Statistical and Computational Methods for Genetic Diseases: An Overview

A Systematic Approach to Assess the Activity and Classification of PCSK9 Variants

BACKGROUND

Gain of function (GOF) mutations of PCSK9 cause autosomal dominant familial hypercholesterolemia as they reduce the abundance of LDL receptor (LDLR) more efficiently than wild-type PCSK9. In contrast, PCSK9 loss of function (LOF) variants are associated with a hypocholesterolemic phenotype. Dozens of PCSK9 variants have been reported, but most remain of unknown significance since their characterization has not been conducted.

OBJECTIVE

Our aim was to make the most comprehensive assessment of PCSK9 variants and to determine the simplest approach for the classification of these variants.

METHODS

The expression, maturation, secretion, and activity of nine well-established PCSK9 variants were assessed in transiently transfected HEK293 cells by Western blot and flow cytometry. Their extracellular activities were determined in HepG2 cells incubated with the purified recombinant PCSK9 variants. Their binding affinities toward the LDLR were determined by solid-phase immunoassay.

RESULTS

LDLR expression increased when cells were transfected with LOF variants and reduced when cells were transfected with GOF variants compared with wild-type PCSK9. Extracellular activities measurements yielded exactly similar results. GOF and LOF variants had increased, respectively reduced, affinities for the LDLR compared with wild-type PCSK9 with the exception of one GOF variant (R218S) that showed complete resistance to inactivation by furin. All variants were expressed at similar levels and underwent normal maturation and secretion patterns except for two LOF and two GOF mutants.

CONCLUSIONS

We propose that transient transfections of HEK293 cells with a plasmid encoding a PCSK9 variant followed by LDLR expression assessment by flow cytometry is sufficient to reliably determine its GOF or LOF status. More refined experiments should only be used to determine the underlying mechanism(s) at hand.

A Novel CCT5 Missense Variant Associated with Early Onset Motor Neuropathy

Diseases associated with acquired or genetic defects in members of the chaperoning system (CS) are increasingly found and have been collectively termed chaperonopathies. Illustrative instances of genetic chaperonopathies involve the genes for chaperonins of Groups I (e.g., Heat shock protein 60, Hsp60) and II (e.g., Chaperonin Containing T-Complex polypeptide 1, CCT). Examples of the former are hypomyelinating leukodystrophy 4 (HLD4 or MitCHAP60) and hereditary spastic paraplegia (SPG13). A distal sensory mutilating neuropathy has been linked to a mutation [p.(His147Arg)] in subunit 5 of the CCT5 gene. Here, we describe a new possibly pathogenic variant [p.(Leu224Val)] of the same subunit but with a different phenotype. This yet undescribed disease affects a girl with early onset demyelinating neuropathy and a severe motor disability. By whole exome sequencing (WES), we identified a homozygous CCT5 c.670C>G p.(Leu224Val) variant in the CCT5 gene. In silico 3D-structure analysis and bioinformatics indicated that this variant could undergo abnormal conformation and could be pathogenic. We compared the patient’s clinical, neurophysiological and laboratory data with those from patients carrying p.(His147Arg) in the equatorial domain. Our patient presented signs and symptoms absent in the p.(His147Arg) cases. Molecular dynamics simulation and modelling showed that the Leu224Val mutation that occurs in the CCT5 intermediate domain near the apical domain induces a conformational change in the latter. Noteworthy is the striking difference between the phenotypes putatively linked to mutations in the same CCT subunit but located in different structural domains, offering a unique opportunity for elucidating their distinctive roles in health and disease

Data integration by fuzzy similarity-based hierarchical clustering

BACKGROUND

High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data.

RESULTS

In this work we propose a multi-view integration methodology (named FH-Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression, Mirna Expression, Methylation). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity between sets of observations, by using an appropriate metric. For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. Finally, a consensus matrix, that is a representative information of all dendrograms, is formed by combining multiple hierarchical agglomerations by an approach based on transitive consensus matrix construction. Several experiments and comparisons are made on real data (e.g., Glioblastoma, Prostate Cancer) to assess the proposed approach.

CONCLUSIONS

Fuzzy logic allows us to introduce more flexible data agglomeration techniques. From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. The results suggest that FH-Clust provides better prognostic value and clinical significance compared to the analysis of single-omic data alone and it is very competitive with respect to other techniques from literature.

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.

Statistical Methods and Software for Substance Use and Dependence Genetic Research

BACKGROUND

Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

METHODS

Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

CONCLUSION

In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Characterization of two novel pathogenic variants at compound heterozygous status in lipase maturation factor 1 gene causing severe hypertriglyceridemia

BACKGROUND

Severe hypertriglyceridemia is a rare disease characterized by triglyceride levels higher than 1000 mg/dL (11.3 mmol/L) and acute pancreatitis. The disease is caused by pathogenic variants in genes encoding lipoprotein lipase (LPL), apolipoprotein A5, apolipoprotein C2, glycosylphosphatidylinositol-anchored high-density lipoprotein-binding protein 1, and lipase maturation factor 1 (LMF1).

OBJECTIVE

We aim to identify the genetic cause of severe hypertriglyceridemia and characterize the new variants in a patient with severe hypertriglyceridemia.

METHODS

The proband was a male showing severe hypertriglyceridemia (triglycerides 1416 mg/dL, 16.0 mmol/L); proband's relatives were also screened. Genetic screening included direct sequencing of the above genes and identification of large rearrangements in the LPL gene. Functional characterization of mutant LMF1 variants was performed by complementing LPL maturation in transfected LMF1-deficient mouse fibroblasts.

RESULTS

The proband and his affected brother were compound heterozygotes for variants in the LMF1 gene never identified as causative of severe hypertriglyceridemia c.[157delC;1351C>T];[410C>T], p.[(Arg53Glyfs*5)];[(Ser137Leu)]. Functional analysis demonstrated that the p.(Arg53Glyfs*5) truncation completely abolished and the p.(Ser137Leu) missense variant dramatically diminished the lipase maturation activity of LMF1.

CONCLUSIONS

In addition to a novel truncating variant, we describe for the first time a missense variant functionally demonstrated affecting the lipase maturation function of LMF1. This is the first case in which compound heterozygous variants in LMF1 were functionally demonstrated as causative of severe hypertriglyceridemia.

Identification and in vitro characterization of two new PCSK9 Gain of Function variants found in patients with Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal dominant disease caused by pathogenic variants in genes encoding for LDL receptor (LDLR), Apolipoprotein B and Proprotein convertase subtilisin/kexin type 9 (PCSK9). Among PCSK9 variants, only Gain-of- Function (GOF) variants lead to FH. Greater attention should be paid to the classification of variants as pathogenic. Two hundred sixty nine patients with a clinical suspect of FH were screened for variants in LDLR and the patients without pathogenic variants were screened for variants in PCSK9 and APOB. Functional characterization of PCSK9 variants was performed by assessment of protein secretion, of LDLR activity in presence of PCSK9 variant proteins as well as of the LDLR affinity of the PCSK9 variants. Among 81 patients without pathogenic variants in LDLR, 7 PCSK9 heterozygotes were found, 4 of whom were carriers of variants whose role in FH pathogenesis is still unknown. Functional characterization revealed that two variants (p.(Ser636Arg) and p.(Arg357Cys)) were GOF variants. In Conclusions, we demonstrated a GOF effect of 2 PCSK9 variants that can be considered as FH-causative variants. The study highlights the important role played by functional characterization in integrating diagnostic procedures when the pathogenicity of new variants has not been previously demonstrated.

Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk

BACKGROUND

We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.

METHODS

We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.

RESULTS

From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.

CONCLUSIONS

DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.

IMPACT

This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619-24. ©2016 AACR.