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  • 1
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 12 ( 2021-12-21)
    Abstract: Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals. Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs aiming to evaluate cross-ethnic performance. Results: We identified 10 statistically significant ( p & lt; 5.00E-08) and 90 additional suggestive ( p & lt; 1.00E-06) variants associated with incident stroke in the meta-analysis. Six of the top 10 variants were located in an intergenic region on chromosome 18 ( LINC01443-LOC644669 ). Additional variants of interest were located in or near the COL12A1, SNTG1 , PCDH7 , TMTC1 , and NTM genes . Replication was conducted in the Warfarin Pharmacogenomics Cohort (WPC), and while none of the variants were directly validated, seven intronic variants of NTM proximal to our target variants, had a p -value & lt;5.00E-04 in the WPC. The inclusion of the PRS did not improve the prediction accuracy compared to a reference model adjusting for age, sex, and genetic ancestry in either study and had lower predictive accuracy compared to models accounting for established stroke risk factors. These results demonstrate the necessity for PRS derivation in AAs, particularly for diseases that affect AAs disproportionately. Conclusion: This study highlights biologically plausible genetic determinants for incident stroke in hypertensive AAs. Ultimately, a better understanding of genetic risk factors for stroke in AAs may give new insight into stroke burden and potential clinical tools for those among the highest at risk.
    Type of Medium: Online Resource
    ISSN: 1664-8021
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2606823-0
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  • 2
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 13 ( 2022-5-3)
    Abstract: Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRS common ) with a rare variant PRS (PRS rare ) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m 2 ), obesity (BMI ≥ 30 kg/m 2 ), and extreme obesity (BMI ≥ 40 kg/m 2 ). We built PRSs common and PRSs rare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRS common explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRS rare explained 1.49%, and 2.97% and 3.68%, respectively. The PRS rare was associated with an increased risk of obesity and extreme obesity (OR obesity = 1.37 per SD PRS , P obesity = 1.7x10 -85 ; OR extremeobesity = 1.55 per SD PRS , P extremeobesity = 3.8x10 -40 ), which was attenuated, after adjusting for PRS common (OR obesity = 1.08 per SD PRS , P obesity = 9.8x10 -6 ; OR extremeobesity = 1.09 per SD PRS , P extremeobesity = 0.02). When PRS rare and PRS common are combined, the increase in explained variance attributed to PRS rare was small (incremental Nagelkerke R 2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRS rare to PRS common provided little improvement to the prediction of obesity (PRS rare AUC = 0.591; PRS common AUC = 0.708; PRS combined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRS rare provides limited improvement over PRS common in the prediction of obesity risk, based on these large populations.
    Type of Medium: Online Resource
    ISSN: 1664-2392
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2592084-4
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  • 3
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 9 ( 2022-9-28)
    Abstract: Some but not all African-Americans (AA) who carry APOL1 nephropathy risk variants ( APOL1 ) develop kidney failure (end-stage kidney disease, ESKD). To identify genetic modifiers, we assessed gene–gene interactions in a large prospective cohort of the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Methods Genotypes from 8,074 AA participants were obtained from Illumina Infinium Multi-Ethnic AMR/AFR Extended BeadChip. We compared 388 incident ESKD cases with 7,686 non-ESKD controls, using a two-locus interaction approach. Logistic regression was used to examine the effect of APOL1 risk status (using recessive and additive models), single nucleotide polymorphism (SNP), and APOL 1 * SNP interaction on incident ESKD, adjusting for age, sex, and ancestry. APOL1 * SNP interactions that met the threshold of 1.0 × 10 −5 were replicated in the Genetics of Hypertension Associated Treatment (GenHAT) study (626 ESKD cases and 6,165 controls). In a sensitivity analysis, models were additionally adjusted for diabetes status. We conducted additional replication in the BioVU study. Results Two APOL1 risk alleles prevalence (recessive model) was similar in the REGARDS and GenHAT studies. Only one APOL1 –SNP interaction, for rs7067944 on chromosome 10, ~10 KB from the PCAT5 gene met the genome-wide statistical threshold ( P interaction = 3.4 × 10 −8 ), but this interaction was not replicated in the GenHAT study. Among other relevant top findings (with P interaction & lt; 1.0 × 10 −5 ), a variant ( rs2181251 ) near SMOC2 on chromosome six interacted with APOL1 risk status (additive) on ESKD outcomes (REGARDS study, P interaction =5.3 × 10 −6 ) but the association was not replicated (GenHAT study, P interaction = 0.07, BioVU study, P interaction = 0.53). The association with the locus near SMOC2 persisted further in stratified analyses. Among those who inherited ≥1 alternate allele of rs2181251, APOL1 was associated with an increased risk of incident ESKD (OR [95%CI] = 2.27[1.53, 3.37] ) but APOL1 was not associated with ESKD in the absence of the alternate allele (OR [95%CI] = 1.34[0.96, 1.85] ) in the REGARDS study. The associations were consistent after adjusting for diabetes. Conclusion In a large genome-wide association study of AAs, a locus SMOC2 exhibited a significant interaction with the APOL1 locus. SMOC2 contributes to the progression of fibrosis after kidney injury and the interaction with APOL1 variants may contribute to an explanation for why only some APOLI high-risk individuals develop ESKD.
    Type of Medium: Online Resource
    ISSN: 2296-858X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2775999-4
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  • 4
    In: Frontiers in Big Data, Frontiers Media SA, Vol. 5 ( 2022-6-22)
    Abstract: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.
    Type of Medium: Online Resource
    ISSN: 2624-909X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2957497-3
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