Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous ...socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.
During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these ...approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.
The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping ...methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.
We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.
FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com
: christian.benner@helsinki.fi or matti.pirinen@helsinki.fi.
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). ...We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits ...of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance's PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.
...we discuss some key future advances, open questions and challenges in this developing field, when moving toward low-frequency variants and cross-phenotype interactions. Multivariate modeling ...approaches have already been shown to provide improved insights into genetic mechanisms and the interaction networks behind many complex traits, including atherosclerosis, coronary heart disease, and lipid levels, which would have gone undetected using the standard univariate modeling 2, 19-22.
Circulating cytokines and growth factors are regulators of inflammation and have been implicated in autoimmune and metabolic diseases. In this genome-wide association study (GWAS) of up to 8,293 ...Finns we identified 27 genome-widely significant loci (p < 1.2 × 10−9) for one or more cytokines. Fifteen of the associated variants had expression quantitative trait loci in whole blood. We provide genetic instruments to clarify the causal roles of cytokine signaling and upstream inflammation in immune-related and other chronic diseases. We further link inflammatory markers with variants previously associated with autoimmune diseases such as Crohn disease, multiple sclerosis, and ulcerative colitis and hereby elucidate the molecular mechanisms underpinning these diseases and suggest potential drug targets.
Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is ...unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.
We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.
A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.
Smoking behaviors, including amount smoked, smoking cessation, and tobacco-related diseases, are altered by the rate of nicotine clearance. Nicotine clearance can be estimated using the nicotine ...metabolite ratio (NMR) (ratio of 3'hydroxycotinine/cotinine), but only in current smokers. Advancing the genomics of this highly heritable biomarker of CYP2A6, the main metabolic enzyme for nicotine, will also enable investigation of never and former smokers. We performed the largest genome-wide association study (GWAS) to date of the NMR in European ancestry current smokers (n = 5185), found 1255 genome-wide significant variants, and replicated the chromosome 19 locus. Fine-mapping of chromosome 19 revealed 13 putatively causal variants, with nine of these being highly putatively causal and mapping to CYP2A6, MAP3K10, ADCK4, and CYP2B6. We also identified a putatively causal variant on chromosome 4 mapping to TMPRSS11E and demonstrated an association between TMPRSS11E variation and a UGT2B17 activity phenotype. Together the 14 putatively causal SNPs explained ~38% of NMR variation, a substantial increase from the ~20 to 30% previously explained. Our additional GWASs of nicotine intake biomarkers showed that cotinine and smoking intensity (cotinine/cigarettes per day (CPD)) shared chromosome 19 and chromosome 4 loci with the NMR, and that cotinine and a more accurate biomarker, cotinine + 3'hydroxycotinine, shared a chromosome 15 locus near CHRNA5 with CPD and Pack-Years (i.e., cumulative exposure). Understanding the genetic factors influencing smoking-related traits facilitates epidemiological studies of smoking and disease, as well as assists in optimizing smoking cessation support, which in turn will reduce the enormous personal and societal costs associated with smoking.
OBJECTIVE—Genome-wide association studies have identified several genetic variants associated with coronary heart disease (CHD). The aim of this study was to evaluate the genetic risk discrimination ...and reclassification and apply the results for a 2-stage population risk screening strategy for CHD.
APPROACH AND RESULTS—We genotyped 28 genetic variants in 24 124 participants in 4 Finnish population-based, prospective cohorts (recruitment years 1992–2002). We constructed a multilocus genetic risk score and evaluated its association with incident cardiovascular disease events. During the median follow-up time of 12 years (interquartile range 8.75–15.25 years), we observed 1093 CHD, 1552 cardiovascular disease, and 731 acute coronary syndrome events. Adding genetic information to conventional risk factors and family history improved risk discrimination of CHD (C-index 0.856 versus 0.851; P=0.0002) and other end points (cardiovascular diseaseC-index 0.840 versus 0.837, P=0.0004; acute coronary syndromeC-index 0.859 versus 0.855, P=0.001). In a standard population of 100 000 individuals, additional genetic screening of subjects at intermediate risk for CHD would reclassify 2144 subjects (12%) into high-risk category. Statin allocation for these subjects is estimated to prevent 135 CHD cases over 14 years. Similar results were obtained by external validation, where the effects were estimated from a training data set and applied for a test data set.
CONCLUSIONS—Genetic risk score improves risk prediction of CHD and helps to identify individuals at high risk for the first CHD event. Genetic screening for individuals at intermediate cardiovascular risk could help to prevent future cases through better targeting of statins.
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