Biology subjects

Pa, J.

Publications and source records attributed to Pa, J..

4 recordsLinked to original sources

bioRxiv2020

Mitochondrial pathway polygenic risk scores are associated with Alzheimer's Disease

INTRODUCTIONGenetic, animal and epidemiological studies involving biomolecular and clinical endophenotypes implicate mitochondrial dysfunction in Alzheimers disease (AD) pathogenesis. Polygenic risk scores (PRS) provide a novel approach to assess biological pathway-associated disease risk by combining the effects of variation at multiple, functionally related genes. METHODSWe investigated associations of PRS for genes involved in 12 mitochondrial pathways (pathway-PRS) related to AD in 854 participants from Alzheimers Disease Neuroimaging Initiative. RESULTSPathway-PRS for four mitochondrial pathways are significantly associated with increased AD risk: (i) response to oxidative stress (OR: 2.01 [95% Cl: 1.71, 2.37]); (ii) mitochondrial transport (OR: 1.81 [95% Cl: 1.55, 2.13]); (iii) hallmark oxidative phosphorylation (OR: 1.23 [95% Cl: 1.07, 1.41]); and (iv) mitochondrial membrane potential regulation (OR: 1.18 [95% Cl: 1.03, 1.36]). DISCUSSIONTherapeutic approaches targeting these pathways may have potential for modifying AD pathogenesis. Further investigation is required to establish a causal role for these pathways in AD pathology.

neuroscience↗

bioRxiv2019

Mitonuclear interactions influence Alzheimer’s disease risk

We examined the associations between mitochondrial DNA haplogroups (MT-hg) and their interactions with a polygenic risk score based on nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset of dementia (AOO). Logistic regression was used to determine the effect of MT-hgs and nMT-PRS on dementia at baseline (332 controls / 204 cases). Cox proportional hazards models were used to model dementia AOO (n=1047; 433 incident cases). Additionally, we tested for interactions between MT-hg and nMT-PRS in the logistic and Cox models. MT-hg K and a one SD larger nMT-PRS were associated with elevated odds of dementia. Significant antagonistic interactions between the nMT-PRS and MT-hg K and T were observed. Individual MT-hg were not associated with AOO; however, a significant antagonistic interactions was observed between the nMT-PRS and MT-hg T and a synergistic interaction between the nMT-PRS and MT-hg V. These results suggest that MT-hgs influence dementia risk, and that variants in the nuclear and mitochondrial genome interact to influence the age of onset of dementia.\n\nHighlightsO_LIMitochondrial dysfunction has been proposed to influence dementia risk\nC_LIO_LIMT-hg K and T interacted with a genetic risk score to reduce dementia risk\nC_LIO_LIMT-hg T and V interacted with a genetic risk score to influence dementia age of onset\nC_LI

genetics↗

bioRxiv2019

MitoImpute: A Snakemake pipeline for imputation of mitochondrial genetic variants

BackgroundVariation in mitochondrial DNA (mtDNA) identified by genotyping microarrays or by sequencing only hypervariable regions of the genome may be insufficient to reliably assign mitochondrial genomes to phylogenetic lineages or haplogroups. This lack of resolution can limit functional and clinical interpretation of a substantial body of existing mtDNA data. To address this limitation, we developed and evaluated a method for imputing missing mtDNA single nucleotide variants (mtSNVs) that uses a large reference alignment of complete mtDNA sequences. The method and reference alignment are combined into a pipeline, which we call MitoImpute. ResultsWe aligned the sequences of 36,960 complete human mitochondrial genomes downloaded from GenBank, filtered and controlled for quality. These sequences were reformatted for use in imputation software, IMPUTE2. We assessed the imputation accuracy of MitoImpute by measuring haplogroup and genotype concordance in data from the 1,000 Genomes Project and the Alzheimers Disease Neuroimaging Initiative (ADNI). The mean improvement of haplogroup assignment in the 1,000 Genomes samples was 42.7% (Matthews correlation coefficient = 0.64). In the ADNI cohort, we imputed missing single nucleotide variants. ConclusionsThese results show that our reference alignment and panel can be used to impute missing mtSNVs in exiting data obtained from using microarrays, thereby broadening the scope of functional and clinical investigation of mtDNA. This improvement may be particularly useful in studies where participants have been recruited over time and mtDNA data obtained using different methods, enabling better integration of early data collected using less accurate methods with more recent sequence data.

genomics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗