scholarly journals Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations

2018 ◽  
Author(s):  
Steven Gazal ◽  
Po-Ru Loh ◽  
Hilary K. Finucane ◽  
Andrea Ganna ◽  
Armin Schoech ◽  
...  
Keyword(s):  
Negative Selection ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Common Variant ◽  
Cell Type ◽  
Functional Annotations ◽  
Common Coding ◽  
Cell Type Specific ◽  
Coding Variants

AbstractCommon variant heritability is known to be concentrated in variants within cell-type-specific non-coding functional annotations, with a limited role for common coding variants. However, little is known about the functional distribution of low-frequency variant heritability. Here, we partitioned the heritability of both low-frequency (0.5% ≤ MAF < 5%) and common (MAF ≥ 5%) variants in 40 UK Biobank traits (average N = 363K) across a broad set of coding and non-coding functional annotations, employing an extension of stratified LD score regression to low-frequency variants that produces robust results in simulations. We determined that non-synonymous coding variants explain 17±1% of low-frequency variant heritability versus only 2.1±0.2% of common variant heritability , and that regions conserved in primates explain nearly half of (43±2%). Other annotations previously linked to negative selection, including non-synonymous variants with high PolyPhen-2 scores, non-synonymous variants in genes under strong selection, and low-LD variants, were also significantly more enriched for as compared to . Cell-type-specific non-coding annotations that were significantly enriched for of corresponding traits tended to be similarly enriched for for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain DPFC explain 57±12% of vs. 12±2% of for neuroticism, implicating the action of negative selection on low-frequency variants affecting gene regulation in the brain. Forward simulations confirmed that the ratio of low-frequency variant enrichment vs. common variant enrichment primarily depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict the effect size variance of causal rare variants (MAF < 0.5%) in the annotation, informing their prioritization in whole-genome sequencing studies. Our results provide a deeper understanding of low-frequency variant functional architectures and guidelines for the design of association studies targeting functional classes of low-frequency and rare variants.

scholarly journals Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

2021 ◽  
Author(s):  
Abhishek Nag ◽  
Lawrence Middleton ◽  
Ryan S Dhindsa ◽  
Dimitrios Vitsios ◽  
Eleanor M Wigmore ◽  
...  
Keyword(s):  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Protein Coding ◽  
The Uk ◽  
Metabolic Biomarkers ◽  
Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

scholarly journals Advantages of genotype imputation with ethnically matched reference panel for rare variant association analyses

2019 ◽  
Author(s):  
Mart Kals ◽  
Tiit Nikopensius ◽  
Kristi Läll ◽  
Kalle Pärn ◽  
Timo Tõnis Sikka ◽  
...  
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Genotype Imputation ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Variant Analysis ◽  
Coding Variants

AbstractGenotype imputation has become a standard procedure prior genome-wide association studies (GWASs). For common and low-frequency variants, genotype imputation can be performed sufficiently accurately with publicly available and ethnically heterogeneous reference datasets like 1000 Genomes Project (1000G) and Haplotype Reference Consortium panels. However, the imputation of rare variants has been shown to be significantly more accurate when ethnically matched reference panel is used. Even more, greater genetic similarity between reference panel and target samples facilitates the detection of rare (or even population-specific) causal variants. Notwithstanding, the genome-wide downstream consequences and differences of using ethnically mixed and matched reference panels have not been yet comprehensively explored.We determined and quantified these differences by performing several comparative evaluations of the discovery-driven analysis scenarios. A variant-wise GWAS was performed on seven complex diseases and body mass index by using genome-wide genotype data of ∼37,000 Estonians imputed with ethnically mixed 1000G and ethnically matched imputation reference panels. Although several previously reported common (minor allele frequency; MAF > 5%) variant associations were replicated in both resulting imputed datasets, no major differences were observed among the genome-wide significant findings or in the fine-mapping effort. In the analysis of rare (MAF < 1%) coding variants, 46 significantly associated genes were identified in the ethnically matched imputed data as compared to four genes in the 1000G panel based imputed data. All resulting genes were consequently studied in the UK Biobank data.These associations provide a solid example of how rare variants can be efficiently analysed to discover novel, potentially functional genetic variants in relevant phenotypes. Furthermore, our work serves as proof of a cost-efficient study design, demonstrating that the usage of ethnically matched imputation reference panels can enable substantially improved imputation of rare variants, facilitating novel high-confidence findings in rare variant GWAS scans.Author summaryOver the last decade, genome-wide association studies (GWASs) have been widely used for detecting genetic biomarkers in a wide range of traits. Typically, GWASs are carried out using chip-based genotyping data, which are then combined with a more densely genotyped reference panel to infer untyped genetic variants in chip-typed individuals. The latter method is called genotype imputation and its accuracy depends on multiple factors. Publicly available and ethnically heterogeneous imputation reference panels (IRPs) such as 1000 Genomes Project (1000G) are sufficiently accurate for imputation of common and low-frequency variants, but custom ethnically matched IRPs outperform these in case of rare variants. In this work, we systematically compare downstream association analysis effects on eight complex traits in ∼37,000 Estonians imputed with ethnically mixed and ethnically matched IRPs. We do not observe major differences in the single variant analysis, where both imputed datasets replicate previously reported significant loci. But in the gene-based analysis of rare protein-coding variants we show that ethnically matched panel clearly outperforms 1000G panel based imputation, providing 10-fold increase in significant gene-trait associations. Our study demonstrates empirically that imputed data based on ethnically matched panel is very promising for rare variant analysis – it captures more population-specific variants and makes it possible to efficiently identify novel findings.

scholarly journals Partitioning heritability by functional category using GWAS summary statistics

2015 ◽  
Author(s):  
Hilary Kiyo Finucane ◽  
Brendan Bulik-Sullivan ◽  
Alexander Gusev ◽  
Gosia Trynka ◽  
Yakir Reshef ◽  
...  
Keyword(s):  
Association Studies ◽  
Smoking Behavior ◽  
Complex Diseases ◽  
New Method ◽  
Age At Menarche ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Cell Type ◽  
Genome Wide ◽  
Cell Type Specific

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

scholarly journals Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

2021 ◽  
Author(s):  
Elior Rahmani ◽  
Brandon Jew ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Best Practices ◽  
Regression Model ◽  
Association Studies ◽  
Composition Analysis ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Interaction Terms ◽  
Mathematical Explanations ◽  
Cell Type Specific

AbstractWe benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.

scholarly journals Transcribed enhancers in the human brain identify novel disease risk mechanisms

2021 ◽  
Author(s):  
Pengfei Dong ◽  
Gabriel E. Hoffman ◽  
Pasha Apontes ◽  
Jaroslav Bendl ◽  
Samir Rahman ◽  
...  
Keyword(s):  
Human Brain ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Cell Type ◽  
Level Variation ◽  
Functional Interpretation ◽  
Neuropsychiatric Diseases ◽  
Order Of Magnitude ◽  
Cell Type Specific

Enhancer RNAs (eRNAs) constitute an important tissue- and cell-type-specific layer of the regulome. Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding the population-level variation of eRNAs in the human brain. We jointly analyzed cell type-specific transcriptome and regulome data to identify 30,795 neuronal and 23,265 non-neuronal eRNAs, expanding the catalog of known human brain eRNAs by an order of magnitude. Examination of the population-level variation of the transcriptome and regulome in 1,382 brain samples identified reproducible changes affecting cis- and trans-co-regulation of eRNA-gene modules in schizophrenia. We show that 13% of schizophrenia heritability is jointly mediated in cis by brain gene and eRNA expression. Inclusion of eRNAs in transcriptome-wide association studies facilitated fine-mapping and functional interpretation of disease loci. Overall, our study characterizes the eRNA-gene regulome and genetic mechanisms in the human cortex in both healthy and disease states.

scholarly journals Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans

2015 ◽  
Vol 21 (5) ◽  
pp. 601-607 ◽  
Author(s):  
E Olfson ◽  
N L Saccone ◽  
E O Johnson ◽  
L-S Chen ◽  
R Culverhouse ◽  
...  
Keyword(s):  
African Americans ◽  
Nicotine Dependence ◽  
Low Frequency ◽  
Common Coding ◽  
Coding Variants

scholarly journals Common and Rare Variants in Genes Associated with von Willebrand Factor Level Variation: No Accumulation of Rare Variants in Swedish von Willebrand Disease Patients

TH Open ◽  
2020 ◽  
Vol 04 (04) ◽  
pp. e322-e331
Author(s):  
Eric Manderstedt ◽  
Christina Lind-Halldén ◽  
Stefan Lethagen ◽  
Christer Halldén
Keyword(s):  
Von Willebrand Factor ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Von Willebrand Disease ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Common ◽  
Von Willebrand ◽  
Willebrand Factor

AbstractGenome-wide association studies (GWASs) have identified genes that affect plasma von Willebrand factor (VWF) levels. ABO showed a strong effect, whereas smaller effects were seen for VWF, STXBP5, STAB2, SCARA5, STX2, TC2N, and CLEC4M. This study screened comprehensively for both common and rare variants in these eight genes by resequencing their coding sequences in 104 Swedish von Willebrand disease (VWD) patients. The common variants previously associated with the VWF level were all accumulated in the VWD patients compared to three control populations. The strongest effect was detected for blood group O coded for by the ABO gene (71 vs. 38% of genotypes). The other seven VWF level associated alleles were enriched in the VWD population compared to control populations, but the differences were small and not significant. The sequencing detected a total of 146 variants in the eight genes. Excluding 70 variants in VWF, 76 variants remained. Of the 76 variants, 54 had allele frequencies > 0.5% and have therefore been investigated for their association with the VWF level in previous GWAS. The remaining 22 variants with frequencies < 0.5% are less likely to have been evaluated previously. PolyPhen2 classified 3 out of the 22 variants as probably or possibly damaging (two in STAB2 and one in STX2); the others were either synonymous or benign. No accumulation of low frequency (0.05–0.5%) or rare variants (<0.05%) in the VWD population compared to the gnomAD (Genome Aggregation Database) population was detected. Thus, rare variants in these genes do not contribute to the low VWF levels observed in VWD patients.

SMARCA2 common variant association and rare variant excess in Schizophrenia patients from an Algerian Trio Cohort

2011 ◽  
Vol 26 (S2) ◽  
pp. 1346-1346
Author(s):  
D. Benmessaoud ◽  
A.-M. Lepagnol-Bestel ◽  
M. Delepine ◽  
J. Hager ◽  
J.-M. Moalic ◽  
...  
Keyword(s):  
Rare Variants ◽  
Association Studies ◽  
Common Variant ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Fisher Test ◽  
Coding Regions ◽  
Genome Wide ◽  
Whole Exome ◽  
Positive Evolution

Genome wide association studies (GWAS) of Schizophrenia (SZ) patients have identified common variants in ten genes including SMARCA2 (Koga et al., HMG, 2009). We found that the SZ-GWAS genes are part of an interacting network centered on SMARCA2 (Loe-Mie et al., HMG, 2010). Furthermore, SMARCA2 was found disrupted in SZ (Walsh et al., Science, 2008). SMARCA2 encodes the ATPase (BRM) of the SWI/SNF chromatin remodeling complex that is at the interface of genome and environmental adaptation.Taking advantage of an Algerian trio cohort of one hundred SZ patients (Benmessaoud et al., BMC Psychiatry, 2008), we replicated the association of SNP rs2296212 localized in exon 33, already shown associated in Koga study and resulting in D1546E amino acid change in the SMARCA2 protein. We studied SMARCA2 codons and found that exon 33 displays a signature of positive evolution in the primate lineage.Our working hypothesis is that the coding regions displaying positive selection are target of novel rare variants. To address this question, we sequenced two exons displaying positive evolution and one exon without evidence of positive evolution.We found (i) that rare variants are significantly in excess in SZ-patients compared to their parents (p = 0.038, Fisher test) and (ii) a higher proportion of rare variants in the primate-accelerated exons compared with the non-evolutionary exon in SZ-patients (p = 0.032, Fisher test).SMARCA2 exon sequencing and whole exome sequencing from patients harboring SNP rs2296212 common variant are under progress. Altogether, these results are expected to give new insights into the genetic architecture of SZ.

scholarly journals Analysis of putative cis-regulatory elements regulating blood pressure variation

2020 ◽  
Vol 29 (11) ◽  
pp. 1922-1932
Author(s):  
Priyanka Nandakumar ◽  
Dongwon Lee ◽  
Thomas J Hoffmann ◽  
Georg B Ehret ◽  
Dan Arking ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Association Studies ◽  
Specific Effect ◽  
Cell Types ◽  
Regulatory Elements ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Functional Scores ◽  
Cell Type Specific

Abstract Hundreds of loci have been associated with blood pressure (BP) traits from many genome-wide association studies. We identified an enrichment of these loci in aorta and tibial artery expression quantitative trait loci in our previous work in ~100 000 Genetic Epidemiology Research on Aging study participants. In the present study, we sought to fine-map known loci and identify novel genes by determining putative regulatory regions for these and other tissues relevant to BP. We constructed maps of putative cis-regulatory elements (CREs) using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Variants within these regions may be evaluated quantitatively for their tissue- or cell-type-specific regulatory impact using deltaSVM functional scores, as described in our previous work. We aggregate variants within these putative CREs within 50 Kb of the start or end of ‘expressed’ genes in these tissues or cell types using public expression data and use deltaSVM scores as weights in the group-wise sequence kernel association test to identify candidates. We test for association with both BP traits and expression within these tissues or cell types of interest and identify the candidates MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B and PPCDC. Additionally, we examined two known QT interval genes, SCN5A and NOS1AP, in the Atherosclerosis Risk in Communities Study, as a positive control, and observed the expected heart-specific effect. Thus, our method identifies variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.

Sign in / Sign up

Export Citation Format

Share Document