Sequencing of Lp-PLA2-encoding PLA2G7 gene in 2000 Europeans reveals several rare loss-of-function mutations

Biological Properties and Clinical Significance of Lipoprotein-Associated Phospholipase A2 in Ischemic Stroke

Ischemic stroke, which occurs following blockage of the blood supply to the brain, is a leading cause of death worldwide. Its main cause is atherosclerosis, a disease of the arteries characterized by the deposition of plaques of fatty material on the inner artery walls. Multiple proteins involved in the inflammation response have been identified as diagnosing biomarkers of ischemic stroke. One of these is lipoprotein-associated phospholipase A2 (Lp-PLA2), an enzyme that can hydrolyze circulating oxidized phospholipids, generating proinflammatory lysophosphatidylcholine and promoting the development of atherosclerosis. In the last two decades, a number of studies have revealed that both the concentration and the activity of Lp-PLA2 are independent biomarkers of ischemic stroke. The US Food and Drug Administration (FDA) has approved two tests to determine Lp-PLA2 mass and activity for predicting stroke. In this review, we summarize the biological properties of Lp-PLA2, the detection sensitivity and limitations of Lp-PLA2 measurement, the clinical significance and association of Lp-PLA2 in ischemic stroke, and the prospects of therapeutic inhibition of Lp-PLA2 as an intervention and treatment.

The changes of Lp-PLA2 in patients with gestational diabetes and its clinical significance

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy and associated with adverse pregnancy outcomes. The aim of this study was to investigate the changes of lipoprotein-associated phospholipaseA2 (Lp-PLA2) level and its correlation with biochemical indexes in patients with GDM.This observational cross-sectional study was performed among 52 GDM and 48 healthy pregnant women. Automatic biochemical analyzer was employed to test the biochemical indexes, including fasting plasma glucose (FPG), Hemoglobin A1c (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). The lipoprotein-associated phospholipaseA2 (Lp-PLA2) level was evaluated by enzyme-linked immunosorbent assay, and homeostatic model assessment for insulin resistance (HOMA-IR) was calculated.The levels of FPG, HbA1c, HOMA-IR, TG, TC and LDL-C were significantly increased while high-density lipoprotein cholesterol (HDL-C) level was significantly decreased in the GDM group when compared with those in the control group. Lp-PLA2 level in maternal blood in the GDM group was significantly higher than that in the control group (199.125 ± 23.494 vs165.825 ± 15.576 ng/mL, P < .05) and logistic regression analysis further confirmed the association of Lp-PLA2 levels with GDM. Furthermore, Lp-PLA2 positively correlated with HOMA-IR, TC, and LDL-C.Our results confirmed the association of Lp-PLA2 with GDM. This broadens our knowledge on the pathophysiology of GDM and provides insights into the development of new targets for the prevention and treatment of GDM.

The population genomics of adaptive loss of function

Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are revealing extensive loss-of-function and gene content variation, yet the adaptive significance of much of this variation remains unknown. Here we examine the evolutionary dynamics of adaptive loss of function through the lens of population genomics and consider the challenges and opportunities of studying adaptive loss-of-function alleles using population genetics models. We discuss how the theoretically expected existence of allelic heterogeneity, defined as multiple functionally analogous mutations at the same locus, has proven consistent with empirical evidence and why this impedes both the detection of selection and causal relationships with phenotypes. We then review technical progress towards new functionally explicit population genomic tools and genotype-phenotype methods to overcome these limitations. More broadly, we discuss how the challenges of studying adaptive loss of function highlight the value of classifying genomic variation in a way consistent with the functional concept of an allele from classical population genetics.

Lipoprotein-associated phospholipase A2: The story continues

Inflammation is thought to play an important role in the pathogenesis of vascular diseases. Lipoprotein-associated phospholipase A2 (Lp-PLA2) mediates vascular inflammation through the regulation of lipid metabolism in blood, thus, it has been extensively investigated to identify its role in vascular inflammation-related diseases, mainly atherosclerosis. Although darapladib, the most advanced Lp-PLA2 inhibitor, failed to meet the primary endpoints of two large phase III trials in atherosclerosis patients cotreated with standard medical care, the research on Lp-PLA2 has not been terminated. Novel pathogenic, epidemiologic, genetic, and crystallographic studies regarding Lp-PLA2 have been reported recently, while novel inhibitors were identified through a fragment-based lead discovery strategy. More strikingly, recent clinical and preclinical studies revealed that Lp-PLA2 inhibition showed promising therapeutic effects in diabetic macular edema and Alzheimer's disease. In this review, we not only summarized the knowledge of Lp-PLA2 established in the past decades but also emphasized new findings in recent years. We hope this review could be valuable for helping researchers acquire a much deeper insight into the nature of Lp-PLA2, identify more potent and selective Lp-PLA2 inhibitors, and discover the potential indications of Lp-PLA2 inhibitors.

Identifying individual risk rare variants using protein structure guided local tests (POINT)

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.

DCC/NTN1 complex mutations in patients with congenital hypogonadotropic hypogonadism impair GnRH neuron development

Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disease characterized by absent puberty and infertility due to GnRH deficiency, and is often associated with anosmia [Kallmann syndrome (KS)]. The genetic etiology of CHH is heterogeneous, and more than 30 genes have been implicated in approximately 50% of patients with CHH. We hypothesized that genes encoding axon-guidance proteins containing fibronectin type-III (FN3) domains (similar to ANOS1, the first gene associated with KS), are mutated in CHH. We performed whole-exome sequencing in a cohort of 133 CHH probands to test this hypothesis, and identified rare sequence variants (RSVs) in genes encoding for the FN3-domain encoding protein deleted in colorectal cancer (DCC) and its ligand Netrin-1 (NTN1). In vitro studies of these RSVs revealed altered intracellular signaling associated with defects in cell morphology, and confirmed five heterozygous DCC mutations in 6 probands-5 of which presented as KS. Two KS probands carry heterozygous mutations in both DCC and NTN1 consistent with oligogenic inheritance. Further, we show that Netrin-1 promotes migration in immortalized GnRH neurons (GN11 cells). This study implicates DCC and NTN1 mutations in the pathophysiology of CHH consistent with the role of these two genes in the ontogeny of GnRH neurons in mice.

Effect of lipoprotein-associated phospholipase A2 inhibitor on insulin resistance in streptozotocin-induced diabetic pregnant rats

This paper aims to investigate the influence of lipoprotein-associated phospholipase A2 (Lp-PLA2) inhibitor, darapladib, on insulin resistance (IR) in streptozotocin (STZ)-induced diabetic pregnant rats. The rat models were divided into Control (normal pregnancy), STZ + saline (STZ-induced diabetic pregnant rats), STZ + Low-dose and STZ + High-dose darapladib (STZ-induced diabetic pregnant rats treated with low-/high-dose darapladib) groups. Pathological changes were observed by Hematoxylin-eosin (HE) and Immunohistochemistry staining. Lp-PLA2 levels were determined by enzyme-linked immunosorbent assay (ELISA). An automatic biochemical analyzer was used to measure the serum levels of biochemical indicators, and homeostatic model assessment for insulin resistance (HOMA-IR) and insulin sensitivity index (ISI) were calculated. Western blot was applied to determine levels of inflammatory cytokines. Compared with Control group, rats in the STZ + saline group were significantly decreased in body weight, the number of embryo implantation, the number of insulin positive cells and pancreatic islet size as well as the islet endocrine cells, and high-density lipoprotein (HDL-C) level, but substantially increased in Lp-PLA2, low-density lipoprotein (LDL-C), fatty acids (FFA), serum total cholesterol (TC), triglyceride (TG) levels. Moreover, the increased fasting plasma glucose (FPG) and HOMA-IR and inflammatory cytokines but decreased fasting insulin (FINS) and ISI were also found in diabetic pregnant rats. On the contrary, rats in the darapladib-treated groups were just opposite to the STZ + saline group, and STZ + High-dose group improved better than STZ + Low-dose group. Thus, darapladib can improve lipid metabolism, and enhance insulin sensitivity of diabetic pregnant rats by regulating inflammatory cytokines.

Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples

A properly designed distance-based measure can capture informative genetic differences among individuals with different phenotypes and can be used to detect variants responsible for the phenotypes. To detect associated variants, various tests have been designed to contrast genetic dissimilarity or similarity scores of certain subject groups in different ways, among which the most widely used strategy is to quantify the difference between the within-group genetic dissimilarity/similarity (i.e., case-case and control-control similarities) and the between-group dissimilarity/similarity (i.e., case-control similarities). While it has been noted that for common variants, the within-group and the between-group measures should all be included; in this work, we show that for rare variants, comparison based on the two within-group measures can more effectively quantify the genetic difference between cases and controls. The between-group measure tends to overlap with one of the two within-group measures for rare variants, although such overlap is not present for common variants. Consequently, a dissimilarity or similarity test that includes the between-group information tends to attenuate the association signals and leads to power loss. Based on these findings, we propose a dissimilarity test that compares the degree of SNP dissimilarity within cases to that within controls to better characterize the difference between two disease phenotypes. We provide the statistical properties, asymptotic distribution, and computation details for a small sample size of the proposed test. We use simulated and real sequence data to assess the performance of the proposed test, comparing it with other rare-variant methods including those similarity-based tests that use both within-group and between-group information. As similarity-based approaches serve as one of the dominating approaches in rare-variant analysis, our results provide some insight for the effective detection of rare variants.

On the substructure controls in rare variant analysis: Principal components or variance components?

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

Genetic invalidation of Lp-PLA2 as a therapeutic target: Large-scale study of five functional Lp-PLA2-lowering alleles

Aims Darapladib, a potent inhibitor of lipoprotein-associated phospholipase A2 (Lp-PLA2), has not reduced risk of cardiovascular disease outcomes in recent randomized trials. We aimed to test whether Lp-PLA2 enzyme activity is causally relevant to coronary heart disease. Methods In 72,657 patients with coronary heart disease and 110,218 controls in 23 epidemiological studies, we genotyped five functional variants: four rare loss-of-function mutations (c.109+2T > C (rs142974898), Arg82His (rs144983904), Val279Phe (rs76863441), Gln287Ter (rs140020965)) and one common modest-impact variant (Val379Ala (rs1051931)) in PLA2G7, the gene encoding Lp-PLA2. We supplemented de-novo genotyping with information on a further 45,823 coronary heart disease patients and 88,680 controls in publicly available databases and other previous studies. We conducted a systematic review of randomized trials to compare effects of darapladib treatment on soluble Lp-PLA2 activity, conventional cardiovascular risk factors, and coronary heart disease risk with corresponding effects of Lp-PLA2-lowering alleles. Results Lp-PLA2 activity was decreased by 64% ( p = 2.4 × 10-25) with carriage of any of the four loss-of-function variants, by 45% ( p < 10-300) for every allele inherited at Val279Phe, and by 2.7% ( p = 1.9 × 10-12) for every allele inherited at Val379Ala. Darapladib 160 mg once-daily reduced Lp-PLA2 activity by 65% ( p < 10-300). Causal risk ratios for coronary heart disease per 65% lower Lp-PLA2 activity were: 0.95 (0.88-1.03) with Val279Phe; 0.92 (0.74-1.16) with carriage of any loss-of-function variant; 1.01 (0.68-1.51) with Val379Ala; and 0.95 (0.89-1.02) with darapladib treatment. Conclusions In a large-scale human genetic study, none of a series of Lp-PLA2-lowering alleles was related to coronary heart disease risk, suggesting that Lp-PLA2 is unlikely to be a causal risk factor.

Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.

Next-generation sequencing technologies have allowed genetic association studies of complex traits at the single base-pair resolution, where most genetic variants have extremely low mutation frequencies. These rare variants have been the focus of modern statistical-computational genomics due to their potential to explain missing disease heritability. The identification of individual rare variants associated with diseases can provide new biological insights and enable the precise delineation of disease mechanisms. However, due to the extreme rarity of mutations and large numbers of variants, significances of causative variants tend to be mixed inseparably with a few noncausative ones, and standard multiple testing procedures controlling for false positives fail to provide a meaningful way to include a large proportion of the causative variants. To address the challenge of detecting weak biological signals, we propose a novel statistical procedure, based on false-negative control, to provide a practical approach for variant inclusion in large-scale sequencing studies. By determining those variants that can be confidently dispatched as noncausative, the proposed procedure offers an objective selection of a modest number of potentially causative variants at the single-locus level. Results can be further prioritized or used to infer disease-associated genes with annotation information.

Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations

Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.

Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors

Studying complex diseases in the post genome-wide association studies (GWAS) era has led to developing methods that consider factor-sets rather than individual genetic/environmental factors (i.e., Multi-G-Multi-E studies), and mining for potential gene-environment (G×E) interactions has proven to be an invaluable aid in both discovery and deciphering underlying biological mechanisms. Current approaches for examining effect profiles in Multi-G-Multi-E analyses are either underpowered due to large degrees of freedom, ill-suited for detecting G×E interactions due to imprecise modeling of the G and E effects, or lack of capacity for modeling interactions between two factor-sets (e.g., existing methods focus primarily on a single E factor). In this work, we illustrate the issues encountered in constructing kernels for investigating interactions between two factor-sets, and propose a simple yet intuitive solution to construct the G×E kernel that retains the ease-of-interpretation of classic regression. We also construct a series of kernel machine (KM) score tests to evaluate the complete effect profile (i.e., the G, E, and G×E effects individually or in combination). We show, via simulations and a data application, that the proposed KM methods outperform the classic and PC regressions across a range of scenarios, including varying effect size, effect structure, and interaction complexity. The largest power gain was observed when the underlying effect structure involved complex G×E interactions; however, the proposed methods have consistent, powerful performance when the effect profile is simple or complex, suggesting that the proposed method could be a useful tool for exploratory or confirmatory G×E analysis.

STABILITY and SOLID-TIMI 52: Lipoprotein associated phospholipase A2 (Lp-PLA2) as a biomarker or risk factor for cardiovascular diseases

Lipoprotein associated phospholipase A2 (Lp-PLA₂) - an enzyme with several pro-inflammatory properties - has been hypothesized to be involved in the pathogenesis of atherosclerosis and plaque vulnerability. Lp-PLA₂ activity has been demonstrated to be an independent predictor of coronary heart disease (CHD) and ischemic stroke. However, it has been recently reported that carriers of loss of function variants in PLA2G7 gene (encoding Lp-PLA2) had no lower CHD risk than non-carriers. The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy (STABILITY), and the Stabilization of Plaque Using Darapladib - Thrombolysis in Myocardial Infarction 52 (SOLID-TIMI 52) studies are two phase III, randomized, placebo-controlled, clinical trials that were conducted to evaluate the clinical efficacy and safety of the Lp-PLA2 inhibitor (darapladib), against a background of optimal medical therapy, in patients with stable CHD and acute coronary syndrome, respectively. In both studies, darapladib failed to reduce the risk of major coronary events as compared to placebo. In addition, darapladib was associated with significantly higher rates of drug discontinuation, and adverse side effects such as diahrrea and malodorous feces, urine, and skin, as compared to placebo. These data suggest that Lp-PLA₂ may be a biomarker of vascular inflammation rather than a causal pathway of cardiovascular (CV) diseases. It also challenges the notion that inhibition of Lp-PLA₂ is a worthwhile approach in patients with CHD. Alternate therapies that target inflammation are awaited to reduce residual risk in patients with CV diseases.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

Testing for modes of inheritance involving compound heterozygotes

Functional variants change the protein product or the expression of genes. Due to the latest advances in sequencing technology, most known functional variants can now be assayed in a cost-effective manner. However, to fully use the information from functional variants, researchers need to model the joint effect of these variants. In this article, we propose methods that model the action/interaction of loss-of-function (LOF) mutations, i.e., those mutations that eliminate the protein product of a gene. When multiple LOFs occur in the same causal gene/region, their effect on a phenotype might depend on whether these mutations lie on the same DNA strand/haplotype. When compared to LOFs occurring on the same strand, if these mutations lie on different strands, both copies of the gene are impaired and the impact on the relevant phenotypes is likely to be more severe. To use the information from LOF strand colocalization, we propose three methods that utilize the information from the estimated number of affected strands. We compare the performance of the proposed and competing methods by using simulations of common and rare LOF variants. Two of the proposed methods exhibited desirable power profiles, the first for both common and rare LOFs and the second only for common LOFs. One of the existing methods, collapsed double heterozygosity, exhibits good power to detect compound models for rare variants, especially when no haplotype harbors two or more rare alleles. Consequently, we recommend these three methods to be used for the analysis of functional variants coming from sequencing studies.

Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals

The optimal study design for identifying rare variants associated with common disease is not yet clear and researchers have to decide whether to prioritize lower sequencing coverage on larger sample sizes, or higher coverage on smaller sample sizes. High-coverage sequencing affords several advantages, such as genotype accuracy and improved identification of very rare variants, but this comes at increased cost. However, the magnitude of the contribution of very rare variants to the statistical power of gene-based association tests is unknown. By using Sanger sequence data on seven genes from 1998 subjects with simulated phenotypes, we provide evidence that excluding very rare variants, in general, reduces the statistical power of rare variant association tests only modestly. However, if the probability of being causal and the effect size of the causal variants are inversely related to the minor allele frequency, then very rare variants do contribute to some power, however the absolute power remains low. As very rare variants constitute the majority of variants identified in sequencing studies, these findings suggest that careful attention need to be placed on the plausible relationship that exist between very rare variants and common disease.

Multiple regression methods show great potential for rare variant association tests

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.

Modulation of oxidative stress, inflammation, and atherosclerosis by lipoprotein-associated phospholipase A2

Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)), also known as platelet-activating factor acetylhydrolase (PAF-AH), is a unique member of the phospholipase A(2) superfamily. This enzyme is characterized by its ability to specifically hydrolyze PAF as well as glycerophospholipids containing short, truncated, and/or oxidized fatty acyl groups at the sn-2 position of the glycerol backbone. In humans, Lp-PLA(2) circulates in active form as a complex with low- and high-density lipoproteins. Clinical studies have reported that plasma Lp-PLA(2) activity and mass are strongly associated with atherogenic lipids and vascular risk. These observations led to the hypothesis that Lp-PLA(2) activity and/or mass levels could be used as biomarkers of cardiovascular disease and that inhibition of the activity could offer an attractive therapeutic strategy. Darapladib, a compound that inhibits Lp-PLA(2) activity, is anti-atherogenic in mice and other animals, and it decreases atherosclerotic plaque expansion in humans. However, disagreement continues to exist regarding the validity of Lp-PLA(2) as an independent marker of atherosclerosis and a scientifically justified target for intervention. Circulating Lp-PLA(2) mass and activity are associated with vascular risk, but the strength of the association is reduced after adjustment for basal concentrations of the lipoprotein carriers with which the enzyme associates. Genetic studies in humans harboring an inactivating mutation at this locus indicate that loss of Lp-PLA(2) function is a risk factor for inflammatory and vascular conditions in Japanese cohorts. Consistently, overexpression of Lp-PLA(2) has anti-inflammatory and anti-atherogenic properties in animal models. This thematic review critically discusses results from laboratory and animal studies, analyzes genetic evidence, reviews clinical work demonstrating associations between Lp-PLA(2) and vascular disease, and summarizes results from animal and human clinical trials in which administration of darapladib was tested as a strategy for the management of atherosclerosis.

Biallelic mutations in PLA2G5, encoding group V phospholipase A2, cause benign fleck retina

Flecked-retina syndromes, including fundus flavimaculatus, fundus albipunctatus, and benign fleck retina, comprise a group of disorders with widespread or limited distribution of yellow-white retinal lesions of various sizes and configurations. Three siblings who have benign fleck retina and were born to consanguineous parents are the basis of this report. A combination of homozygosity mapping and exome sequencing helped to identify a homozygous missense mutation, c.133G>T (p.Gly45Cys), in PLA2G5, a gene encoding a secreted phospholipase (group V phospholipase A(2)). A screen of a further four unrelated individuals with benign fleck retina detected biallelic variants in the same gene in three patients. In contrast, no loss of function or common (minor-allele frequency>0.05%) nonsynonymous PLA2G5 variants have been previously reported (EVS, dbSNP, 1000 Genomes Project) or were detected in an internal database of 224 exomes (from subjects with adult onset neurodegenerative disease and without a diagnosis of ophthalmic disease). All seven affected individuals had fundoscopic features compatible with those previously described in benign fleck retina and no visual or electrophysiological deficits. No medical history of major illness was reported. Levels of low-density lipoprotein were mildly elevated in two patients. Optical coherence tomography and fundus autofluorescence findings suggest that group V phospholipase A(2) plays a role in the phagocytosis of photoreceptor outer-segment discs by the retinal pigment epithelium. Surprisingly, immunohistochemical staining of human retinal tissue revealed localization of the protein predominantly in the inner and outer plexiform layers.

Detecting low frequent loss-of-function alleles in genome wide association studies with red hair color as example

Multiple loss-of-function (LOF) alleles at the same gene may influence a phenotype not only in the homozygote state when alleles are considered individually, but also in the compound heterozygote (CH) state. Such LOF alleles typically have low frequencies and moderate to large effects. Detecting such variants is of interest to the genetics community, and relevant statistical methods for detecting and quantifying their effects are sorely needed. We present a collapsed double heterozygosity (CDH) test to detect the presence of multiple LOF alleles at a gene. When causal SNPs are available, which may be the case in next generation genome sequencing studies, this CDH test has overwhelmingly higher power than single SNP analysis. When causal SNPs are not directly available such as in current GWA settings, we show the CDH test has higher power than standard single SNP analysis if tagging SNPs are in linkage disequilibrium with the underlying causal SNPs to at least a moderate degree (r²>0.1). The test is implemented for genome-wide analysis in the publically available software package GenABEL which is based on a sliding window approach. We provide the proof of principle by conducting a genome-wide CDH analysis of red hair color, a trait known to be influenced by multiple loss-of-function alleles, in a total of 7,732 Dutch individuals with hair color ascertained. The association signals at the MC1R gene locus from CDH were uniformly more significant than traditional GWA analyses (the most significant P for CDH = 3.11×10⁻¹⁴² vs. P for rs258322 = 1.33×10⁻⁶⁶). The CDH test will contribute towards finding rare LOF variants in GWAS and sequencing studies.