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BackgroundResearchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.MethodsWe developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.ResultsEvent rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.ConclusionsWe conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

The full range of cell functions is under-determined in most human diseases. The evidence that somatic cell competition and clonal imbalance play a role in non-neoplastic chronic disease reveal a need for a dedicated effort to explore single cell function if we are to understand the mechanisms by which cell population behaviors influence disease. It will be vital to document not only the prevalent pathologic behaviors but also those beneficial functions eliminated or suppressed by competition. An improved mechanistic understanding of the role of somatic cell biology will help to stratify chronic disease, define more precisely at an individual level the role of environmental factors and establish principles for prevention and potential intervention throughout the life course and across the trajectory from wellness to disease.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

Mutations in the calcium-binding protein calsequestrin cause the highly lethal familial arrhythmia catecholaminergic polymorphic ventricular tachycardia (CPVT). In vivo, calsequestrin multimerizes into filaments, but there is not yet an atomic-resolution structure of a calsequestrin filament. We report a crystal structure of a human cardiac calsequestrin filament with supporting mutational analysis and in vitro filamentation assays. We identify and characterize a new disease-associated calsequestrin mutation, S173I, that is located at the filament-forming interface, and further show that a previously reported dominant disease mutation, K180R, maps to the same surface. Both mutations disrupt filamentation, suggesting that disease pathology is due to defects in multimer formation. An ytterbium-derivatized structure pinpoints multiple credible calcium sites at filament-forming interfaces, explaining the atomic basis of calsequestrin filamentation in the presence of calcium. Our study thus provides a unifying molecular mechanism through which dominant-acting calsequestrin mutations provoke lethal arrhythmias.

BackgroundGenetic variants in calsequestrin-2 (CASQ2) cause an autosomal recessive form of catecholaminergic polymorphic ventricular tachycardia (CPVT), although isolated reports have identified arrhythmic phenotypes among heterozygotes. Improved insight into the inheritance patterns, arrhythmic risks, and molecular mechanisms of CASQ2-CPVT was sought through an international multicenter collaboration.MethodsGenotype-phenotype segregation in CASQ2-CPVT families was assessed, and the impact of genotype on arrhythmic risk was evaluated using Cox regression models. Putative dominant CASQ2 missense variants and the established recessive CASQ2-p.R33Q variant were evaluated using oligomerization assays and their locations mapped to a recent CASQ2 filament structure.ResultsA total of 112 individuals, including 36 CPVT probands (24 homozygotes/compound heterozygotes and 12 heterozygotes) and 76 family members possessing at least 1 presumed pathogenic CASQ2 variant, were identified. Among CASQ2 homozygotes and compound heterozygotes, clinical penetrance was 97.1% and 26 of 34 (76.5%) individuals had experienced a potentially fatal arrhythmic event with a median age of onset of 7 years (95% CI, 6-11). Fifty-one of 66 CASQ2 heterozygous family members had undergone clinical evaluation, and 17 of 51 (33.3%) met diagnostic criteria for CPVT. Relative to CASQ2 heterozygotes, CASQ2 homozygote/compound heterozygote genotype status in probands was associated with a 3.2-fold (95% CI, 1.3-8.0; P=0.013) increased hazard of a composite of cardiac syncope, aborted cardiac arrest, and sudden cardiac death, but a 38.8-fold (95% CI, 5.6-269.1; P

BackgroundThe ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making.Methods and resultsWe identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse.ConclusionsModern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.

BackgroundAutomated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in primary care and rural settings. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram interpretation, including (1) view identification, (2) image segmentation, (3) quantification of structure and function, and (4) disease detection.MethodsUsing 14 035 echocardiograms spanning a 10-year period, we trained and evaluated convolutional neural network models for multiple tasks, including automated identification of 23 viewpoints and segmentation of cardiac chambers across 5 common views. The segmentation output was used to quantify chamber volumes and left ventricular mass, determine ejection fraction, and facilitate automated determination of longitudinal strain through speckle tracking. Results were evaluated through comparison to manual segmentation and measurements from 8666 echocardiograms obtained during the routine clinical workflow. Finally, we developed models to detect 3 diseases: hypertrophic cardiomyopathy, cardiac amyloid, and pulmonary arterial hypertension.ResultsConvolutional neural networks accurately identified views (eg, 96% for parasternal long axis), including flagging partially obscured cardiac chambers, and enabled the segmentation of individual cardiac chambers. The resulting cardiac structure measurements agreed with study report values (eg, median absolute deviations of 15% to 17% of observed values for left ventricular mass, left ventricular diastolic volume, and left atrial volume). In terms of function, we computed automated ejection fraction and longitudinal strain measurements (within 2 cohorts), which agreed with commercial software-derived values (for ejection fraction, median absolute deviation=9.7% of observed, N=6407 studies; for strain, median absolute deviation=7.5%, n=419, and 9.0%, n=110) and demonstrated applicability to serial monitoring of patients with breast cancer for trastuzumab cardiotoxicity. Overall, we found automated measurements to be comparable or superior to manual measurements across 11 internal consistency metrics (eg, the correlation of left atrial and ventricular volumes). Finally, we trained convolutional neural networks to detect hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with C statistics of 0.93, 0.87, and 0.85, respectively.ConclusionsOur pipeline lays the groundwork for using automated interpretation to support serial patient tracking and scalable analysis of millions of echocardiograms archived within healthcare systems.

Disruption of hepatocyte growth hormone (GH) signaling through disruption of Jak2 (JAK2L) leads to fatty liver. Previously, we demonstrated that development of fatty liver depends on adipocyte GH signaling. We sought to determine the individual roles of hepatocyte and adipocyte Jak2 on whole-body and tissue insulin sensitivity and liver metabolism. On chow, JAK2L mice had hepatic steatosis and severe whole-body and hepatic insulin resistance. However, concomitant deletion of Jak2 in hepatocytes and adipocytes (JAK2LA) completely normalized insulin sensitivity while reducing liver lipid content. On high-fat diet, JAK2L mice had hepatic steatosis and insulin resistance despite protection from diet-induced obesity. JAK2LA mice had higher liver lipid content and no protection from obesity but retained exquisite hepatic insulin sensitivity. AKT activity was selectively attenuated in JAK2L adipose tissue, whereas hepatic insulin signaling remained intact despite profound hepatic insulin resistance. Therefore, JAK2 in adipose tissue is epistatic to liver with regard to insulin sensitivity and responsiveness, despite fatty liver and obesity. However, hepatocyte autonomous JAK2 signaling regulates liver lipid deposition under conditions of excess dietary fat. This work demonstrates how various tissues integrate JAK2 signals to regulate insulin/glucose and lipid metabolism.

The striking rise of obesity-related metabolic disorders has focused attention on adipocytes as critical mediators of disease phenotypes. To better understand the role played by excess adipose in metabolic dysfunction it is crucial to decipher the transcriptional underpinnings of the low-grade adipose inflammation characteristic of diseases such as type 2 diabetes. Through employing a comparative transcriptomics approach, we identified IRF1 as differentially regulated between primary and in vitro-derived genetically matched adipocytes. This suggests a role as a mediator of adipocyte inflammatory phenotypes, similar to its function in other tissues. Utilizing adipose-derived mesenchymal progenitors we subsequently demonstrated that expression of IRF1 in adipocytes indeed contributes to upregulation of inflammatory processes, both in vitro and in vivo. This highlights IRF1's relevance to obesity-related inflammation and the resultant metabolic dysregulation.

BackgroundTruncating mutations in the giant sarcomeric gene Titin are the most common type of genetic alteration in dilated cardiomyopathy. Detailed studies have amassed a wealth of information about truncating variant position in cases and controls. Nonetheless, considerable confusion exists as to how to interpret the pathogenicity of these variants, hindering our ability to make useful recommendations to patients.Methods and resultsBuilding on our recent discovery of a conserved internal promoter within the Titin gene, we sought to develop an integrative statistical model to explain the observed pattern of Titin truncation variants in patients with dilated cardiomyopathy and population controls. We amassed Titin truncation mutation information from 1714 human dilated cardiomyopathy cases and >69 000 controls and found 3 factors explaining the distribution of Titin mutations: (1) alternative splicing, (2) whether the internal promoter Cronos isoform was disrupted, and (3) whether the distal C terminus was targeted (in keeping with the observation that truncation variants in this region escape nonsense-mediated decay and continue to be incorporated in the sarcomere). A model using these 3 factors had strong predictive performance with an area under the receiver operating characteristic curve of 0.81. Accordingly, individuals with either the most severe form of dilated cardiomyopathy or whose mutations demonstrated clear family segregation experienced the highest risk profile across all 3 components.ConclusionsWe conclude that quantitative models derived from large-scale human genetic and phenotypic data can be applied to help overcome the ever-growing challenges of genetic data interpretation. Results of our approach can be found at http://cvri.ucsf.edu/~deo/TTNtruncationvariant.html.

BackgroundTruncating mutations in the giant sarcomeric gene Titin are the most common type of genetic alteration in dilated cardiomyopathy. Detailed studies have amassed a wealth of information about truncating variant position in cases and controls. Nonetheless, considerable confusion exists as to how to interpret the pathogenicity of these variants, hindering our ability to make useful recommendations to patients.Methods and resultsBuilding on our recent discovery of a conserved internal promoter within the Titin gene, we sought to develop an integrative statistical model to explain the observed pattern of Titin truncation variants in patients with dilated cardiomyopathy and population controls. We amassed Titin truncation mutation information from 1714 human dilated cardiomyopathy cases and >69 000 controls and found 3 factors explaining the distribution of Titin mutations: (1) alternative splicing, (2) whether the internal promoter Cronos isoform was disrupted, and (3) whether the distal C terminus was targeted (in keeping with the observation that truncation variants in this region escape nonsense-mediated decay and continue to be incorporated in the sarcomere). A model using these 3 factors had strong predictive performance with an area under the receiver operating characteristic curve of 0.81. Accordingly, individuals with either the most severe form of dilated cardiomyopathy or whose mutations demonstrated clear family segregation experienced the highest risk profile across all 3 components.ConclusionsWe conclude that quantitative models derived from large-scale human genetic and phenotypic data can be applied to help overcome the ever-growing challenges of genetic data interpretation. Results of our approach can be found at http://cvri.ucsf.edu/~deo/TTNtruncationvariant.html.

For placental mammals, the transition from the in utero maternal environment to postnatal life requires the activation of thermogenesis to maintain their core temperature. This is primarily accomplished by induction of uncoupling protein 1 (UCP1) in brown and beige adipocytes, the principal sites for uncoupled respiration. Despite its importance, how placental mammals license their thermogenic adipocytes to participate in postnatal uncoupled respiration is not known. Here, we provide evidence that the "alarmin" IL-33, a nuclear cytokine that activates type 2 immune responses, licenses brown and beige adipocytes for uncoupled respiration. We find that, in absence of IL-33 or ST2, beige and brown adipocytes develop normally but fail to express an appropriately spliced form of Ucp1 mRNA, resulting in absence of UCP1 protein and impairment in uncoupled respiration and thermoregulation. Together, these data suggest that IL-33 and ST2 function as a developmental switch to license thermogenesis during the perinatal period. PAPERCLIP.

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.

BackgroundStudies of ≤15 atrial fibrillation (AF) patients have identified atrial-specific mutations within connexin genes, suggesting that somatic mutations may account for sporadic cases of the arrhythmia. We sought to identify atrial somatic mutations among patients with and without AF using targeted deep next-generation sequencing of 560 genes, including genetic culprits implicated in AF, the Mendelian cardiomyopathies and channelopathies, and all ion channels within the genome.Methods and resultsTargeted gene capture and next-generation sequencing were performed on DNA from lymphocytes and left atrial appendages of 34 patients (25 with AF). Twenty AF patients had undergone cardiac surgery exclusively for pulmonary vein isolation and 17 had no structural heart disease. Sequence alignment and variant calling were performed for each atrial-lymphocyte pair using the Burrows-Wheeler Aligner, the Genome Analysis Toolkit, and MuTect packages. Next-generation sequencing yielded a median 265-fold coverage depth (interquartile range, 64-369). Comparison of the 3 million base pairs from each atrial-lymphocyte pair revealed a single potential somatic missense mutation in 3 AF patients and 2 in a single control (12 versus 11%; P=1). All potential discordant variants had low allelic fractions (range, 2.3%-7.3%) and none were detected with conventional sequencing.ConclusionsUsing high-depth next-generation sequencing and state-of-the art somatic mutation calling approaches, no pathogenic atrial somatic mutations could be confirmed among 25 AF patients in a comprehensive cardiac arrhythmia genetic panel. These findings indicate that atrial-specific mutations are rare and that somatic mosaicism is unlikely to exert a prominent role in AF pathogenesis.

RationaleTreatment of sinus node disease with regenerative or cell-based therapies will require a detailed understanding of gene regulatory networks in cardiac pacemaker cells (PCs).ObjectiveTo characterize the transcriptome of PCs using RNA sequencing and to identify transcriptional networks responsible for PC gene expression.Methods and resultsWe used laser capture microdissection on a sinus node reporter mouse line to isolate RNA from PCs for RNA sequencing. Differential expression and network analysis identified novel sinoatrial node-enriched genes and predicted that the transcription factor Islet-1 is active in developing PCs. RNA sequencing on sinoatrial node tissue lacking Islet-1 established that Islet-1 is an important transcriptional regulator within the developing sinoatrial node.Conclusions(1) The PC transcriptome diverges sharply from other cardiomyocytes; (2) Islet-1 is a positive transcriptional regulator of the PC gene expression program.

BackgroundHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories.Methods and resultsWe prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [P

ImportanceThe identification of a patient with a rare form of severe dysbetalipoproteinemia allowed the study of the consequences of total absence of apolipoprotein E (apoE).ObjectivesTo discover the molecular basis of this rare disorder and to determine the effects of complete absence of apoE on neurocognitive and visual function and on lipoprotein metabolism.Design, setting, and participantsWhole-exome sequencing was performed on the patient's DNA. He underwent detailed neurological and visual function testing and lipoprotein analysis. Lipoprotein analysis was also performed in the Cardiovascular Research Institute, University of California, San Francisco, on blood samples from the proband's mother, wife, 2 daughters, and normolipidemic control participants.Main outcome measuresWhole-exome sequencing, lipoprotein analysis, and neurocognitive function.ResultsThe patient was homozygous for an ablative APOE frameshift mutation (c.291del, p.E97fs). No other mutations likely to contribute to the phenotype were discovered, with the possible exception of two, in ABCC2 (p.I670T) and LIPC (p.G137R). Despite complete absence of apoE, he had normal vision, exhibited normal cognitive, neurological, and retinal function, had normal findings on brain magnetic resonance imaging, and had normal cerebrospinal fluid levels of β-amyloid and tau proteins. He had no significant symptoms of cardiovascular disease except a suggestion of myocardial ischemia on treadmill testing and mild atherosclerosis noted on carotid ultrasonography. He had exceptionally high cholesterol content (760 mg/dL; to convert to millimoles per liter, multiply by 0.0259) and a high cholesterol to triglycerides ratio (1.52) in very low-density lipoproteins with elevated levels of small-diameter high-density lipoproteins, including high levels of prebeta-1 high-density lipoprotein. Intermediate-density lipoproteins, low-density lipoproteins, and very low-density lipoproteins contained elevated apoA-I and apoA-IV levels. The patient's apoC-III and apoC-IV levels were decreased in very low-density lipoproteins. Electron microscopy revealed large lamellar particles having electron-opaque cores attached to electron-lucent zones in intermediate-density and low-density lipoproteins. Low-density lipoprotein particle diameters were distributed bimodally.Conclusions and relevanceDespite a profound effect on lipoprotein metabolism, detailed neurocognitive and retinal studies failed to demonstrate any defects. This suggests that functions of apoE in the brain and eye are not essential or that redundant mechanisms exist whereby its role can be fulfilled. Targeted knockdown of apoE in the central nervous system might be a therapeutic modality in neurodegenerative disorders.

In vertebrates, activation of innate immunity is an early response to injury, implicating it in the regenerative process. However, the mechanisms by which innate signals might regulate stem cell functionality are unknown. Here, we demonstrate that type 2 innate immunity is required for regeneration of skeletal muscle after injury. Muscle damage results in rapid recruitment of eosinophils, which secrete IL-4 to activate the regenerative actions of muscle resident fibro/adipocyte progenitors (FAPs). In FAPs, IL-4/IL-13 signaling serves as a key switch to control their fate and functions. Activation of IL-4/IL-13 signaling promotes proliferation of FAPs to support myogenesis while inhibiting their differentiation into adipocytes. Surprisingly, type 2 cytokine signaling is also required in FAPs, but not in myeloid cells, for rapid clearance of necrotic debris, a process that is necessary for timely and complete regeneration of tissues.

Lipoprotein(a) (Lp(a)) is an important causal cardiovascular risk factor, with serum Lp(a) levels predicting atherosclerotic heart disease and genetic determinants of Lp(a) levels showing association with myocardial infarction. Lp(a) levels vary widely between populations, with African-derived populations having nearly 2-fold higher Lp(a) levels than European Americans. We investigated the genetic basis of this difference in 4464 African Americans from the Jackson Heart Study (JHS) using a panel of up to 1447 ancestry informative markers, allowing us to accurately estimate the African ancestry proportion of each individual at each position in the genome. In an unbiased genome-wide admixture scan for frequency-differentiated genetic determinants of Lp(a) level, we found a convincing peak (LOD = 13.6) at 6q25.3, which spans the LPA locus. Dense fine-mapping of the LPA locus identified a number of strongly associated, common biallelic SNPs, a subset of which can account for up to 7% of the variation in Lp(a) level, as well as >70% of the African-European population differences in Lp(a) level. We replicated the association of the most strongly associated SNP, rs9457951 (p = 6 × 10(-22), 27% change in Lp(a) per allele, ∼5% of Lp(a) variance explained in JHS), in 1,726 African Americans from the Dallas Heart Study and found an even stronger association after adjustment for the kringle(IV) repeat copy number. Despite the strong association with Lp(a) levels, we find no association of any LPA SNP with incident coronary heart disease in 3,225 African Americans from the Atherosclerosis Risk in Communities Study.

Genome-wide association analysis in populations of European descent has recently found more than a hundred genetic variants affecting risk for common disease. An open question, however, is how relevant the variants discovered in Europeans are to other populations. To address this problem for cardiovascular phenotypes, we studied a cohort of 4,464 African Americans from the Jackson Heart Study (JHS), in whom we genotyped both a panel of 12 recently discovered genetic variants known to predict lipid profile levels in Europeans and a panel of up to 1,447 ancestry informative markers allowing us to determine the African ancestry proportion of each individual at each position in the genome. Focusing on lipid profiles -- HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), and triglycerides (TG) -- we identified the lipoprotein lipase (LPL) locus as harboring variants that account for interethnic variation in HDL-C and TG. In particular, we identified a novel common variant within LPL that is strongly associated with TG (p = 2.7 x 10(-6)) and explains nearly 1% of the variability in this phenotype, the most of any variant in African Americans to date. Strikingly, the extensively studied "gain-of-function" S447X mutation at LPL, which has been hypothesized to be the major determinant of the LPL-TG genetic association and is in trials for human gene therapy, has a significantly diminished strength of biological effect when it is found on a background of African rather than European ancestry. These results suggest that there are other, yet undiscovered variants at the locus that are truly causal (and are in linkage disequilibrium with S447X) or that work synergistically with S447X to modulate TG levels. Finally, we find systematically lower effect sizes for the 12 risk variants discovered in European populations on the African local ancestry background in JHS, highlighting the need for caution in the use of genetic variants for risk assessment across different populations.