Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

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Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

10.1101/2020.07.02.184028 ◽

2020 ◽

Author(s):

Robin N. Beaumont ◽

Isabelle K. Mayne ◽

Rachel M. Freathy ◽

Caroline F. Wright

Keyword(s):

Birth Weight ◽

Statistical Power ◽

Developmental Disorders ◽

Association Studies ◽

Later Life ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Fetal Effect ◽

Common Genetic Variants ◽

Causal Genes

AbstractBirth weight is an important factor in newborn and infant survival, and both low and high birth weights are associated with adverse later life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with either maternal or fetal effects on birth weight. Knowledge of the underlying causal genes and pathways is crucial to understand how these loci influence birth weight, and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme upper or lower ends of the normal distribution, and genes implicated in those syndromes may provide valuable information to help prioritise candidate genes at GWAS loci. We examined the proximity of genes implicated in developmental disorders to birth weight GWAS loci at which a fetal effect is either likely or cannot be ruled out. We used simulations to test whether those genes fall disproportionately close to the GWAS loci. We found that birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected by chance. This is the case both when the developmental disorder gene is the nearest gene to the birth weight SNP and also when examining all genes within 258kb of the SNP. This enrichment was driven by genes that cause monogenic developmental disorders with dominant modes of inheritance. We found several examples of SNPs located in the intron of one gene that mark plausible effects via different nearby genes implicated in monogenic short stature, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight loci, which has helped identify GWAS loci likely to have direct fetal effects on birth weight which could not previously be classified as fetal or maternal due to insufficient statistical power.

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Identifying Thyroid Carcinoma-Related Genes by Integrating GWAS and eQTL Data

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.645275 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fei Shen ◽

Xiaoxiong Gan ◽

Ruiying Zhong ◽

Jianhua Feng ◽

Zhen Chen ◽

...

Keyword(s):

Thyroid Cancer ◽

Thyroid Carcinoma ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Cancer Pathogenesis ◽

Genome Wide ◽

Causal Genes ◽

Eqtl Data

Thyroid carcinoma (TC) is the most common endocrine malignancy. The incidence rate of thyroid cancer has increased rapidly in recent years. The occurrence and development of thyroid cancers are highly related to the massive genetic and epigenetic changes. Therefore, it is essential to explore the mechanism of thyroid cancer pathogenesis. Genome-Wide Association Studies (GWAS) have been widely used in various diseases. Researchers have found multiple single nucleotide polymorphisms (SNPs) are significantly related to TC. However, the biological mechanism of these SNPs is still unknown. In this paper, we used one GWAS dataset and two eQTL datasets, and integrated GWAS with expression quantitative trait loci (eQTL) in both thyroid and blood to explore the mechanism of mutations and causal genes of thyroid cancer. Finally, we found rs1912998 regulates the expression of IGFALS (P = 1.70E-06) and HAGH (P = 5.08E-07) in thyroid, which is significantly related to thyroid cancer. In addition, KEGG shows that these genes participate in multiple thyroid cancer-related pathways.

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Modeling diseases in multiple mouse strains for precision medicine studies

Physiological Genomics ◽

10.1152/physiolgenomics.00123.2016 ◽

2017 ◽

Vol 49 (3) ◽

pp. 177-179 ◽

Cited By ~ 3

Author(s):

Andrés D. Klein

Keyword(s):

Statistical Power ◽

Association Studies ◽

Phenotypic Variability ◽

Inbred Strains ◽

Mouse Strains ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Genome Wide ◽

Number Of Patients

The genetic basis of the phenotypic variability observed in patients can be studied in mice by generating disease models through genetic or chemical interventions in many genetic backgrounds where the clinical phenotypes can be assessed and used for genome-wide association studies (GWAS). This is particularly relevant for rare disorders, where patients sharing identical mutations can present with a wide variety of symptoms, but there are not enough number of patients to ensure statistical power of GWAS. Inbred strains are homozygous for each loci, and their single nucleotide polymorphisms catalogs are known and freely available, facilitating the bioinformatics and reducing the costs of the study, since it is not required to genotype every mouse. This kind of approach can be applied to pharmacogenomics studies as well.

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From the Genetics of Ankylosing Spondylitis to New Biology and Drug Target Discovery

Frontiers in Immunology ◽

10.3389/fimmu.2021.624632 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zaarour Nancy ◽

Li Yan ◽

Shi Hui ◽

Bowness Paul ◽

Chen Liye

Keyword(s):

Ankylosing Spondylitis ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Drug Target Discovery ◽

Gwas Study ◽

Translational Study ◽

Disease Biology ◽

Genome Wide ◽

Causal Genes

Genome-wide association studies (GWAS) have identified 113 single nucleotide polymorphisms (SNPs) affecting the risk of developing ankylosing spondylitis (AS), and an on-going GWAS study will likely identify 100+ new risk loci. The translation of genetic findings to novel disease biology and treatments has been difficult due to the following challenges: (1) difficulties in determining the causal genes regulated by disease-associated SNPs, (2) difficulties in determining the relevant cell-type(s) that causal genes exhibit their function(s), (3) difficulties in determining appropriate cellular contexts to interrogate the functional role of causal genes in disease biology. This review will discuss recent progress and unanswered questions with a focus on these challenges. Additionally, we will review the investigation of biology and the development of drugs related to the IL-23/IL-17 pathway, which has been partially driven by the AS genetics, and discuss what can be learned from these studies for the future functional and translational study of AS-associated genes.

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Common genetic variants associated with Parkinson’s disease display widespread signature of epigenetic plasticity

Scientific Reports ◽

10.1038/s41598-019-54865-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 11

Author(s):

Amit Sharma ◽

Naoki Osato ◽

Hongde Liu ◽

Shailendra Asthana ◽

Tikam Chand Dakal ◽

...

Keyword(s):

Binding Sites ◽

Dopaminergic Neurons ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Additional Layer ◽

Genome Wide ◽

Intron 1 ◽

Common Genetic Variants

AbstractParkinson disease (PD) is characterized by a pivotal progressive loss of substantia nigra dopaminergic neurons and aggregation of α-synuclein protein encoded by the SNCA gene. Genome-wide association studies identified almost 100 sequence variants linked to PD in SNCA. However, the consequences of this genetic variability are rather unclear. Herein, our analysis on selective single nucleotide polymorphisms (SNPs) which are highly associated with the PD susceptibility revealed that several SNP sites attribute to the nucleosomes and overlay with bivalent regions poised to adopt either active or repressed chromatin states. We also identified large number of transcription factor (TF) binding sites associated with these variants. In addition, we located two docking sites in the intron-1 methylation prone region of SNCA which are required for the putative interactions with DNMT1. Taken together, our analysis reflects an additional layer of epigenomic contribution for the regulation of the SNCA gene in PD.

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Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

10.1101/2021.09.24.461734 ◽

2021 ◽

Author(s):

Jenna Hershberger ◽

Ryokei Tanaka ◽

Joshua C. Wood ◽

Nicholas Kaczmar ◽

Di Wu ◽

...

Keyword(s):

Statistical Power ◽

Prediction Models ◽

Sweet Corn ◽

Association Studies ◽

A Priori ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Causal Genes ◽

Corn Kernels

Sweet corn is consistently one of the most highly consumed vegetables in the U.S., providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step towards understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.

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Genetic Predictors of Lithium Response

V M BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY ◽

10.31363/2313-7053-2019-4-1-26-27 ◽

2019 ◽

pp. 26-27

Author(s):

U. Heilbronner

Keyword(s):

Statistical Power ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Response Scale ◽

Single Nucleotide ◽

Genetic Predictors ◽

Genome Wide ◽

Lithium Response ◽

Initial Genome

Lithium remains a first-line pharmacological treatment of bipolar disorder (BD). However, treatment response is heterogeneous, with several lines of evidence implicating genetic factors. Unfortunately, neither hypothesis-driven approaches nor initial genome-wide association studies (GWAS) were successful in identifying genetic drivers of response heterogeneity, probably due to low statistical power and different phenotype measurements. Recently, a GWAS of the Consortium of Lithium Genetics (ConLiGen) has identified four single nucleotide polymorphisms (SNPs) mediating response to lithium, located in genes for two long non-coding RNAs. This success was only possible by international collaboration and the use of an established lithium response scale. The findings await further replication.

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AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2015-0074 ◽

2016 ◽

Vol 15 (2) ◽

Cited By ~ 4

Author(s):

Mathieu Emily

Keyword(s):

Statistical Power ◽

Association Studies ◽

Case Control ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Genome Wide ◽

Biological Interpretation ◽

Limited Power ◽

Control Association

AbstractAmong the large of number of statistical methods that have been proposed to identify gene-gene interactions in case-control genome-wide association studies (GWAS), gene-based methods have recently grown in popularity as they confer advantage in both statistical power and biological interpretation. All of the gene-based methods jointly model the distribution of single nucleotide polymorphisms (SNPs) sets prior to the statistical test, leading to a limited power to detect sums of SNP-SNP signals. In this paper, we instead propose a gene-based method that first performs SNP-SNP interaction tests before aggregating the obtained

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Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies

Biometrika ◽

10.1093/biomet/asaa098 ◽

2020 ◽

Author(s):

Huijuan Zhou ◽

Xianyang Zhang ◽

Jun Chen

Keyword(s):

Error Rate ◽

Rate Control ◽

Multiple Testing ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Family Wise Error Rate ◽

Genome Wide

Abstract The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase the detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focus on the false discovery rate control while covariate-adaptive FWER-controlling procedures remain under-developed. Here we propose a novel covariate-adaptive FWER-controlling procedure that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyze 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

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The open targets post-GWAS analysis pipeline

Bioinformatics ◽

10.1093/bioinformatics/btaa020 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2936-2937 ◽

Cited By ~ 4

Author(s):

Gareth Peat ◽

William Jones ◽

Michael Nuhn ◽

José Carlos Marugán ◽

William Newell ◽

...

Keyword(s):

Drug Targets ◽

Gene Expression Regulation ◽

Association Studies ◽

Genome Wide Association Studies ◽

Protein Coding ◽

Data Resource ◽

Coding Regions ◽

Genome Wide ◽

Causal Genes ◽

Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

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