scholarly journals Mitochondrial DNA SNPs associated with Schizophrenia exhibit Highly Variable Inter-allelic Haplogroup Affiliation and Nuclear Genogeographic Affinity: Bi-Genomic Linkage Disequilibrium raises Major Concerns for Link to Disease

2017 ◽  
Author(s):  
Christian M Hagen ◽  
Vanessa F Gonçalves ◽  
Paula L Hedley ◽  
Jonas Bybjerg-Grauholm ◽  
Marie Bækvad-Hansen ◽  
...  
Keyword(s):  
Mitochondrial Dna ◽  
Linkage Disequilibrium ◽  
Significant Role ◽  
Population Stratification ◽  
Association Studies ◽  
Human Diseases ◽  
Genomic Linkage ◽  
Disease Associations ◽  
Allelic Differences

AbstractMitochondria play a significant role in human diseases. However, disease associations with mitochondrial DNA (mtDNA) SNPs have proven difficult to replicate. A reanalysis of eight schizophrenia-associated mtDNA SNPs, in 23,743 normal Danes and 2,538 schizophrenia patients, revealed marked inter-allelic differences in haplogroup affiliation and nuclear ancestry, genogeophraphic affinity (GGA). This bi-genomic linkage disequilibrium (2GLD) could entail population stratification. Only two mitochondrial SNPs, m. 15043A and m. 15218G, were significantly associated with schizophrenia. However, these associations disappeared when corrected for haplogroup affiliation. The extensive 2GLD documented is a major concern when interpreting historic as well as designing future mtDNA association studies.

scholarly journals Expectation of the intercept from bivariate LD score regression in the presence of population stratification

2018 ◽  
Author(s):  
Loic Yengo ◽  
Jian Yang ◽  
Peter M. Visscher
Keyword(s):  
Linkage Disequilibrium ◽  
Population Stratification ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Popular Method ◽  
Extended Theory ◽  
Genome Wide ◽  
Estimate Heritability ◽  
Theoretical Results

Linkage disequilibrium (LD) score regression is an increasingly popular method used to quantify the level of confounding in genome-wide association studies (GWAS) or to estimate heritability and genetic correlation between traits. When applied to a pair of GWAS, the LD score regression (LDSC) methodology produces a statistic, referred to as the bivariate LDSC intercept, which deviation from 0 is classically interpreted as an indication of sample overlap between the two GWAS. Here we propose an extension of the theory underlying the bivariate LDSC methodology, which accounts for population stratification within and between GWAS. Our extended theory predicts an inflation of the bivariate LDSC intercept when sample sizes and heritability are large, even in the absence of sample overlap. We illustrate our theoretical results with simulations based on actual SNP genotypes and we propose a re-interpretation of previously published results in the light of our extended theory.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Mitochondrial DNA Methylation and Human Diseases

2021 ◽  
Vol 22 (9) ◽  
pp. 4594
Author(s):  
Andrea Stoccoro ◽  
Fabio Coppedè
Keyword(s):  
Dna Methylation ◽  
Mitochondrial Dna ◽  
Mitochondrial Genome ◽  
Nuclear Dna ◽  
Epigenetic Modification ◽  
Nuclear Genome ◽  
Epigenetic Modifications ◽  
Human Diseases ◽  
Loop Region ◽  
D Loop

Epigenetic modifications of the nuclear genome, including DNA methylation, histone modifications and non-coding RNA post-transcriptional regulation, are increasingly being involved in the pathogenesis of several human diseases. Recent evidence suggests that also epigenetic modifications of the mitochondrial genome could contribute to the etiology of human diseases. In particular, altered methylation and hydroxymethylation levels of mitochondrial DNA (mtDNA) have been found in animal models and in human tissues from patients affected by cancer, obesity, diabetes and cardiovascular and neurodegenerative diseases. Moreover, environmental factors, as well as nuclear DNA genetic variants, have been found to impair mtDNA methylation patterns. Some authors failed to find DNA methylation marks in the mitochondrial genome, suggesting that it is unlikely that this epigenetic modification plays any role in the control of the mitochondrial function. On the other hand, several other studies successfully identified the presence of mtDNA methylation, particularly in the mitochondrial displacement loop (D-loop) region, relating it to changes in both mtDNA gene transcription and mitochondrial replication. Overall, investigations performed until now suggest that methylation and hydroxymethylation marks are present in the mtDNA genome, albeit at lower levels compared to those detectable in nuclear DNA, potentially contributing to the mitochondria impairment underlying several human diseases.

scholarly journals Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

2021 ◽  
pp. 1-11
Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Effect Sizes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Tikhonov Regularisation

<b><i>Background:</i></b> Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. <b><i>Objectives:</i></b> We review methods that attempt to adjust the effect sizes (β<i>-</i>coefficients) of summary statistics, instead of simple LD pruning. <b><i>Methods:</i></b> We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. <b><i>Results:</i></b> Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. <b><i>Conclusions:</i></b> There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

scholarly journals A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Huaqing Zhao ◽  
Nandita Mitra ◽  
Peter A. Kanetsky ◽  
Katherine L. Nathanson ◽  
Timothy R. Rebbeck
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Propensity Scores ◽  
Association Studies ◽  
Germ Cell Tumors ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Testicular Germ Cell ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.

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