Investigation of Single Nucleotide Polymorphisms Associated to Familial Combined Hyperlipidemia with Random Forests. In: Apolloni B, Bassis S, Esposito A, Morabito FC, editors. Neural Nets and Surroundings. Berlin: Springer Berlin Heidelberg; 2013. pp. 169-78. [DOI: 10.1007/978-3-642-35467-0_18]

Impact of 12-SNP and 6-SNP Polygenic Scores on Predisposition to High LDL-Cholesterol Levels in Patients with Familial Hypercholesterolemia

BACKGROUND

Familial hypercholesterolemia (FH) comprises high LDL-cholesterol (LDL-c) levels and high cardiovascular disease risk. In the absence of pathogenic variants in causative genes, a polygenic basis was hypothesized.

METHODS

In a population of 418 patients (excluding homozygotes) with clinical suspicion of FH, the FH-causative genes and the regions of single nucleotide polymorphisms (SNPs) included in 12-SNP and 6-SNP scores were sequenced by next-generation sequencing, allowing for the detection of pathogenic variants (V+) in 220 patients. To make a comparison, only patients without uncertain significance variants (V-/USV-) were considered (n = 162).

RESULTS

Higher values of both scores were observed in V+ than in V-. Considering a cut-off leading to 80% of V-/USV- as score-positive, a lower prevalence of patients positive for both 12-SNP and 6-SNP scores was observed in V+ (p = 0.010 and 0.033, respectively). Mainly for the 12-SNP score, among V+ patients, higher LDL-c levels were observed in score-positive (223 mg/dL -IQR 187-279) than in negative patients (212 mg/dL -IQR 162-240; p = 0.006). Multivariate analysis confirmed the association of scores and LDL-c levels independently of age, sex, and presence of pathogenic variants and revealed a greater association in children.

CONCLUSIONS

The 12-SNP and 6-SNP polygenic scores could explain hypercholesterolemia in patients without pathogenic variants as well as the variability of LDL-c levels among patients with FH-causative variants.

Genetic Heterogeneity of Familial Hypercholesterolemia: Repercussions for Molecular Diagnosis

Genetics of Familial Hypercholesterolemia (FH) is ascribable to pathogenic variants in genes encoding proteins leading to an impaired LDL uptake by the LDL receptor (LDLR). Two forms of the disease are possible, heterozygous (HeFH) and homozygous (HoFH), caused by one or two pathogenic variants, respectively, in the three main genes that are responsible for the autosomal dominant disease: LDLR, APOB and PCSK9 genes. The HeFH is the most common genetic disease in humans, being the prevalence about 1:300. Variants in the LDLRAP1 gene causes FH with a recessive inheritance and a specific APOE variant was described as causative of FH, contributing to increase FH genetic heterogeneity. In addition, variants in genes causing other dyslipidemias showing phenotypes overlapping with FH may mimic FH in patients without causative variants (FH-phenocopies; ABCG5, ABCG8, CYP27A1 and LIPA genes) or act as phenotype modifiers in patients with a pathogenic variant in a causative gene. The presence of several common variants was also considered a genetic basis of FH and several polygenic risk scores (PRS) have been described. The presence of a variant in modifier genes or high PRS in HeFH further exacerbates the phenotype, partially justifying its variability among patients. This review aims to report the updates on the genetic and molecular bases of FH with their implication for molecular diagnosis.

Genetic spectrum of familial hypercholesterolemia and correlations with clinical expression: Implications for diagnosis improvement

Familial hypercholesterolemia (FH) is the most common genetic disease caused by variants in LDLR, APOB, PCSK9 genes; it is characterized by high levels of LDL-cholesterol and premature cardiovascular disease. We aim to perform a retrospective analysis of a genetically screened population (528 unrelated patients-342 adults and 186 children) to evaluate the biochemical and clinical correlations with the different genetic statuses. Genetic screening was performed by traditional sequencing and some patients were re-analyzed by next-generation-sequencing. Pathogenic variants, mainly missense in the LDLR gene, were identified in 402/528 patients (76.1%), including 4 homozygotes, 17 compound heterozygotes and 1 double heterozygotes. A gradual increase of LDL-cholesterol was observed from patients without pathogenic variants to patients with a defective variant, to patients with a null variant and to patients with two variants. Six variants accounted for 51% of patients; a large variability of LDL-cholesterol was observed among patients carrying the same variant. The frequency of pathogenic variants gradually increased from unlikely FH to definite FH, according to the Dutch Lipid Clinic Network criteria. Genetic diagnosis can help prognostic evaluation of FH patients, discriminating between the different genetic statuses or variant types. Clinical suspicion of FH should be considered even if few symptoms are present or if LDL-cholesterol is only mildly increased.

Endothelial function improvement in patients with familial hypercholesterolemia receiving PCSK-9 inhibitors on top of maximally tolerated lipid lowering therapy

BACKGROUND

Treatment with protein convertase subtilisin kexin type 9 inhibitors (PCSK-9i) reduced cholesterol levels and cardiovascular events in patients with hypercholesterolemia. We assessed changes in lipid profile, oxidation markers and endothelial function in patients with familial hypercholesterolemia (FH) after a 12-week treatment with a PCSK-9i.

METHODS

Patients with FH starting a treatment with PCSK-9i were included. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), small dense LDL (assessed by LDL score), 11-dehydro-thromboxane (11-TXB2), 8-isoprostaglandin-2alpha (8-iso-PGF2α), flow-mediated dilation (FMD) and reactive hyperaemia index (RHI) were evaluated before starting PCSK-9i treatment and after a 12-week treatment.

RESULTS

Twenty-five subjects were enrolled (52% males, mean age 51.5 years). At the 12-week assessment, we observed a 38% median reduction in TC, 52% in LDL-C, 7% in Lp(a) and 46% in LDL score. In parallel, 11-TXB2 and 8-iso-PGF2α showed a reduction of 18% and 17%, respectively. FMD changed from 4.78% ± 2.27 at baseline to 10.6% ± 5.89 at 12 weeks (p < 0.001), with RHI changing from 2.37 ± 1.23 to 3.76 ± 1.36 (p < 0.001). A multivariate analysis showed that, after adjusting for potential confounders, change in LDL score was an independent predictor of changes in FMD (β = -0.846, p = 0.015) and in 8-iso-PGF2α (β = 0.778, p = 0.012).

CONCLUSIONS

Small dense LDL reduction (assessed by LDL score) is related to changes in oxidation markers and endothelial function in patients with FH treated with PCSK-9i.

Changes in carotid stiffness in patients with familial hypercholesterolemia treated with Evolocumab®: A prospective cohort study

BACKGROUND AND AIM

Protein convertase subtilisin kexin type 9 (PCSK-9) inhibitors demonstrated efficacy in cholesterol reduction and in the prevention of cardiovascular events. We evaluated changes in lipid profile and carotid stiffness in patients with familial hypercholesterolemia during 12 weeks of treatment with a PCSK-9 inhibitor, Evolocumab®.

METHODS AND RESULTS

Patients with familial hypercholesterolemia starting a treatment with Evolocumab® were included. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), small dense LDL (assessed by LDL score) and carotid stiffness were evaluated before starting treatment with Evolocumab® and during 12 weeks of treatment. Twenty-five subjects were enrolled (52% males, mean age 51.5 years). TC and LDL-C were reduced of 38% and 52%, respectively during treatment, with LDL score reduced of 46.1%. In parallel, carotid stiffness changed from 8.8 (IQR: 7.0-10.4) m/sec to 6.6 (IQR: 5.4-7.5) m/sec, corresponding to a median change of 21.4% (p < 0.001), with a significant increase in carotid distensibility (from 12.1, IQR: 8.73-19.3 kPA^-1 × 10^-3 at T₀ to 21.8, IQR: 16.6-31.8 kPA^-1 × 10^-3 at T12_w) corresponding to a median change of 62.8% (p < 0.001). A multivariate analysis showed that changes in LDL score were independently associated with changes in carotid stiffness (β = 0.429, p = 0.041).

CONCLUSION

Small dense LDL reduction, as assessed by LDL score, is associated with changes in carotid stiffness in patients with familial hypercholesterolemia treated with Evolocumab®.

A Sparse-Modeling Based Approach for Class Specific Feature Selection

In this work, we propose a novel Feature Selection framework called Sparse-Modeling Based Approach for Class Specific Feature Selection (SMBA-CSFS), that simultaneously exploits the idea of Sparse Modeling and Class-Specific Feature Selection. Feature selection plays a key role in several fields (e.g., computational biology), making it possible to treat models with fewer variables which, in turn, are easier to explain, by providing valuable insights on the importance of their role, and likely speeding up the experimental validation. Unfortunately, also corroborated by the no free lunch theorems, none of the approaches in literature is the most apt to detect the optimal feature subset for building a final model, thus it still represents a challenge. The proposed feature selection procedure conceives a two-step approach: (a) a sparse modeling-based learning technique is first used to find the best subset of features, for each class of a training set; (b) the discovered feature subsets are then fed to a class-specific feature selection scheme, in order to assess the effectiveness of the selected features in classification tasks. To this end, an ensemble of classifiers is built, where each classifier is trained on its own feature subset discovered in the previous phase, and a proper decision rule is adopted to compute the ensemble responses. In order to evaluate the performance of the proposed method, extensive experiments have been performed on publicly available datasets, in particular belonging to the computational biology field where feature selection is indispensable: the acute lymphoblastic leukemia and acute myeloid leukemia, the human carcinomas, the human lung carcinomas, the diffuse large B-cell lymphoma, and the malignant glioma. SMBA-CSFS is able to identify/retrieve the most representative features that maximize the classification accuracy. With top 20 and 80 features, SMBA-CSFS exhibits a promising performance when compared to its competitors from literature, on all considered datasets, especially those with a higher number of features. Experiments show that the proposed approach may outperform the state-of-the-art methods when the number of features is high. For this reason, the introduced approach proposes itself for selection and classification of data with a large number of features and classes.

A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data

In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.

Statistical and Computational Methods for Genetic Diseases: An Overview

The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases.