Genetics of Parkinson's disease Lill, Christina M.
Molecular and cellular probes,
December 2016, 2016-12-00, 20161201, Volume:
30, Issue:
6
Journal Article
Peer reviewed
Almost two decades after the identification of SNCA as the first causative gene in Parkinson's disease (PD) and the subsequent understanding that genetic factors play a substantial role in PD ...development, our knowledge of the genetic architecture underlying this disease has vastly improved. Approximately 5–10% of patients suffer from a monogenic form of PD where autosomal dominant mutations in SNCA, LRRK2, and VPS35 and autosomal recessive mutations in PINK1, DJ-1, and Parkin cause the disease with high penetrance. Furthermore, recent whole-exome sequencing have described autosomal recessive DNAJC6 mutations in predominately atypical, but also cases with typical PD. In addition, several other genes have been linked to atypical Parkinsonian phenotypes. However, the vast majority of PD is genetically complex, i.e. it is caused by the combined action of common genetic variants in concert with environmental factors. By the application of genome-wide association studies, 26 PD risk loci have been established to date. Similar to other genetically complex diseases, these show only moderate effects on PD risk. Increasing this etiologic complexity, many of the involved genetic and environmental risk factors likely interact in an intricate fashion. This article aims to provide a comprehensive overview of the current knowledge in PD genetics.
Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant ...SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
Zymoseptoria tritici is the causal agent of Septoria tritici blotch, a major pathogen of wheat globally and the most damaging pathogen of wheat in Europe. A gene-for-gene (GFG) interaction between Z. ...tritici and wheat cultivars carrying the Stb6 resistance gene has been postulated for many years, but the genes have not been identified.
We identified AvrStb6 by combining quantitative trait locus mapping in a cross between two Swiss strains with a genome-wide association study using a natural population of c. 100 strains from France. We functionally validated AvrStb6 using ectopic transformations.
AvrStb6 encodes a small, cysteine-rich, secreted protein that produces an avirulence phenotype on wheat cultivars carrying the Stb6 resistance gene. We found 16 nonsynonymous single nucleotide polymorphisms among the tested strains, indicating that AvrStb6 is evolving very rapidly. AvrStb6 is located in a highly polymorphic subtelomeric region and is surrounded by transposable elements, which may facilitate its rapid evolution to overcome Stb6 resistance.
AvrStb6 is the first avirulence gene to be functionally validated in Z. tritici, contributing to our understanding of avirulence in apoplastic pathogens and the mechanisms underlying GFG interactions between Z. tritici and wheat.
Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps ...may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. The H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and post-association analysis of results. The workflow is scalable--laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker or Singularity.
Summary
Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of ...metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome‐wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass‐spectrometry‐based analytical systems and genome sequencing technologies. Metabolic genome‐wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS‐based multi‐dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS‐based multi‐dimensional analysis and emerging insights into the diversity of metabolism.
Significance Statement
In this review, we highlight some recent progress in the dissection of metabolic diversity, with an emphasis on metabolic genome‐wide association study (mGWAS) and mGWAS‐based multi‐dimensional analysis.
Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and informative than a series of univariate ...analyses. However, in most cases, studies of genotype-phenotype relationships have been analyzed only one trait at a time. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the
wing in the
Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different laboratories. We found 2396 significant SNPs using a 5% false discovery rate cutoff in the multivariate analyses, but just four significant SNPs in univariate analyses of scores on the first 20 principal component axes. One quarter of these initially significant SNPs retain their effects in regularized models that take into account population structure and linkage disequilibrium. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. We exploit this fact to show that the effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. A subset of SNP effects were replicable in an unrelated panel of inbred lines. Association studies that take a phenomic approach, considering many traits simultaneously, are an important complement to the power of genomics.
Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition ...including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to ...SARS-CoV-2 and the severity of COVID-19
, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases
. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ...ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10
; 43 loci at p < 6 × 10
). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10
), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open ...resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal ( http://genetics.opentargets.org ), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.
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