Chemical Genetic Validation of GWAS-derived Disease Loci

2021 ◽  
Vol 16 ◽  
Author(s):  
Yuan Quan ◽  
Hong-Yu Zhang
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Target Genes ◽  
Association Studies ◽  
Chemical Genetics ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Chemical Genetic ◽  
Disease Loci ◽  
Underlying Pathogenesis

Background: Genome-wide association studies (GWAS) have opened the door to unprecedented large-scale identification of susceptibility loci for human diseases and traits. However, it is still a great challenge to validate these loci and elucidate how these sequence variants give rise to the genetic and phenotypic changes. Because many drug targets are genetic disease genes and the general drug mode of action (MoA, agonist or antagonist) is in line with the consequence of target gene mutations (loss-of-function (LOF) or gain-of-function (GOF)), here we propose a chemical genetic method to address the above issues of GWAS. Objective: This study intends to use chemical genetics information to validate GWAS-derived disease loci and interpret their underlying pathogenesis. Method: We conducted a comprehensive comparative analysis on GWAS data and drug/target information (chemical genetics information). Results: We have identified hundreds of GWAS-derived disease loci which are linked to drug target genes and have matched disease traits and drug indications. It is interesting to note that more than 40% genes have been recognized as disorder factors, indicating the potential power of chemical genetic validation. The pathogenesis of these loci was inferred by corresponding drug MoA. Some inferences were supported by prior experimental observations; some were interpreted in terms of microRNA regulation, codon usage bias, and transcriptional regulation, in particular the transcription factorbinding affinity variation induced by disease-causing mutations. Conclusion: In summary, chemical genetics information is useful to validate GWAS-derived disease loci and to interpret their underlying pathogenesis as well, which has important implications not only in medical genetics but also in methodology evaluation of GWAS.

scholarly journals Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis

2018 ◽  
Author(s):  
Satish K Nandakumar ◽  
Sean K McFarland ◽  
Laura Marlene Mateyka ◽  
Caleb A Lareau ◽  
Jacob C Ulirsch ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Target Genes ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genome Wide ◽  
Causal Variants ◽  
Genomic Regions

Genome-wide association studies (GWAS) have identified thousands of variants associated with human diseases and traits. However, the majority of GWAS-implicated variants are in non-coding genomic regions and require in depth follow-up to identify target genes and decipher biological mechanisms. Here, rather than focusing on causal variants, we have undertaken a pooled loss-of-function screen in primary hematopoietic cells to interrogate 389 candidate genes contained in 75 loci associated with red blood cell traits. Using this approach, we identify 77 genes at 38 GWAS loci, with most loci harboring 1-2 candidate genes. Importantly, the hit set was strongly enriched for genes validated through orthogonal genetic approaches. Genes identified by this approach are enriched in relevant biological pathways, allowing regulators of human erythropoiesis and blood disease modifiers to be defined. More generally, this functional screen provides a paradigm for gene-centric follow up of GWAS for a variety of human diseases and traits.

scholarly journals Genetics of Atopic Dermatitis: From DNA Sequence to Clinical Relevance

Dermatology ◽  
2019 ◽  
Vol 235 (5) ◽  
pp. 355-364 ◽  
Author(s):  
Mari Løset ◽  
Sara J. Brown ◽  
Marit Saunes ◽  
Kristian Hveem
Keyword(s):  
Atopic Dermatitis ◽  
Genetic Risk ◽  
Drug Targets ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Skin Barrier Function ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Sequencing

Atopic dermatitis (AD) is a complex disease that is thought to be triggered by environmental factors in genetically susceptible individuals. Twin studies have estimated the heritability of AD to be approximately 75%, with the null (loss-of-function) mutations of the gene encoding filaggrin (FLG) (chromosome 1q21.3) as the strongest known genetic risk factor. The discovery of the filaggrin gene was important in the emerging model for AD pathogenesis, combining skin barrier function with adaptive and innate immunity. Assisted by the recent development of large-scale high-throughput genomics, more than 30 genetic loci have been linked to AD across different populations. Identification of these loci, together with functional studies, has already provided new insights into disease biology and identified novel drug targets. Further, these susceptibility loci are laying the groundwork for phenome-wide association studies to test their multiple phenotype relationships and application of Mendelian randomization to investigate causal relationships. Despite many known genes, a majority of the genetic risk for AD is yet unexplored. Therefore, studies investigating refined phenotype groups, low-frequency and rare genetic variation, gene-gene and/or gene-environment interactions, epigenetic mechanisms and data from multi-omics technologies are warranted. In this review, we describe genetic discoveries for AD, including results from candidate gene studies, studies of AD-like genetic diseases, genome-wide association studies and genetic sequencing studies. We explain how some of these genetic discoveries have unraveled new mechanistic insights into the pathogenesis of AD and exemplify how personal genetic data could be used for preventive strategies and a tailored treatment regimen (i.e., precision medicine).

scholarly journals Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Satish K Nandakumar ◽  
Sean K McFarland ◽  
Laura M Mateyka ◽  
Caleb A Lareau ◽  
Jacob C Ulirsch ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Target Genes ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Variants

Genome-wide association studies (GWAS) have identified thousands of variants associated with human diseases and traits. However, the majority of GWAS-implicated variants are in non-coding regions of the genome and require in depth follow-up to identify target genes and decipher biological mechanisms. Here, rather than focusing on causal variants, we have undertaken a pooled loss-of-function screen in primary hematopoietic cells to interrogate 389 candidate genes contained in 75 loci associated with red blood cell traits. Using this approach, we identify 77 genes at 38 GWAS loci, with most loci harboring 1–2 candidate genes. Importantly, the hit set was strongly enriched for genes validated through orthogonal genetic approaches. Genes identified by this approach are enriched in specific and relevant biological pathways, allowing regulators of human erythropoiesis and modifiers of blood diseases to be defined. More generally, this functional screen provides a paradigm for gene-centric follow up of GWAS for a variety of human diseases and traits.

scholarly journals GWAS of serum ALT and AST reveals an association of SLC30A10 Thr95Ile with hypermanganesemia symptoms

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucas D. Ward ◽  
Ho-Chou Tu ◽  
Chelsea B. Quenneville ◽  
Shira Tsour ◽  
Alexander O. Flynn-Carroll ◽  
...  
Keyword(s):  
Bile Duct ◽  
Extrahepatic Bile Duct ◽  
Association Studies ◽  
Missense Variant ◽  
Deficiency Anemia ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Extrahepatic Bile Duct Cancer ◽  
Hepatocellular Damage ◽  
Increased Risk

AbstractUnderstanding mechanisms of hepatocellular damage may lead to new treatments for liver disease, and genome-wide association studies (GWAS) of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) serum activities have proven useful for investigating liver biology. Here we report 100 loci associating with both enzymes, using GWAS across 411,048 subjects in the UK Biobank. The rare missense variant SLC30A10 Thr95Ile (rs188273166) associates with the largest elevation of both enzymes, and this association replicates in the DiscovEHR study. SLC30A10 excretes manganese from the liver to the bile duct, and rare homozygous loss of function causes the syndrome hypermanganesemia with dystonia-1 (HMNDYT1) which involves cirrhosis. Consistent with hematological symptoms of hypermanganesemia, SLC30A10 Thr95Ile carriers have increased hematocrit and risk of iron deficiency anemia. Carriers also have increased risk of extrahepatic bile duct cancer. These results suggest that genetic variation in SLC30A10 adversely affects more individuals than patients with diagnosed HMNDYT1.

scholarly journals Genetic architecture of schizophrenia: a review of major advancements

2021 ◽  
pp. 1-10
Author(s):  
Sophie E. Legge ◽  
Marcos L. Santoro ◽  
Sathish Periyasamy ◽  
Adeniran Okewole ◽  
Arsalan Arsalan ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Copy Number Variants ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Loci ◽  
Significant Enrichment ◽  
High Heritability ◽  
Coding Variants

Abstract Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

scholarly journals Cis-Segregation of c.1171C>T Stop Codon (p.R391*) in SERPINC1 Gene and c.1691G>A Transition (p.R506Q) in F5 Gene and Selected GWAS Multilocus Approach in Inherited Thrombophilia

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 934
Author(s):  
Donato Gemmati ◽  
Giovanna Longo ◽  
Eugenia Franchini ◽  
Juliana Araujo Silva ◽  
Ines Gallo ◽  
...  
Keyword(s):  
At Risk ◽  
Stop Codon ◽  
Association Studies ◽  
Premature Stop Codon ◽  
Large Family ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Gene Markers ◽  
Inherited Thrombophilia ◽  
Fv Leiden

Inherited thrombophilia (e.g., venous thromboembolism, VTE) is due to rare loss-of-function mutations in anticoagulant factors genes (i.e., SERPINC1, PROC, PROS1), common gain-of-function mutations in procoagulant factors genes (i.e., F5, F2), and acquired risk conditions. Genome Wide Association Studies (GWAS) recently recognized several genes associated with VTE though gene defects may unpredictably remain asymptomatic, so calculating the individual genetic predisposition is a challenging task. We investigated a large family with severe, recurrent, early-onset VTE in which two sisters experienced VTE during pregnancies characterized by a perinatal in-utero thrombosis in the newborn and a life-saving pregnancy-interruption because of massive VTE, respectively. A nonsense mutation (CGA > TGA) generating a premature stop-codon (c.1171C>T; p.R391*) in the exon 6 of SERPINC1 gene (1q25.1) causing Antithrombin (AT) deficiency and the common missense mutation (c.1691G>A; p.R506Q) in the exon 10 of F5 gene (1q24.2) (i.e., FV Leiden; rs6025) were coinherited in all the symptomatic members investigated suspecting a cis-segregation further confirmed by STR-linkage-analyses [i.e., SERPINC1 IVS5 (ATT)5–18, F5 IVS2 (AT)6–33 and F5 IVS11 (GT)12–16] and SERPINC1 intragenic variants (i.e., rs5878 and rs677). A multilocus investigation of blood-coagulation balance genes detected the coexistence of FV Leiden (rs6025) in trans with FV HR2-haplotype (p.H1299R; rs1800595) in the aborted fetus, and F11 rs2289252, F12 rs1801020, F13A1 rs5985, and KNG1 rs710446 in the newborn and other members. Common selected gene variants may strongly synergize with less common mutations tuning potential life-threatening conditions when combined with rare severest mutations. Merging classic and newly GWAS-identified gene markers in at risk families is mandatory for VTE risk estimation in the clinical practice, avoiding partial risk score evaluation in unrecognized at risk patients.

scholarly journals Optimized permutation testing for information theoretic measures of multi-gene interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
James M. Kunert-Graf ◽  
Nikita A. Sakhanenko ◽  
David J. Galas
Keyword(s):  
Large Scale ◽  
Permutation Test ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Permutation Testing ◽  
Exact Test ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Full Analysis ◽  
Computational Bottleneck

Abstract Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. Results In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples. Conclusions The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.

Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies

2016 ◽  
Vol 27 (9) ◽  
pp. 2657-2673 ◽  
Author(s):  
Mathieu Emily
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Disease Model ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Power Functions ◽  
Power Comparison ◽  
Powerful Test ◽  
Armitage Trend Test ◽  
Mode Of Inheritance

The Cochran-Armitage trend test (CA) has become a standard procedure for association testing in large-scale genome-wide association studies (GWAS). However, when the disease model is unknown, there is no consensus on the most powerful test to be used between CA, allelic, and genotypic tests. In this article, we tackle the question of whether CA is best suited to single-locus scanning in GWAS and propose a power comparison of CA against allelic and genotypic tests. Our approach relies on the evaluation of the Taylor decompositions of non-centrality parameters, thus allowing an analytical comparison of the power functions of the tests. Compared to simulation-based comparison, our approach offers the advantage of simultaneously accounting for the multidimensionality of the set of features involved in power functions. Although power for CA depends on the sample size, the case-to-control ratio and the minor allelic frequency (MAF), our results first show that it is largely influenced by the mode of inheritance and a deviation from Hardy–Weinberg Equilibrium (HWE). Furthermore, when compared to other tests, CA is shown to be the most powerful test under a multiplicative disease model or when the single-nucleotide polymorphism largely deviates from HWE. In all other situations, CA lacks in power and differences can be substantial, especially for the recessive mode of inheritance. Finally, our results are illustrated by the comparison of the performances of the statistics in two genome scans.

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

2012 ◽  
Vol 215 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Georg Homuth ◽  
Alexander Teumer ◽  
Uwe Völker ◽  
Matthias Nauck
Keyword(s):  
Blood Cells ◽  
Large Scale ◽  
Genetic Factors ◽  
Association Studies ◽  
Transcriptional Profiling ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Future Developments ◽  
Genome Wide ◽  
Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

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