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- Marini, S., et al.
(författare)
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Association of Apolipoprotein E With Intracerebral Hemorrhage Risk by Race/Ethnicity A Meta-analysis
- 2019
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Ingår i: Jama Neurology. - : American Medical Association (AMA). - 2168-6149 .- 2168-6157. ; 76:4, s. 480-491
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Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE Genetic studies of intracerebral hemorrhage (ICH) have focused mainly on white participants, but genetic risk may vary or could be concealed by differing nongenetic coexposures in nonwhite populations. Transethnic analysis of risk may clarify the role of genetics in ICH risk across populations. OBJECTIVE To evaluate associations between established differences in ICH risk by race/ethnicity and the variability in the risks of apolipoprotein E (APOE) epsilon 4 alleles, the most potent genetic risk factor for ICH. DESIGN, SETTING, AND PARTICIPANTS This case-control study of primary ICH meta-analyzed the association of APOE allele status on ICH risk, applying a 2-stage clustering approach based on race/ethnicity and stratified by a contributing study. A propensity score analysis was used to model the association of APOE with the burden of hypertension across race/ethnic groups. Primary ICH cases and controls were collected from 3 hospital- and population-based studies in the United States and 8 in European sites in the International Stroke Genetic Consortium. Participants were enrolled from January 1, 1999, to December 31, 2017. Participants with secondary causes of ICH were excluded from enrollment. Controls were regionally matched within each participating study. MAIN OUTCOMES AND MEASURES Clinical variables were systematically obtained from structured interviews within each site. APOE genotype was centrally determined for all studies. RESULTS In total, 13 124 participants (7153 [54.5%] male with a median [interquartile range] age of 66 [56-76] years) were included. In white participants, APOE epsilon 2 (odds ratio [OR], 1.49; 95% CI, 1.24-1.80; P < .001) and APOE epsilon 4 (OR, 1.51; 95% CI, 1.23-1.85; P < .001) were associated with lobar ICH risk; however, within self-identified Hispanic and black participants, no associations were found. After propensity score matching for hypertension burden, APOE epsilon 4 was associated with lobar ICH risk among Hispanic (OR, 1.14; 95% CI, 1.03-1.28; P = .01) but not in black (OR, 1.02; 95% CI, 0.98-1.07; P = .25) participants. APOE epsilon 2 and epsilon 4 did not show an association with nonlobar ICH risk in any race/ethnicity. CONCLUSIONS AND RELEVANCE APOE epsilon 4 and epsilon 2 alleles appear to affect lobar ICH risk variably by race/ethnicity, associations that are confirmed in white individuals but can be shown in Hispanic individuals only when the excess burden of hypertension is propensity score-matched; further studies are needed to explore the interactions between APOE alleles and environmental exposures that vary by race/ethnicity in representative populations at risk for ICH.
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- Tabassum, R, et al.
(författare)
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Genetic architecture of human plasma lipidome and its link to cardiovascular disease
- 2019
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 4329-
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Tidskriftsartikel (refereegranskat)abstract
- Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10−8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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