scholarly journals Genetic control of the human brain proteome

2019 ◽  
Author(s):  
Chloe Robins ◽  
Aliza P. Wingo ◽  
Wen Fan ◽  
Duc M. Duong ◽  
Jacob Meigs ◽  
...  
Keyword(s):  
Human Brain ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Large Scale ◽  
Meta Analysis ◽  
Protein Abundance ◽  
Single Nucleotide Variants ◽  
Healthy Human ◽  
Normal Individuals ◽  
Trait Locus

AbstractAlteration of protein abundance and conformation are widely believed to be the hallmark of neurodegenerative diseases. Yet relatively little is known about the genetic variation that controls protein abundance in the healthy human brain. The genetic control of protein abundance is generally thought to parallel that of RNA expression, but there is little direct evidence to support this view. Here, we performed a large-scale protein quantitative trait locus (pQTL) analysis using single nucleotide variants (SNVs) from whole-genome sequencing and tandem mass spectrometry-based proteomic quantification of 12,691 unique proteins (7,901 after quality control) from the dorsolateral prefrontal cortex (dPFC) in 144 cognitively normal individuals. We identified 28,211 pQTLs that were significantly associated with the abundance of 864 proteins. These pQTLs were compared to dPFC expression quantitative trait loci (eQTL) in cognitive normal individuals (n=169; 81 had protein data) and a meta-analysis of dPFC eQTLs (n=1,433). We found that strong pQTLs are generally only weak eQTLs, and that the majority of strong eQTLs are not detectable pQTLs. These results suggest that the genetic control of mRNA and protein abundance may be substantially distinct and suggests inference concerning protein abundance made from mRNA in human brain should be treated with caution.

Convergent lines of evidence support NOTCH4 as a schizophrenia risk gene

2020 ◽  
pp. jmedgenet-2020-106830
Author(s):  
Yan Zhang ◽  
Shiwu Li ◽  
Xiaoyan Li ◽  
Yongfeng Yang ◽  
Wenqiang Li ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Meta Analysis ◽  
Eqtl Analysis ◽  
Risk Gene ◽  
And Migration ◽  
Eqtl Data ◽  
Lines Of Evidence ◽  
Brain Tissues

The association between NOTCH4 and schizophrenia has been repeatedly reported. However, the results from different genetic studies are inconsistent, and the role of NOTCH4 in schizophrenia pathogenesis remains unknown. Here, we provide convergent lines of evidence that support NOTCH4 as a schizophrenia risk gene. We first performed a meta-analysis and found that a genetic variant (rs2071287) in NOTCH4 was significantly associated with schizophrenia (a total of 125 848 subjects, p=8.31×10−17), with the same risk allele across all tested samples. Expression quantitative trait loci (eQTL) analysis showed that rs2071287 was significantly associated with NOTCH4 expression (p=1.08×10−14) in human brain tissues, suggesting that rs2071287 may confer schizophrenia risk through regulating NOTCH4 expression. Sherlock integrative analysis using a large-scale schizophrenia GWAS and eQTL data from human brain tissues further revealed that NOTCH4 was significantly associated with schizophrenia (p=4.03×10−7 in CMC dataset and p=3.06×10−6 in xQTL dataset), implying that genetic variants confer schizophrenia risk through modulating NOTCH4 expression. Consistently, we found that NOTCH4 was significantly downregulated in brains of schizophrenia patients compared with controls (p=2.53×10−3), further suggesting that dysregulation of NOTCH4 may have a role in schizophrenia. Finally, we showed that NOTCH4 regulates proliferation, self-renewal, differentiation and migration of neural stem cells, suggesting that NOTCH4 may confer schizophrenia risk through affecting neurodevelopment. Our study provides convergent lines of evidence that support the involvement of NOTCH4 in schizophrenia. In addition, our study also elucidates a possible mechanism for the role of NOTCH4 in schizophrenia pathogenesis.

T247. Oxytocin Gene Networks in the Human Brain: A Gene Expression and Large-Scale fMRI Meta-Analysis Study

2018 ◽  
Vol 83 (9) ◽  
pp. S225
Author(s):  
Daniel Quintana ◽  
Jaroslav Rokicki ◽  
Dennis van der Meer ◽  
Dag Alnæs ◽  
Tobias Kaufmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Gene Networks ◽  
Large Scale ◽  
Meta Analysis ◽  
Analysis Study

scholarly journals Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

2021 ◽  
Author(s):  
Alex N. Nguyen Ba ◽  
Katherine R. Lawrence ◽  
Artur Rego-Costa ◽  
Shreyas Gopalakrishnan ◽  
Daniel Temko ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Trait Locus

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

Abstract MP84: Trans-ethnic Meta-analysis of Exome Chip Data Reveals Novel Low-frequency Variants Contributing to Central Adiposity

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Kristin L Young ◽  
Anne Justice ◽  
Tugce Karaderi ◽  
Heather Highland ◽  
Mariaelisa Graff ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Large Scale ◽  
Blood Glucose Concentration ◽  
Meta Analysis ◽  
Low Frequency ◽  
Fat Distribution ◽  
Central Adiposity ◽  
Single Nucleotide Variants ◽  
Chip Data ◽  
Exome Chip

Central adiposity is a leading risk factor for cardiovascular disease, and genetic factors contribute both to fat distribution, measured as waist-to-hip ratio adjusted for BMI (WHRa), and to differences in central adiposity prevalence. To date, 49 loci have been associated with WHRa, based on studies of common [minor allele frequency (MAF) ≥5%] single nucleotide variants (SNVs), primarily in European descent populations. Our aim was to identify low frequency (LFV: MAF <5%) and rare (RV: MAF <1%) coding variants associated with WHRa using Exome-Chip data from 344,369 individuals of European (84%), South Asian (8%), African (5%), East Asian(2%), and Hispanic/Latino (1%) ancestry. We performed fixed effects meta-analyses of study-specific WHRa associations stratified by sex and ancestry and then combined across strata for both SNV and gene-based results. We used a strict definition of variants annotated as damaging by 5 algorithms to perform gene-based analyses using the sequence kernel association test (SKAT). Analyses included up to 284,499 SNVs (218,195 with MAF<5%), and 15,063 genes with at least one SNV that met our inclusion criteria. Five LFVs reached chip-wide significance (CWS: P<2.5E-7) in our all ancestry sex-combined analyses, including one novel non-synonymous LFV in RAPGEF3 [MAF=0.01, β (SE) = -0.09 (0.012), P=1.28E-13]. In addition, one novel RV reached CWS in men for UGGT2 [MAF<0.01, β (SE) = -0.142 (0.025), P=9.71E-9], and one RV reached CWS in women for ACVR1C [MAF<0.01, β (SE) = -0.09 (0.018), P=1.09E-7]. Gene-based analyses identified RAPGEF3 (P=1.18E-11) as significantly associated with WHRa in the all ancestry sex combined analyses after correction for multiple tests (P<2.5E-6), though conditional analysis revealed that this result is driven by the top SV identified in this region. RAPGEF3 also shows a significant association (p=4.68E-12) in all ancestry, sex combined gene-based analysis of BMI. RAPGEF3 is expressed in subcutaneous and visceral adipose tissue, and has been implicated in insulin regulation. RAPGEF3 plays a role in the GLP1 pathway, which controls insulin secretion in response to blood glucose concentration. Our results highlight the importance of large-scale genomic studies for identifying LFV and RV influencing central fat distribution. Understanding these genetic effects may provide insights into the progression of central adiposity and highlight potential population-specific variants that increase susceptibility.

scholarly journals A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

2020 ◽  
Vol 16 (6) ◽  
pp. e1007882 ◽  
Author(s):  
Hélène Ruffieux ◽  
Jérôme Carayol ◽  
Radu Popescu ◽  
Mary-Ellen Harper ◽  
Robert Dent ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait Locus Mapping ◽  
Quantitative Trait ◽  
Human Protein ◽  
Protein Abundance ◽  
Trait Locus ◽  
Locus Mapping

scholarly journals QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes

2019 ◽  
Vol 48 (D1) ◽  
pp. D983-D991 ◽  
Author(s):  
Zhanye Zheng ◽  
Dandan Huang ◽  
Jianhua Wang ◽  
Ke Zhao ◽  
Yao Zhou ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Large Scale ◽  
Cell Types ◽  
Tissue Cell ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Significant Qtls ◽  
Functional Variants ◽  
Trait Locus ◽  
Molecular Phenotypes

Abstract Recent advances in genome sequencing and functional genomic profiling have promoted many large-scale quantitative trait locus (QTL) studies, which connect genotypes with tissue/cell type-specific cellular functions from transcriptional to post-translational level. However, no comprehensive resource can perform QTL lookup across multiple molecular phenotypes and investigate the potential cascade effect of functional variants. We developed a versatile resource, named QTLbase, for interpreting the possible molecular functions of genetic variants, as well as their tissue/cell-type specificity. Overall, QTLbase has five key functions: (i) curating and compiling genome-wide QTL summary statistics for 13 human molecular traits from 233 independent studies; (ii) mapping QTL-relevant tissue/cell types to 78 unified terms according to a standard anatomogram; (iii) normalizing variant and trait information uniformly, yielding &gt;170 million significant QTLs; (iv) providing a rich web client that enables phenome- and tissue-wise visualization; and (v) integrating the most comprehensive genomic features and functional predictions to annotate the potential QTL mechanisms. QTLbase provides a one-stop shop for QTL retrieval and comparison across multiple tissues and multiple layers of molecular complexity, and will greatly help researchers interrogate the biological mechanism of causal variants and guide the direction of functional validation. QTLbase is freely available at http://mulinlab.org/qtlbase.

scholarly journals Quantifying functional impact of non-coding variants with multi-task Bayesian neural network

2019 ◽  
Vol 36 (5) ◽  
pp. 1397-1404
Author(s):  
Chencheng Xu ◽  
Qiao Liu ◽  
Jianyu Zhou ◽  
Minzhu Xie ◽  
Jianxing Feng ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Single Nucleotide Polymorphisms ◽  
Quantitative Trait ◽  
Large Scale ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Functional Impact ◽  
Coding Regions ◽  
Trait Locus

Abstract Motivation Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. Results We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. Availability and implementation Code can be downloaded from https://github.com/Zoesgithub/MtBNN. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document