126). Specific associations between metabolomic signals and abundances of taxa were detected (twenty-five at the genus level and nineteen at the species level): notably, numerous associations were observed for creatinine (positively associated with eleven species and negatively associated with Faecalibacterium prausnitzii). This large-scale population-based study highlights metabolites associated with gut microbial features and provides new insights into the understanding of complex host–gut microbiota metabolic relationships. In particular, our results support the implication of a ‘gut–kidney axis’. More studies providing a detailed exploration of these complex interactions and their implications for host health are needed.

Manifestations of genetic risk for Alzheimer’s Disease in the blood: a cross-sectional multi-omic analysis in healthy adults aged 18-90+

10.1101/2021.03.26.437267 ◽

2021 ◽

Author(s):

Laura Heath ◽

John C. Earls ◽

Andrew T. Magis ◽

Sergey A. Kornilov ◽

Jennifer C. Lovejoy ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Association Study ◽

Genetic Variants ◽

Genetic Risk ◽

Multiple Testing ◽

Large Scale ◽

Genome Wide Association Study ◽

Cross Sectional ◽

Risk Variants

AbstractDeeply phenotyped cohort data can elucidate differences associated with genetic risk for common complex diseases across an age spectrum. Previous work has identified genetic variants associated with Alzheimer’s disease (AD) risk from large-scale genome-wide association study meta-analyses. To explore effects of known AD-risk variants, we performed a phenome-wide association study on ~2000 clinical, proteomic, and metabolic blood-based analytes obtained from 2,831 cognitively normal adult clients of a consumer-based scientific wellness company. Results uncovered statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE). These effects were detectable from early adulthood. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. Sex-stratified GWAS results from an independent AD case-control meta-analysis supported sexspecific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. These analyses support evidence from previous functional genomics studies in the identification of a causal variant within the PILRA gene. Taken together, this study highlights clues to the earliest effects of AD genetic risk variants in individuals where disease symptoms have not (yet) arisen.

Large-scale proteome analysis of CSF implicates altered glucose metabolism in Alzheimer's disease

10.1101/2021.09.02.21262642 ◽

2021 ◽

Author(s):

Daniel J. Panyard ◽

Justin McKetney ◽

Yuetiva K. Deming ◽

Autumn R. Morrow ◽

Gilda E. Ennis ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Multiple Testing ◽

Large Scale ◽

Csf Biomarkers ◽

Multiple Testing Correction ◽

Comparative Performance ◽

Amyloid A ◽

Altered Glucose ◽

Associated Proteins

A major hallmark of Alzheimer's disease (AD) is the aggregation of misfolded proteins (β-amyloid (A) and hyperphosphorylated tau (T)) in the brain. As these proteins can be monitored by cerebrospinal fluid (CSF) measures, the AD proteome in CSF has been of particular interest. Here, we conducted a proteome-wide assessment of the CSF in an AD cohort among participants with and without AD pathology (n = 137 total participants: 56 A-T-, 39 A+T-, and 42 A+T+; 915 proteins analyzed), identifying a diverse set of proteins in the CSF enriched for extracellular and immune system processes. We then interrogated the proteome using the amyloid, tau, and neurodegeneration (ATN) framework of AD and a panel of 9 CSF biomarkers for neurodegeneration and neuroinflammation. After multiple testing correction, we identified a total of 61 proteins significantly associated with AT group (P < 5.46 x 10-5; strongest was SMOC1, P = 1.87 x 10-12) and 636 significant protein-biomarker associations (P < 6.07 x 10-6; strongest was a positive association between neurogranin and EPHA4, P = 2.42 x 10-25) across all measures except for interleukin-6, which had no significantly associated proteins. Community network and pathway enrichment analyses highlighted three biomarker-associated protein networks: one related to amyloid and tau measures, one to CSF neurogranin, and one to the remaining CSF biomarkers. Glucose metabolic pathways were enriched primarily among the amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, both of which were replicated as strongly associated with AD (P = 1.07 x 10-19 and P = 7.43 x 10-14, respectively) in an independent CSF proteomics cohort (n = 717 participants). Comparative performance of the CSF proteome in predicting AT categorization was high (mean AUC range 0.891-0.924 with number of protein predictors ranging from 37-97) relative to other omic predictors from the genome, CSF metabolome, and demographics from the same cohort of individuals. Collectively, these results emphasize the importance of the CSF proteome relative to other omics and implicate glucose metabolic dysregulation as amyloid and tau pathology emerges in AD.