scholarly journals Polygenic Score of Intelligence is More Predictive of Crystallized than Fluid Performance Among Children

2019 ◽  
Author(s):  
Robert J. Loughnan ◽  
Clare E. Palmer ◽  
Wesley K. Thompson ◽  
Anders M. Dale ◽  
Terry L. Jernigan ◽  
...  
Keyword(s):  
Surface Area ◽  
Association Studies ◽  
Brain Morphology ◽  
Genome Wide Association Studies ◽  
Polygenic Score ◽  
Genome Wide ◽  
Mri Scans ◽  
Intelligence Scores ◽  
Stratified Analysis ◽  
Variance Explained

AbstractScores on intelligence tests have been reported to correlate significantly with educational, occupational and health outcomes. Twin and genome wide association studies in adults have revealed that intelligence scores are moderately heritable. We aimed to better understand the relationship between genetic variation and intelligence in the context of the developing brain. Specifically, we questioned if a genetic predictor of intelligence derived from a large GWAS dataset a) loaded on specific factors of cognition (i.e. fluid vs. crystallized) and b) were related to differences in cortical brain morphology measured using MRI scans. To do this we calculated an intelligence polygenic score (IPS) for the Adolescent Brain Cognitive Development (ABCD) baseline data, which consists of 11,875 nine- and ten-year old children across the US. We found that the IPS was a highly significant predictor of estimates of both fluid (t=8.7, p=3.0×10−18, 0.8% variance explained) and crystallized (t=17.1, p=2.0×10−64, 3.1% variance explained) cognition. Critically we found greater predictive power for crystallized than fluid (z=5.1, p=3.1×10−7), this result replicated in ancestry stratified analysis: for Europeans (z=4.7, 3.2×10−8) and non-Europeans (z=2.6, p=9.4×10−3). This indicates a stronger loading of IPS on crystallized cognition. IPS was significantly related to total cortical surface area (t=5.5, p=2.5×10−8, 0.4% variance explained), but not mean thickness (t=2.0, p=0.045) – after Bonferroni correction. These results replicated in the European subsample (area: t=5.4, p=6.3×10−8, mean thickness: t=2.3, p=0.021), but not in the non-European subsample (area: t=2.4, p=0.016, mean thickness: t=-0.41, p=0.68). Vertex-wise analyses within the European group showed that the surface area association is largely global across the cortex. The stronger association of IPS with crystallized compared to fluid measures is consistent with recent results that more culturally dependent measures of cognition are more heritable. These findings in children provide new evidence relevant to the developmental origins of previously observed cognitive loadings and brain morphology patterns associated with polygenic predictors of intelligence.

scholarly journals Understanding the genetic determinants of the brain with MOSTest

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis van der Meer ◽  
Oleksandr Frei ◽  
Tobias Kaufmann ◽  
Alexey A. Shadrin ◽  
Anna Devor ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Computational Design ◽  
Brain Regions ◽  
Brain Morphology ◽  
Genome Wide Association Studies ◽  
Small Individual ◽  
Significance Threshold ◽  
Regional Brain ◽  
Genome Wide

Abstract Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10−8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.

scholarly journals Making the MOSTest of imaging genetics

2019 ◽  
Author(s):  
Dennis van der Meer ◽  
Oleksandr Frei ◽  
Tobias Kaufmann ◽  
Alexey A. Shadrin ◽  
Anna Devor ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Computational Design ◽  
Imaging Genetics ◽  
Brain Morphology ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Small Individual ◽  
Regional Brain ◽  
Genome Wide

ABSTRACTRegional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects, which has proven challenging for genome-wide association studies to date, despite its high heritability1,2. Given the distributed nature of the genetic signal across brain regions, joint analysis of regional morphology measures in a multivariate statistical framework provides a way to enhance discovery of genetic variants with current sample sizes. While several multivariate approaches to GWAS have been put forward over the past years3–5, none are optimally suited for complex, large-scale data. Here, we applied the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable permutation-based inference, to 171 subcortical and cortical brain morphology measures from 26,502 participants of the UK Biobank (mean age 55.5 years, 52.0% female). At the conventional genome-wide significance threshold of α=5×10−8, MOSTest identifies 347 genetic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. As such, MOSTest, which we have made publicly available, enhances our understanding of the genetic determinants of regional brain morphology.

scholarly journals Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kira J. Stanzick ◽  
Yong Li ◽  
Pascal Schlosser ◽  
Mathias Gorski ◽  
Matthias Wuttke ◽  
...  
Keyword(s):  
Kidney Function ◽  
Fine Mapping ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Genome Wide ◽  
Credible Sets ◽  
Variance Explained

AbstractGenes underneath signals from genome-wide association studies (GWAS) for kidney function are promising targets for functional studies, but prioritizing variants and genes is challenging. By GWAS meta-analysis for creatinine-based estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics Consortium and UK Biobank (n = 1,201,909), we expand the number of eGFRcrea loci (424 loci, 201 novel; 9.8% eGFRcrea variance explained by 634 independent signal variants). Our increased sample size in fine-mapping (n = 1,004,040, European) more than doubles the number of signals with resolved fine-mapping (99% credible sets down to 1 variant for 44 signals, ≤5 variants for 138 signals). Cystatin-based eGFR and/or blood urea nitrogen association support 348 loci (n = 460,826 and 852,678, respectively). Our customizable tool for Gene PrioritiSation reveals 23 compelling genes including mechanistic insights and enables navigation through genes and variants likely relevant for kidney function in human to help select targets for experimental follow-up.

scholarly journals Multivariate genome-wide association study identifies 1735 unique genetic loci associated with cortical morphology

2020 ◽  
Author(s):  
Alexey A. Shadrin ◽  
Tobias Kaufmann ◽  
Dennis van der Meer ◽  
Clare E. Palmer ◽  
Carolina Makowski ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Fold Increase ◽  
Structural Mri ◽  
Genome Wide Association ◽  
Brain Morphology ◽  
Effective Sample Size ◽  
Brain Anatomy ◽  
Genome Wide Association Studies ◽  
Genome Wide

ABSTRACTBrain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) measures from N=35,657 participants in the UK Biobank. We identified 1598 loci for cortical surface area and 1054 for cortical thickness, reflecting an approximate 10-fold increase compared to the most recent report using commonly applied GWAS methods. Our power analysis indicates that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional GWAS approaches. Our gene-based analyses implicate 10% of all protein-coding genes and point towards pathways involved in neurogenesis and cell differentiation, supporting that we are capturing valid biological mechanisms underlying brain anatomy.

Association between ADAMTS7 TagSNPs and the risk of myocardialinfarction

2019 ◽  
Vol 95 (1127) ◽  
pp. 487-492
Author(s):  
Li-li Liang ◽  
Yu-lan Zhou ◽  
Jie Cheng ◽  
Yu-tong Xiao ◽  
Zi-bin Tang ◽  
...  
Keyword(s):  
Association Studies ◽  
Multivariate Logistic Regression Analysis ◽  
Underlying Mechanism ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
General Validity ◽  
Genome Wide ◽  
Increased Risk ◽  
Stratified Analysis

Purpose of the studyGenome-wide association studies have revealed an association of ADAMTS7 polymorphisms with the risk of cardiovascular diseases. Nonetheless, the role of ADAMTS7 polymorphisms on myocardial infarction (MI) risk remains poorly understood. Here, we aim to evaluate the effect of ADAMTS7 tag single nucleotide polymorphisms (SNPs) on individual susceptibility to MI.Study designGenotyping of the four tagSNPs (rs1994016, rs3825807, rs4380028 and rs7173743) was performed in 232 MI cases and 661 control subjects using PCR-ligase detection reaction (LDR) method. The association of these four tagSNPs with MI risk was performed with SPSS software.ResultsMultivariate logistic regression analysis showed that ADAMTS7 tagSNP rs3825807 exhibited a significant effect on MI risk. Compared with the TT homozygotes, the CT genotype (OR1.93, 95% CI1.30to 2.85, Pc=0.004) and the combined CC/CT genotypes (OR1.70, 95% CI1.16 to 2.50, Pc=0.028) were statistically significantly associated with the increased risk for MI. Further stratified analysis revealed a more significant association with MI risk among older subjects, hypertensives, non-diabetics and patients with hyperlipidaemia. Consistently, the haplotype rs1994016T–rs3825807C containing rs3825807 C allele exhibited increased MI risk (OR1.52, 95% CI1.10 to 2.10, p=0.010). However, we did not detect any association of the other three tagSNPs with MI risk.ConclusionsOur finding suggest that ADAMTS7 tagSNP rs3825807 contributes to MI susceptibility in the Chinese Han population. Further studies are necessary to confirm the general validity of our findings and to clarify the underlying mechanism for this association.

scholarly journals LINC00673 rs11655237 C>T confers neuroblastoma susceptibility in Chinese population

2018 ◽  
Vol 38 (1) ◽  
Author(s):  
Zhuorong Zhang ◽  
Yitian Chang ◽  
Wei Jia ◽  
Jiao Zhang ◽  
Ruizhong Zhang ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Association Studies ◽  
Stage Iv ◽  
Staging System ◽  
Large Sample Size ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage Iv Disease ◽  
Poor Outcomes ◽  
Stratified Analysis

Neuroblastoma, which accounts for approximately 10% of all pediatric cancer-related deaths, has become a therapeutic challenge and global burden attributed to poor outcomes and mortality rates of its high-risk form. Previous genome-wide association studies (GWASs) identified the LINC00673 rs11655237 C>T polymorphism to be associated with the susceptibility of several malignant tumors. However, the association between this polymorphism and neuroblastoma susceptibility is not clear. We genotyped LINC00673 rs11655237 C>T in 393 neuroblastoma patients in comparison with 812 age-, gender-, and ethnicity-matched healthy controls. We found a significant association between the LINC00673 rs11655237 C>T polymorphism and neuroblastoma risk (TT compared with CC: adjusted odds ratio (OR) =1.80, 95% confidence interval (CI) =1.06–3.06, P=0.029; TT/CT compared with CC: adjusted OR =1.31, 95% CI =1.02–1.67, P=0.033; and T compared with C: adjusted OR =1.29, 95% CI =1.06–1.58, P=0.013). Furthermore, stratified analysis indicated that the rs11655237 T allele carriers were associated with increased neuroblastoma risk for patients with tumor originating from the adrenal gland (adjusted OR =1.51, 95% CI =1.06–2.14, P=0.021) and International Neuroblastoma Staging System (INSS) stage IV disease (adjusted OR =1.60, 95% CI =1.12–2.30, P=0.011). In conclusion, we verified that the LINC00673 rs11655237 C>T polymorphism might be associated with neuroblastoma susceptibility. Prospective studies with a large sample size and different ethnicities are needed to validate our findings.

scholarly journals The MIR137 VNTR rs58335419 Is Associated With Cognitive Impairment in Schizophrenia and Altered Cortical Morphology

2020 ◽  
Author(s):  
Ebrahim Mahmoudi ◽  
Joshua R Atkins ◽  
Yann Quidé ◽  
William R Reay ◽  
Heath M Cairns ◽  
...  
Keyword(s):  
Association Studies ◽  
Variable Number Tandem Repeat ◽  
Variable Number ◽  
Brain Morphology ◽  
Whole Genome Sequencing Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Wide ◽  
Cortical Morphology

Abstract Genome-wide association studies (GWAS) of schizophrenia have strongly implicated a risk locus in close proximity to the gene for miR-137. While there are candidate single-nucleotide polymorphisms (SNPs) with functional implications for the microRNA’s expression encompassed by the common haplotype tagged by rs1625579, there are likely to be others, such as the variable number tandem repeat (VNTR) variant rs58335419, that have no proxy on the SNP genotyping platforms used in GWAS to date. Using whole-genome sequencing data from schizophrenia patients (n = 299) and healthy controls (n = 131), we observed that the MIR137 4-repeats VNTR (VNTR4) variant was enriched in a cognitive deficit subtype of schizophrenia and associated with altered brain morphology, including thicker left inferior temporal gyrus and deeper right postcentral sulcus. These findings suggest that the MIR137 VNTR4 may impact neuroanatomical development that may, in turn, influence the expression of more severe cognitive symptoms in patients with schizophrenia.

scholarly journals PS-202-Genome-wide association studies of abdominal MRI scans identifies loci associated with liver fat and liver iron in the UK Biobank

2019 ◽  
Vol 70 (1) ◽  
pp. e135
Author(s):  
Henry Wilman ◽  
Constantinos Parisinos ◽  
Matt Kelly ◽  
Stefan Neubauer ◽  
Louise Thomas ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Liver Fat ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Liver Iron ◽  
Genome Wide ◽  
Abdominal Mri ◽  
Mri Scans ◽  
The Uk

scholarly journals VarExp: Estimating variance explained by Genome-Wide GxE summary statistics

2017 ◽  
Author(s):  
Vincent Laville ◽  
Amy R. Bentley ◽  
Florian Privé ◽  
Xiafoeng Zhu ◽  
Jim Gauderman ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Genetics ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Ethical Challenges ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Variance Explained

AbstractMany genomic analyses, such as genome-wide association studies (GWAS) or genome-wide screening for Gene-Environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. In practice, this is commonly estimated using individual genotype data. However, using individual-level data faces practical and ethical challenges when the GWAS results are derived in large consortia through meta-analysis of results from multiple cohorts. In this work, we present a R package, “VarExp”, that allows for the estimation of the percentage of phenotypic variance explained by variants of interest using summary statistics only. Our package allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects, and main genetic and interaction effects jointly. Its implementation integrates all recent methodological developments on the topic and does not need external data to be uploaded by users.The R source code, tutorial and associated example are available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git.

scholarly journals Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals

2020 ◽  
Author(s):  
Kira J Stanzick ◽  
Yong Li ◽  
Mathias Gorski ◽  
Matthias Wuttke ◽  
Cristian Pattaro ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Function ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Estimated Gfr ◽  
Genome Wide ◽  
Credible Sets ◽  
Gene Prioritisation ◽  
Variance Explained

ABSTRACTChronic kidney disease (CKD) has a complex genetic underpinning. Genome-wide association studies (GWAS) of CKD-defining glomerular filtration rate (GFR) have identified hundreds of loci, but prioritization of variants and genes is challenging. To expand and refine GWAS discovery, we meta-analyzed GWAS data for creatinine-based estimated GFR (eGFRcrea) from the Chronic Kidney Disease Genetics Consortium (CKDGen, n=765,348, trans-ethnic) and UK Biobank (UKB, n=436,581, Europeans). The results (i) extend the number of eGFRcrea loci (424 loci; 201 novel; 8.9% eGFRcrea variance explained by 634 independent signals); (ii) improve fine-mapping resolution (138 99% credible sets with ≤5 variants, 44 single-variant sets); (iii) ascertain likely kidney function relevance for 343 loci (consistent association with alternative biomarkers); and (iv) highlight 34 genes with strong evidence by a systematic Gene PrioritiSation (GPS). We provide a sortable, searchable and customizable GPS tool to navigate through the in silico functional evidence and select relevant targets for functional investigations.

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