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.

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Factors Affecting Reproducibility between Genome-Scale siRNA-Based Screens

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057110374994 ◽

2010 ◽

Vol 15 (7) ◽

pp. 735-747 ◽

Cited By ~ 31

Author(s):

Nicholas J. Barrows ◽

Caroline Le Sommer ◽

Mariano A. Garcia-Blanco ◽

James L. Pearson

Keyword(s):

Impact Analysis ◽

Yellow Fever Virus ◽

Association Studies ◽

P Value ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Data Set ◽

Analysis Methodology ◽

Genome Scale ◽

The Impact

RNA interference-based screening is a powerful new genomic technology that addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, the authors performed 2 identically designed small interfering RNA (siRNA)–based, whole-genome screens for host factors supporting yellow fever virus infection. These screens represent 2 separate experiments completed 5 months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole-genome screens. Application of these analysis methodologies within a single screening data set using a fixed threshold equivalent to a p-value ≤0.001 resulted in hit lists ranging from 82 to 1140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and interscreen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least 2 independent siRNAs for each gene product in primary screens. To facilitate validation, the authors conclude by suggesting methods to reduce false discovery at the primary screening stage. In this study, they present the first comprehensive comparison of multiple analysis strategies and demonstrate the impact of the analysis methodology on the composition of the “hit list.” Therefore, they propose that the entire data set derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies.

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What multiple Mendelian randomization approaches reveal about obesity and gout

10.1101/2021.03.26.21254420 ◽

2021 ◽

Author(s):

Charleen D. Adams ◽

Brian Boutwell

Keyword(s):

Observational Studies ◽

Mendelian Randomization ◽

Association Studies ◽

Hdl Cholesterol ◽

P Value ◽

Genome Wide Association Studies ◽

Cholesterol Levels ◽

Genome Wide ◽

The Impact ◽

Apparent Ability

Background/Objectives: Gout is a painful arthritic disease. A robust canon of observational literature suggests strong relationships between obesity, high urate levels, and gout. But findings from observational studies can be fraught with confounding and reverse causation. They can conflict with findings from Mendelian randomization (MR), designed to tackle these biases. We aimed to determine whether the relationships between obesity, higher urate levels, and gout were causal using multiple MR approaches, including an investigation of how other closely related traits, LDL, HDL cholesterol, and triglyceride levels fit into the picture. Subjects/Methods: Summary results from genome-wide association studies of the five above-mentioned traits were extracted and used to perform two-sample (univariable, multivariable, and two-step) MR and MR mediation analysis. Results Obesity increased urate (beta=0.127; 95% CI=0.098, 0.157; P-value=1.2E-17) and triglyceride levels (beta=0.082; 95% CI=0.065, 0.099; P-value=1.2E-21) and decreased HDL cholesterol levels (beta=-0.083; 95% CI=-0.101, -0.065; P-value=2.5E-19). Higher triglyceride levels increased urate levels (beta=0.198; 95% CI=0.146, 0.251; P-value=8.9E-14) and higher HDL levels decreased them (beta=-0.109; 95% CI=-0.148, -0.071; P-value=2.7E-08). Higher urate levels (OR=1.030; 95% CI=1.028, 1.032; P-value=1.1E-130) and obesity caused gout (OR=1.003; 95% CI=1.001, 1.004; P-value=1.3E-04). The mediation MR of obesity on gout with urate levels as a mediator revealed, however, that essentially all of the effect of obesity on gout is mediated through urate. The impact of obesity on LDL cholesterol was null (beta=-0.011; 95% CI=-0.030, 0.008; P-value=2.6E-01), thus it was not included in the multivariable MR. The multivariable MR of obesity, HDL cholesterol, and triglycerides on urate levels revealed that obesity has an effect on urate levels even when accounting for HDL cholesterol and triglyceride levels. Conclusions: Obesity impacts gout indirectly by influencing urate levels and possibly other traits, such as triglycerides, that increase urate levels. Obesity's impact on urate is exacerbated by its apparent ability to decrease HDL cholesterol.

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Longer sleep duration may negatively affect renal function

European Heart Journal ◽

10.1093/ehjci/ehaa946.2373 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

M Mazidi ◽

N Shekoohi ◽

N Katsiki ◽

M Banach

Keyword(s):

Kidney Function ◽

Sleep Duration ◽

Total Population ◽

Association Studies ◽

P Value ◽

Genome Wide Association Studies ◽

Single Single Nucleotide Polymorphism ◽

Inverse Variance ◽

Leave One Out ◽

The Impact

Abstract Background Observational studies evaluating the link between sleep duration and kidney function reported controversial results. In the present study, Mendelian Randomization (MR) analysis was applied to obtain unconfounded estimates of the casual association of genetically determined sleep duration with estimated glomerular filtration rate (eGFR) and the risk of chronic kidney disease (CKD). Methods Data from the largest genome-wide association studies (GWAS) on self-reported and accelerometer derived sleep duration, eGFR and CKD were analysed in total, as well as separately in diabetic and non-diabetic individuals. Inverse variance weighted method (IVW), weighted median (WM)-based method, MR-Egger, as well as MR-Pleiotropy RESidual Sum and Outlier (PRESSO) were applied. To rule out the impact of single single-nucleotide polymorphism (SNP), the leave-one-out method was used. Results Overall, individuals with genetically longer self-reported sleep duration had a higher CKD risk (IVW: beta=0.358, p=0.047). Furthermore, in non-diabetics, longer self-reported sleep duration was negatively associated eGFR (IVW: beta=−0.024, p=0.020). Similarly, accelerometer derived sleep duration was negatively related to eGFR in the total population (IVW: beta=−0.019, p=0.047) and the non-diabetic individuals (IVW: beta=−0.025, p=0.014) (Table). No significant association was found between self-reported sleep duration and eGFR in the whole population (IVW: beta=−0.019, p=0.072) and T2DM patients (IVW: beta=0.028, p=0.484). None of the estimated associations was subjected to a significant level of heterogeneity. Furthermore, MR-PRESSO analysis did not show any chance of outliers for all estimates. The pleiotropy test, with very negligible intercept and insignificant p value. The results of the MR-RAPS were identical with the IVW estimates, highlighting again no possibility of pleiotropy. The leave-one-out method demonstrated that the links were not driven by single SNPs. Conclusions For the first time, the present study shed a light on the potential harmful effects of longer sleep duration (measured both objectively and subjectively) on kidney function. This finding was observed in the total population and in non-diabetic individuals, but not in those with diabetes. Further research is needed to elucidate the links between sleep duration, eGFR and CKD. Funding Acknowledgement Type of funding source: None

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Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study

10.1101/2020.05.04.076661 ◽

2020 ◽

Author(s):

Héctor Climente-González ◽

Christine Lonjou ◽

Fabienne Lesueur ◽

Dominique Stoppa-Lyonnet ◽

Nadine Andrieu ◽

...

Keyword(s):

Breast Cancer ◽

Biological Networks ◽

Familial Breast Cancer ◽

Cancer Susceptibility ◽

Breast Cancer Susceptibility ◽

Genome Wide Association ◽

Cancer Susceptibility Genes ◽

Genome Wide ◽

Network Methods ◽

Core Genes

AbstractNetwork approaches to disease use biological networks, which model functional relationships between the molecules in a cell, to generate hypotheses about the genetics of complex diseases. Several among them jointly consider gene scores, representing the association between each gene and the disease, and the biological context of each gene, modeled by a network. Here, we study six such network methods using gene scores from GENESIS, a genome-wide association study (GWAS) on French women with non-BRCA familial breast cancer. We provide a critical comparison of these six methods, discussing the impact of their mathematical formulation and parameters. Using a biological network yields more compelling results than standard GWAS analyses. Indeed, we find significant overlaps between our solutions and the genes identified in the largest GWAS on breast cancer susceptibility. We further propose to combine these solutions into a consensus network, which brings further insights. The consensus network contains COPS5, a gene related to multiple hallmarks of cancer, and 14 of its neighbors. The main drawback of network methods is that they are not robust to small perturbations in their inputs. Therefore, we propose a stable consensus solution, formed by the most consistently selected genes in multiple subsamples of the data. In GENESIS, it is composed of 68 genes, enriched in known breast cancer susceptibility genes (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10 4) and occupying more central positions in the network than most genes. The network is organized around CUL3, which is involved in the regulation of several genes linked to cancer progression. In conclusion, we showed how network methods help overcome the lack of statistical power of GWAS and improve their interpretation. Project-agnostic implementations of all methods are available at https://github.com/hclimente/gwas-tools.Author summaryGenome-wide association studies (GWAS) scan thousands of genomes to identify variants associated with a complex trait. Over the last 15 years, GWAS have advanced our understanding of the genetics of complex diseases, and in particular of hereditary cancers. However, they have led to an apparent paradox: the more we perform such studies, the more it seems that the entire genome is involved in every disease. The omnigenic model offers an appealing explanation: only a limited number of core genes are directly involved in the disease, but gene functions are deeply interrelated, and so many other genes can alter the function of the core genes. These interrelations are often modeled as networks, and multiple algorithms have been proposed to use these networks to identify the subset of core genes involved in a specific trait. This study applies and compares six such network methods on GENESIS, a GWAS dataset for familial breast cancer in the French population. Combining these approaches allows us to identify potentially novel breast cancer susceptibility genes and provides a mechanistic explanation for their role in the development of the disease. We provide ready-to-use implementations of all the examined methods.

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The impact of personality on the risk and survival of breast cancer: a Mendelian randomization analysis

Psychological Medicine ◽

10.1017/s0033291721001562 ◽

2021 ◽

pp. 1-7

Author(s):

Li Ying ◽

Songzan Chen ◽

Ling Li ◽

Zhijun Pan

Keyword(s):

Breast Cancer ◽

Mendelian Randomization ◽

Association Studies ◽

Causative Role ◽

Genome Wide Association Studies ◽

Luminal A ◽

Mendelian Randomization Analysis ◽

Increased Risk ◽

The Impact ◽

Randomization Analysis

Abstract Background It has long been hypothesized that personality plays a causative role in incidence and outcome of breast cancer (BC), but epidemiological evidence of association between personality and BC is inconsistent. Method We used two-sample Mendelian randomization analysis to estimate the impact of personality on the risk and survival of BC. In total, 109 single nucleotide polymorphisms (SNPs) were utilized as instruments of neuroticism from a large-scale Genome-Wide Association Studies (GWAS), and five SNPs were utilized as instruments of extraversion from Genetic of Personality Consortium and 23andMe. Genetic association with the risk and survival of overall and individual subtype BC were obtained from the Breast Cancer Association Consortium. Result Neuroticism is significantly associated with the risk of overall BC [odds ratio (OR) 1.06; 95% confidence interval (CI) 1.01–1.11; p = 0.015] and the risk of luminal A BC (OR 1.09; 95% CI 1.03–1.16; p = 0.004). Extraversion is not associated with the risk of BC. None of neuroticism or extraversion is associated with the survival of BC. Conclusion Neuroticism was associated with a modest increased risk of BC and particularly luminal A BC.

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Abstract 15527: Association Between Adrenergic Receptor Modulation and the Risk of Heart Failure: A Two-sample Mendelian Randomization Study

Circulation ◽

10.1161/circ.142.suppl_3.15527 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Claire Baudier ◽

Françoise Fougerousse ◽

Amand F Schmidt ◽

Folkert W Asselbergs ◽

Mickael Guedj ◽

...

Keyword(s):

Heart Failure ◽

Mendelian Randomization ◽

Association Studies ◽

P Value ◽

Sample Population ◽

Genome Wide Association Studies ◽

Receptor Modulation ◽

Genome Wide ◽

Genes Encoding ◽

The Impact

Introduction: The impact of the sympathetic nervous system (SNS) modulation on the risk of heart failure (HF) outside of ß1 receptor blockade remains controversial. Methods: We performed a two-sample Mendelian randomization (MR) study using common independent genetic variants located in the cis region of genes encoding the 9 SNS receptors (α1 A, B, D, α2 A, B, C and ß 1, 2 and 3) that were associated at genome-wide significance (P-value ≤ 5х10 –8 ) with blood pressure (BP) and/or heart rate (HR) in published genome-wide association studies (GWAS) available for BP and HR. Variants were filtered out by Linkage Disequilibrium clumping (LD R 2 > 0.1) and based on their minor allele frequency (MAF < 0.01). The effects of selected variants on the genetic risk of HF were extracted from a GWAS of HF from the HERMES consortium, based on a non-overlapping sample population. MR estimates were obtained using the Wald estimator for a single variant or the inverse variance weighted method for multiple variants. Results: 542,362 controls and 40,805 HF cases were evaluated. Independent variants in genes encoding 4 SNS receptors associated with BP or HR were identified as follows: α1A (diastolic BP), α2B (diastolic BP and HR), ß1 and ß2 (diastolic and systolic BP). MR analysis of α1A and ß1, weighted by their effects on diastolic BP, estimated an association with a higher risk of HF, while α2B variants were associated with a lower risk. We found no evidence for an effect of ß2. A similar relationship with systolic BP was found for ß1 and ß2. HR increasing effect of α2B variants was associated with a decreased odd of HF. Conclusions: Mindful of pleiotropic effects, these findings are consistent with the known benefits of ß1 blockade in HF and support a similar role for α1A blockade; conversely, they suggest a detrimental lowering effect of BP and HR through α2B modulation that deserves further studies. No evidence for a role of ß2 in HF was found.

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MicroRNAs as Biomarker for Breast Cancer

Endocrine Metabolic & Immune Disorders - Drug Targets ◽

10.2174/1871530320666200428113051 ◽

2020 ◽

Vol 20 (10) ◽

pp. 1597-1610 ◽

Cited By ~ 2

Author(s):

Taru Aggarwal ◽

Ridhima Wadhwa ◽

Riya Gupta ◽

Keshav Raj Paudel ◽

Trudi Collet ◽

...

Keyword(s):

Breast Cancer ◽

Association Studies ◽

Large Family ◽

Breast Cancer Diagnosis ◽

Cell Multiplication ◽

Cell Physiology ◽

Gene Transcript ◽

Genome Wide Association Studies ◽

Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

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TCF19 Impacts a Network of Inflammatory and DNA Damage Response Genes in the Pancreatic β-Cell

Metabolites ◽

10.3390/metabo11080513 ◽

2021 ◽

Vol 11 (8) ◽

pp. 513

Author(s):

Grace H. Yang ◽

Danielle A. Fontaine ◽

Sukanya Lodh ◽

Joseph T. Blumer ◽

Avtar Roopra ◽

...

Keyword(s):

Gene Expression ◽

Dna Damage ◽

Dna Damage Response ◽

Association Studies ◽

Cell Cycle Gene ◽

Genome Wide Association Studies ◽

Β Cell ◽

Nucleotide Incorporation ◽

Damage Response ◽

The Impact

Transcription factor 19 (TCF19) is a gene associated with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in genome-wide association studies. Prior studies have demonstrated that Tcf19 knockdown impairs β-cell proliferation and increases apoptosis. However, little is known about its role in diabetes pathogenesis or the effects of TCF19 gain-of-function. The aim of this study was to examine the impact of TCF19 overexpression in INS-1 β-cells and human islets on proliferation and gene expression. With TCF19 overexpression, there was an increase in nucleotide incorporation without any change in cell cycle gene expression, alluding to an alternate process of nucleotide incorporation. Analysis of RNA-seq of TCF19 overexpressing cells revealed increased expression of several DNA damage response (DDR) genes, as well as a tightly linked set of genes involved in viral responses, immune system processes, and inflammation. This connectivity between DNA damage and inflammatory gene expression has not been well studied in the β-cell and suggests a novel role for TCF19 in regulating these pathways. Future studies determining how TCF19 may modulate these pathways can provide potential targets for improving β-cell survival.

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