Publications
113 results found
Wang Z, Emmerich A, Pillon NJ, et al., 2022, Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention, NATURE GENETICS, Vol: 54, Pages: 1332-+, ISSN: 1061-4036
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- Citations: 3
Prokopenko I, Maina J, Pascat V, et al., 2022, Abdominal obesity, rather than overall obesity, is a causal risk factor for pancreatic cancer
<jats:title>Abstract</jats:title> <jats:p>Obesity and type 2 diabetes (T2D) are associated with increased risk of pancreatic cancer. Here we assessed the relationship between pancreatic cancer and two distinct measures of obesity, namely total adiposity, using BMI, versus abdominal, using BMI adjusted waist-to-hip ratio (WHRadjBMI) using polygenic scores (PGS) and Mendelian randomization (MR). We constructed z-score weighted PGS for BMI and WHRadjBMI using publicly available data and tested for their association with pancreatic cancer defined in UK biobank (UKBB). Using publicly available summary statistics we then performed bi-directional MR analyses between the two obesity traits and pancreatic cancer. PGS<jats:sub>BMI</jats:sub> was significantly (multiple testing-corrected) associated with pancreatic cancer (OR[95%CI] = 1.077[1.023–1.13], <jats:italic>P</jats:italic> = 0.0052). The significance of association declined after T2D adjustment (OR[95%CI] = 1.069[1.015–1.079], <jats:italic>P</jats:italic> = 0.012). MR analyses showed a nominally significant causal effect of WHRadjBMI on pancreatic cancer (OR[95%CI] = 1.00095[1.00011–1.0018], <jats:italic>P</jats:italic> = 0.027). Overall, we show that abdominal adiposity measured using WHRadjBMI, is a more important causal risk factor for pancreatic cancer compared to total adiposity, with T2D being a potential driver of this relationship.</jats:p>
Okbay A, Wu Y, Wang N, et al., 2022, Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals., Nat Genet, Vol: 54, Pages: 437-449
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.
Ronkainen J, Nedelec R, Atehortua A, et al., 2022, LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases, ENVIRONMENTAL EPIDEMIOLOGY, Vol: 6
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- Citations: 1
Chen J, Spracklen CN, Marenne G, et al., 2021, The trans-ancestral genomic architecture of glycemic traits, Nature Genetics, Vol: 53, Pages: 840-860, ISSN: 1061-4036
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
Lagou V, Mägi R, Hottenga J-J, et al., 2021, Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability., Nat Commun, Vol: 12
Lagou V, Maegi R, Hottenga J-J, et al., 2021, Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability, NATURE COMMUNICATIONS, Vol: 12, ISSN: 2041-1723
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- Citations: 42
Alves AC, De Silva NMG, Karhunen V, et al., 2019, GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI, Science Advances, Vol: 5, ISSN: 2375-2548
Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here we combine genome-wide association studies with modelling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score and co-localization analyses to determine how developmental timings, molecular pathways and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult BMI, with variants associated with adult BMI acting as early as 4-6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.
Rhodes CJ, Batai K, Bleda M, et al., 2019, Genetic determinants of risk in pulmonary arterial hypertension: international case-control studies and meta-analysis, Lancet Respiratory Medicine, Vol: 7, Pages: 227-238, ISSN: 2213-2600
BackgroundRare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes.MethodsWe did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11 744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses.FindingsA locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55–2·08], p=5·13 × 10–15) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42–1·71], p=7·65 × 10–20) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25–1·48], p=1·69 × 10–12; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-media
Linner RK, Biroli P, Kong E, et al., 2019, Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences, NATURE GENETICS, Vol: 51, Pages: 245-+, ISSN: 1061-4036
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- Citations: 258
Prokopenko I, Miyakawa G, Zheng B, et al., 2019, Alzheimer's disease pathology explains association between dementia with Lewy bodies and APOE-ε4/TOMM40 long poly-T repeat allele variants., Alzheimers & Dementia, Vol: 5, Pages: 814-824, ISSN: 1552-5260
Introduction: The role of TOMM40-APOE 19q13.3 region variants is well documented in Alzheimer's disease (AD) but remains contentious in dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). Methods: We dissected genetic profiles within the TOMM40-APOE region in 451 individuals from four European brain banks, including DLB and PDD cases with/without neuropathological evidence of AD-related pathology and healthy controls. Results: TOMM40-L/APOE-ε4 alleles were associated with DLB (OR TOMM40 -L = 3.61; P value = 3.23 × 10-9; OR APOE -ε4 = 3.75; P value = 4.90 × 10-10) and earlier age at onset of DLB (HR TOMM40 -L = 1.33, P value = .031; HR APOE -ε4 = 1.46, P value = .004), but not with PDD. The TOMM40-L/APOE-ε4 effect was most pronounced in DLB individuals with concomitant AD pathology (OR TOMM40 -L = 4.40, P value = 1.15 × 10-6; OR APOE -ε4 = 5.65, P value = 2.97 × 10-8) but was not significant in DLB without AD. Meta-analyses combining all APOE-ε4 data in DLB confirmed our findings (ORDLB = 2.93, P value = 3.78 × 10-99; ORDLB+AD = 5.36, P value = 1.56 × 10-47). Discussion: APOE-ε4/TOMM40-L alleles increase susceptibility and risk of earlier DLB onset, an effect explained by concomitant AD-related pathology. These findings have important implications in future drug discovery and development efforts in DLB.
Kaakinen M, Prelot L, Draisma H, et al., 2018, Machine learning in multi-omics data to assess longitudinal predictors of glycaemic trait levels, 27th Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), Publisher: WILEY, Pages: 709-709, ISSN: 0741-0395
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- Citations: 1
Macare C, Ducci F, Zhang Y, et al., 2018, A neurobiological pathway to smoking in adolescence: TTC12-ANKK1-DRD2 variants and reward response, European Neuropsychopharmacology, Vol: 28, Pages: 1103-1114, ISSN: 0924-977X
The TTC12-ANKK1-DRD2 gene-cluster has been implicated in adult smoking. Here, we investigated the contribution of individual genes in the TTC12-ANKK1-DRD2 cluster in smoking and their association with smoking-associated reward processing in adolescence. A meta-analysis of TTC12-ANKK1-DRD2 variants and self-reported smoking behaviours was performed in four European adolescent cohorts (N = 14,084). The minor G-allele of rs2236709, mapping TTC12, was associated with self-reported smoking (p = 5.0 × 10−4) and higher plasma cotinine levels (p = 7.0 × 10−5). This risk allele was linked to an increased ventral-striatal blood-oxygen level-dependent (BOLD) response during reward anticipation (n = 1,263) and with higher DRD2 gene expression in the striatum (p = 0.013), but not with TTC12 or ANKK gene expression. These data suggest a role for the TTC12-ANKK1-DRD2 gene-cluster in adolescent smoking behaviours, provide evidence for the involvement of DRD2 in the early stages of addiction and support the notion that genetically-driven inter-individual differences in dopaminergic transmission mediate reward sensitivity and risk to smoking.
Kaakinen M, Jiang L, Lagou V, et al., 2018, Large-scale genetic meta-analysis and correlation analysis in up to 61,457 Europeans show large genetic overlap between fasting and random plasma glucose levels, 50th European-Society-of-Human-Genetics (ESHG) Conference, Publisher: NATURE PUBLISHING GROUP, Pages: 311-311, ISSN: 1018-4813
Lee JJ, Wedow R, Okbay A, et al., 2018, Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals, NATURE GENETICS, Vol: 50, Pages: 1112-+, ISSN: 1061-4036
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- Citations: 934
Draisma HHM, Wielscher M, Hassan S, et al., 2018, Multi-phenotype epigenome-wide association analysis of fasting glucose and insulin in 981 Finnish individuals, ESHG 2018, Publisher: Nature Publishing Group, ISSN: 1018-4813
Draisma HHM, Wielscher M, Hassan S, et al., 2017, Multi–phenotype epigenome-wide association analysis of fasting glucose and insulin in 981 Finns, IC Genomics Symposium 2017, Publisher: F1000 Research Ltd, ISSN: 2046-1402
Rietschel L, Streit F, Zhu G, et al., 2017, Hair cortisol in twins: heritability and genetic overlap with psychological variables and stress-system genes, Scientific Reports, Vol: 7, ISSN: 2045-2322
Hair cortisol concentration (HCC) is a promising measure of long-Term hypothalamus-pituitary-Adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables.
Scott RA, Scott LJ, Mägi R, et al., 2017, An expanded genome-wide association study of Type 2 diabetes in Europeans, Diabetes, Vol: 66, Pages: 2888-2902, ISSN: 0012-1797
To characterise type 2 diabetes (T2D) associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D cases and 132,532 controls of European ancestry after imputation using the 1000 Genomes multi-ethnic reference panel. Promising association signals were followed-up in additional data sets (of 14,545 or 7,397 T2D cases and 38,994 or 71,604 controls). We identified 13 novel T2D-associated loci (p<5×10(-8)), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common SNVs. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion, and in adipocytes, monocytes and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.
Kaakinen M, Magi R, Fischer K, et al., 2017, A rare-variant test for high-dimensional data, European Journal of Human Genetics, Vol: 25, Pages: 988-994, ISSN: 1018-4813
Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved power through multi-phenotype analysis (MPA) and use of a wider allele frequency range, including rare variants (RVs). MPA methods for single-variant association have been proposed, but given their low power for RVs, more efficient approaches are required. We propose multi-phenotype analysis of rare variants (MARV), a burden test-based method for RVs extended to the joint analysis of multiple phenotypes through a powerful reverse regression technique. Specifically, MARV models the proportion of RVs at which minor alleles are carried by individuals within a genomic region as a linear combination of multiple phenotypes, which can be both binary and continuous, and the method accommodates directly the genotyped and imputed data. The full model, including all phenotypes, is tested for association for discovery, and a more thorough dissection of the phenotype combinations for any set of RVs is also enabled. We show, via simulations, that the type I error rate is well controlled under various correlations between two continuous phenotypes, and that the method outperforms a univariate burden test in all considered scenarios. Application of MARV to 4876 individuals from the Northern Finland Birth Cohort 1966 for triglycerides, high- and low-density lipoprotein cholesterols highlights known loci with stronger signals of association than those observed in univariate RV analyses and suggests novel RV effects for these lipid traits.
Kaakinen M, Magi R, Fischer K, et al., 2017, MARV: a tool for genome-wide multi-phenotype analysis of rare variants, BMC BIOINFORMATICS, Vol: 18, ISSN: 1471-2105
Background:Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.Results:We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (P TG+FI = 1.8 × 10−9) than observed in single phenotype rare variant analyses (P TG = 6.5 × 10−8 and P FI = 0.27).Conclusions:MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenot
Mägi R, Suleimanov YV, Clarke GM, et al., 2017, SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes, BMC Bioinformatics, Vol: 18, ISSN: 1471-2105
BACKGROUND: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. RESULTS: We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10(-8)), which has not been reported in previous large-scale GWAS of lipid traits. CONCLUSIONS: The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.
Ehret GB, Ferreira T, Chasman DI, et al., 2016, The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals, Nature Genetics, Vol: 48, Pages: 1171-1184, ISSN: 1546-1718
To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.
Kaakinen M, Claringbould A, Hagenbeek F, et al., 2016, Genome-wide multiphenotype and eQTL analyses provide novel insights into omega fatty acid metabolism and Type 2 diabetes, 76th Scientific Sessions of the American-Diabetes-Association, Publisher: American Diabetes Association, Pages: A52-A52, ISSN: 0012-1797
Winkler TW, Justice AE, Graff M, et al., 2016, Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study, PLOS Genetics, Vol: 12, Pages: e1006166-e1006166, ISSN: 1553-7390
Okbay A, Beauchamp JP, Fontana MA, et al., 2016, Genome-wide association study identifies 74 loci associated with educational attainment, Nature, Vol: 533, Pages: 539-542, ISSN: 0028-0836
Okbay A, Baselmans BML, De Neve J-E, et al., 2016, Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses, Nature Genetics, Vol: 48, Pages: 624-633, ISSN: 1061-4036
Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρˆ| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
Linneberg A, Jacobsen RK, Skaaby T, et al., 2015, Effect of smoking on blood pressure and resting heart rate: a Mendelian randomization meta-analysis in the CARTA consortium, Circulation-Cardiovascular Genetics, Vol: 8, Pages: 832-841, ISSN: 1942-325X
Background—Smoking is an important cardiovascular disease risk factor, but the mechanisms linking smoking to blood pressure are poorly understood.Methods and Results—Data on 141 317 participants (62 666 never, 40 669 former, 37 982 current smokers) from 23 population-based studies were included in observational and Mendelian randomization meta-analyses of the associations of smoking status and smoking heaviness with systolic and diastolic blood pressure, hypertension, and resting heart rate. For the Mendelian randomization analyses, a genetic variant rs16969968/rs1051730 was used as a proxy for smoking heaviness in current smokers. In observational analyses, current as compared with never smoking was associated with lower systolic blood pressure and diastolic blood pressure and lower hypertension risk, but with higher resting heart rate. In observational analyses among current smokers, 1 cigarette/day higher level of smoking heaviness was associated with higher (0.21 bpm; 95% confidence interval 0.19; 0.24) resting heart rate and slightly higher diastolic blood pressure (0.05 mm Hg; 95% confidence interval 0.02; 0.08) and systolic blood pressure (0.08 mm Hg; 95% confidence interval 0.03; 0.13). However, in Mendelian randomization analyses among current smokers, although each smoking increasing allele of rs16969968/rs1051730 was associated with higher resting heart rate (0.36 bpm/allele; 95% confidence interval 0.18; 0.54), there was no strong association with diastolic blood pressure, systolic blood pressure, or hypertension. This would suggest a 7 bpm higher heart rate in those who smoke 20 cigarettes/day.Conclusions—This Mendelian randomization meta-analysis supports a causal association of smoking heaviness with higher level of resting heart rate, but not with blood pressure. These findings suggest that part of the cardiovascular risk of smoking may operate through increasing resting heart rate.
Miettunen J, Nordstrom T, Kaakinen M, et al., 2015, Latent variable mixture modeling in psychiatric research - a review and application, Psychological Medicine, Vol: 46, Pages: 457-467, ISSN: 0033-2917
Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sortingcases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to providea non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classifyindividuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach thatrepresents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categoricaland dimensional states of affairs. This article provides an overview of latent variable mixture models and illustratesthe application of these methods by applying them to the study of the latent structure of psychotic experiences. Theflexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatricand psychological phenomena. They also allow researchers to address research questions that directly compare the viabilityof dimensional, categorical and hybrid conceptions of constructs.
Winkler TW, Justice AE, Graff M, et al., 2015, The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study, PLOS GENETICS, Vol: 11, ISSN: 1553-7404
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- Citations: 229
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