scholarly journals Characterizing the structural covariance network in AD-susceptible single nucleotide polymorphisms and the correlations with cognitive outcomes

2020 ◽  
Author(s):  
Hsin-I Chang ◽  
Yu-Tzu Chang ◽  
Chi-Wei Huang ◽  
Kuo-Lun Huang ◽  
Jung-Lung Hsu ◽  
...  
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Association Studies ◽  
Clinical Manifestations ◽  
Brain Network ◽  
Cognitive Outcomes ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Structural Covariance ◽  
Genotype Group

Abstract BackgroundThe clinical manifestations of Alzheimer disease (AD) are related to brain network degeneration, while genetic differences may mediate network change patterns. A number of AD-susceptible loci have been reported using genome-wide association studies, however, how they modulate intracerebral volume and relationships to cognitive outcomes remains to be established. We hypothesized that different genotype groups may modulate large-scale brain networks independently or interact with apolipoprotein E4 (ApoE4) status to determine neurobehavior test scores.MethodsGray matter structural covariance networks were constructed in 324 patients with AD using T1 magnetic resonance imaging with independent component analysis (ICA). We assessed 15 genetic loci (rs9349407, rs3865444, rs670139, rs744373, rs3851179, rs11136000, rs3764650, rs610932, rs6887649, rs7849530, rs4866650, rs3765728, rs34011, rs6656401, rs597668) using the additive, recessive and dominant model on clinical outcomes. Statistical analysis was performed to explore the independent role of each locus, interactions with ApoE4 status, and relationships to brain ICA network integrity score. We used Cognitive Abilities Screening Instrument (CASI) total score or short-term memory (STM) subscore as the major outcome factors, adjusted for covariates of education, disease duration and age.ResultsClinically, the CD2AP G allele showed a protective role in CASI-total and CASI-STM scores independently or via interactions with non-ApoE4 status, while the CR1 A genotype group was associated with lower STM independently of ApoE4 status. Three loci showed synergic interactions with ApoE4: BIN 1 T allele and MS4A6A G allele with non-ApoE4 status, and FTMT G allele with ApoE4 status. The network integrity scores revealed 9 significant ICA networks (anterior and posterior hippocampus, right temporal, right or left thalamus, inferior cerebellum, medial cerebellum, default mode network, frontal attention network) that correlated with cognitive scores, in which only the ApoE4 and MS4A6A genotype group was independently related to the hippocampus network. Genetic loci of MS4A6A, BIN1, CD2AP, CD33, CLU, BIN1, P73, EXOC3L2, CR1 and MS4AE4 exerted network influence independently or via interactions with ApoE4 status.ConclusionsThis study suggests that AD-susceptible loci may exert clinical significance independently, through interactions with ApoE4 status or by modulating ICA networks to determine cognitive outcomes.

scholarly journals Structural Covariance Network as an Endophenotype in Alzheimer’s Disease-Susceptible Single-Nucleotide Polymorphisms and the Correlations With Cognitive Outcomes

2021 ◽  
Vol 13 ◽  
Author(s):  
Hsin-I Chang ◽  
Yu-Tzu Chang ◽  
Chi-Wei Huang ◽  
Kuo-Lun Huang ◽  
Jung-Lung Hsu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gray Matter ◽  
Short Term Memory ◽  
Brain Network ◽  
Cognitive Outcomes ◽  
Nucleotide Polymorphisms ◽  
Genetic Loci ◽  
Single Nucleotide ◽  
Structural Covariance

The cognitive manifestations of Alzheimer’s disease (AD) are related to brain network degeneration, and genetic differences may mediate network degeneration. Several AD-susceptible loci have been reported to involve amyloid or tau cascades; however, their relationships with gray matter (GM) volume and cognitive outcomes have yet to be established. We hypothesized that single-nucleotide polymorphism genotype groups may interact with apolipoprotein E4 (ApoE4) status or independently exert an effect on cognitive outcomes. We also hypothesized that GM structural covariance networks (SCNs) may serve as an endophenotype of the genetic effect, which, in turn, may be related to neurobehavior test scores. Gray matter SCNs were constructed in 324 patients with AD using T1 magnetic resonance imaging with independent component analysis (ICA). We assessed the effects of 15 genetic loci (rs9349407, rs3865444, rs670139, rs744373, rs3851179, rs11136000, rs3764650, rs610932, rs6887649, rs7849530, rs4866650, rs3765728, rs34011, rs6656401, and rs597668) using additive, recessive, and dominant models on cognitive outcomes. Statistical analysis was performed to explore the independent role of each locus, interactions with ApoE4 status, and relationships to GM ICA network intensity score. For outcome measures, we used the Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI) total score, and short-term memory (STM) subscores, adjusted for the covariates of education, disease duration, and age. Clinically, the CD2AP G allele showed a protective role in MMSE, CASI total, and CASI-STM scores independently or via interactions with non-ApoE4 status, while the CR1 A genotype group was associated with lower STM subscores independent of ApoE4 status. Three loci showed synergic interactions with ApoE4: BIN 1, MS4A6A, and FTMT. Of the 15 meaningful ICA components, 5 SCNs (anterior and posterior hippocampus, right temporal, left thalamus, default mode network) showed relationships with general cognitive performance, in which only the ApoE4 and MS4A6A genotype groups were independently related to the hippocampus network. The genetic loci MS4A6A, BIN1, CLU, CR1, BIN1, PICALM, and FGF1 influenced the networks independently or in synergy. This study suggests that AD-susceptible loci may each exert clinical significance independently through interactions with ApoE4 status or through SCNs as an endophenotype and that this effect is associated with the cognitive outcomes.

Large-scale genome-wide association studies in east Asians identify new genetic loci influencing metabolic traits

2011 ◽  
Vol 43 (10) ◽  
pp. 990-995 ◽  
Author(s):  
Young Jin Kim ◽  
◽  
Min Jin Go ◽  
Cheng Hu ◽  
Chang Bum Hong ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
East Asians ◽  
Metabolic Traits ◽  
Genome Wide

scholarly journals Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Josine L. Min ◽  
Rebecca C. Richmond ◽  
Ake T. Lu ◽  
Maria K. Sobczyk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Biological Aging ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Biomarkers Of Aging ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

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 The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

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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