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International Journal of Forensic Sciences Research Article 18 min read

Retrospective Molecular Analysis in Five Cases Probably Associated with Sudden Death the National Institute of Legal Medicine and Forensic Sciences of Colombia

Alape Ariza J*, Medina Rocha AH, Cabrera Pérez R and Bermúdez- Santana CI
* Corresponding author
ISSN: 2573-1734  10.23880/ijfsc-16000304  Received: March 09, 2023  Published: May 11, 2023
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Keywords
SCD Myocardiopathies Channelopathies NGS
Abstract

In recent years, significant advances have been made in understanding the genetic factors that predispose to sudden cardiac death, finding multiple affected genes that cause arrhythmic disorders, which could trigger sudden death in structurally normal hearts, to determine these genetic variants. It has an important role as a complement in autopsies of deaths to be determined. Cases and controls were analyzed through the study of the exome by next-generation sequencing. The variants were filtered following international recommendations, different software was used to determine the possible variants associated with cardiomyopathies and cardiac channelopathies. Twelve structural variants were found in six genes associated with different types of cardiomyopathies. Seven variants were found in six genes associated with cardiac channelopathies, and additionally, twenty-one variants were found in twelve genes of uncertain significance, four variants may be of clinical relevance. In deaths whose causes remain to be determined after performing the autopsy and considering negative toxicological, virologic, and microscopy results, it is extremely important to carry out a molecular analysis because the cause of death is possibly due to a channelopathy or an arrhythmia that is difficult to detect during the autopsy.

Introduction

The from a forensic point of view, sudden death is mainly defined as a quick, unexpected and natural death. When confronted with the study of a case of sudden cardiac death in an adult or in a child over one year of age, the pathologist generally places the case in one of the following three categories in order of frequency: 1) ischemic heart disease, 2) conditions or diseases grossly associated with sudden death, and 3) hearts that are normal at least on gross examination [1].

Heart disease is the most frequent cause of sudden death and, in turn, of these ischemic heart diseases in adults. Non-ischemic causes are, for example, respiratory diseases (pulmonary embolism and asthma), neurological diseases (cerebral hemorrhage and epilepsy). Even so, no cause of death is determined after a thorough post-mortem examination in approximately 5% of cases [2].

The genetic diagnosis of SCD is very complex, since it is still not easy to identify the causal pathogenic variant, since there are many genes involved, as in the case of channelopathies that present a Mendelian pattern of inheritance with different degrees of penetrance and variable expressivity of the disease [3], turning them into complex diseases, favoring the advancement of molecular tests that can understand the breadth and depth of heart diseases. These discoveries have directly affected the approach to snapshot autopsies by pathologists [4].

Molecular diagnosis has become especially useful in the investigation of SCD, not only to elucidate the cause of death, but also to identify risk factors because this type of disease has a family basis, of genetic origin. That can be monogenic and autosomal dominant, recessive, sex-linked, etc., and the autopsy can be the only possibility that a family can be referred to a cardiology hospital to receive adequate genetic counseling [5, 6, 7].

Studies to date have shown that ion channels can function as part of large complexes of macromolecules that play crucial roles in the transcription, translation, post- translational modification, degradation of all cardiac ion channels, among others [8, 9, 10].

Therefore, understanding the structure and dynamic signaling of multiprotein assemblies is vital to understanding heart function during disease processes [11], and exome analysis can help elucidating some of these aspects that can modify domains in proteins can cause certain pathogenicity.

For this, the hypothesis will be handled: Given the high genetic addition that the Colombian population presents, it is possible to find new gene switches of the ion channels and that may be associated with the events of MSC. Our objective was to identify groups of genes that are possibly associated with sudden cardiac death, through the analysis of five people who died due to sudden death with a negative toxicological result, for which the TruSight One panel (clinical exome) was obtained

Materials and Methods

Subjects

Five (5) cases associated with sudden cardiac death and five (5) controls were selected. The corresponding review of the autopsy protocols was carried out, considering the circumstances of death, clinical history, macro and microscopic studies, toxicology and virology analysis as appropriate, during the autopsy.

Inclusion and Exclusion Criteria

Negative cases in structural coronary heart disease, with microscopically normal cardiomyocytes, negative for toxicology and virology, under 40 years of age. Controls whose death was caused by violent death, excluding those by suicide.

DNA Extraction

Total DNA extraction was performed from blood that was in the evidence center of the National Institute of Legal Medicine and Forensic Sciences, using the QIAamp® DNA Blood Midi/Maxi kit, following the manufacturer’s recommendations.

Preparation of Libraries

Sample library preparation was performed with the TruSight One Sequencing Panel Series (Illumina), which includes 4834 clinically relevant genes, using the Nextera XT Kit (Illumina). The quantification of the libraries was carried out using the Quantitating dsDNA Kit using the Quantus™

Fluorometer Instrument and following the manufacturer’s recommendations.

NGS Sequencing

Paired-end NGS sequencing of 2x150 bp with the MiSeq kit (Illumina Inc., San Diego, CA. USA) using MiSeq Reagent V3 (150 cycles), according to the protocol of the commercial house, was performed. The alignment of DNA readings was performed against the reference genome GRCH37/hg19.

Variant Annotation

The detected variants were annotated using the bioinformatic tools SnpEff [12, 13, 14], which integrate the population databases: dbSNP [15], of the 1000 genomes project (1000 Genomes Project Consortium), the NHLBI Exome Sequencing Project (ESP) [16], database [17], and the gnomAD and exomAD databases (http://gnomad. broadinstitute.org). Variants with a MAF of 0.01 were filtered out. Information related to the association between human phenotype and causative genes was added with ClinVar [18, 19]. In silico predictive algorithms were included: SIFT [20], PolyPhen-2 [21], MutationTaster [22], LRT [23], Mutation Assesor [24], FATHMM (Functional Analysis Through Hidden Markov Models) [25], MetaSVM [26], RadialSVM, LR, VEST3, CADD, GERP++ [27, 28, 29], to assess the pathogenicity of the identified variants. Determining the minor allele frequency (MAF) to filter variants. The norms and guidelines for the interpretation of sequence variants suggested by the American College of Medical Genetics and Genomics (ACMG) were followed for the classification of causality of each of the variants [30].

Cases Gene rsID Func Genetic Variant Change AA zygosity MAF

TRDN rs372169818 exon2 c.G196A p.V66I het 0,04971 0.00019968 0,06909 Absent

TTN rs746749916 exon154 c.G48780T p.W16260C het 0,008302 Absent 0,01633 Absent

I

CAV3 rs753990961 exon2 c.A185G p.Y62C hom 0,008264 Absent 0,004063 Absent

I, II DSP rs78652302 exon24 c.A3701T p.E1234V het 0.0095 0.00399361 0.0095 0.0112 II TTN rs776534823 exon27 c.G5905A p.A1969T het 0,008243 Absent 0,008139 3.23E-05 TTN rs181189778 exon64 c.T16495A p.S5499T het 0.0003 0.00019968 0.0003 0.0003 II, V DSG2 rs142841727 exon15 c.T2759G p.V920G het 0.0032 0.00319489 0.0036 0.0033

Prediction of SNP Impact on Protein Stability

I-Mutant3.0 tools were used to predict the stability of a protein-based on the presence and type of microvariant. For MuPro structural analysis allows the calculation of protein stability variations at arbitrary SNPs.

For the final predictive results of the I-Mutant 3.0 and Mupro tools, the results of the two previous bioinformatics tools were integrated, considering that if the prediction for the SNP is that stability decreases in two tools, the SNP would be considered as a high-risk ARX pathogen.

For the analysis of the effect of the SNP on the 3D structure of proteins and physicochemical properties, the HOPE server was used, which searches for 3D structures of proteins by collecting structural information from a series of sources, including calculations in the 3D coordinates of the protein. UniProt base sequence annotations and predictions from DAS services, are available at http://www.cmbi.ru.nl/ hope/.

Results

Clinical exome sequencing was performed in five unrelated SUD cases, although the data provided covered sequence variants in 4834 genes, the analysis focused on 184 genes associated with heart disease or sudden death. Bioinformatics filtering was performed considering the quality of the variants, population frequency, information provided by the various databases and in silico prediction and following the recommendations of the ACMG/AMP group. Once the different filters were made, relevant variants were found in ten genes that are presented in (Table 1).

ExAC 1000g gnomAD exome gnomAD genome TTN . exon186 c.T73979C p.I24660T het Absent Absent 0,004069 3.23E-05

  • TRPM4 rs138603244 exon17 c.A2365G p.S789G het
  • 0.0009
  • 0.00319489
  • 0.0005
  • 0.002
  • AKAP9 rs2230768 exon18 c.G4841A p.R1614Q het
  • 0.0023
  • 0.00898562
  • 0.0018
  • 0.0084
  • III
  • ANK3 rs41274676 exon37 c.C4465T p.P1489S het
  • 0.0019
  • 0.00079872
  • 0.0017
  • 0.0019
  • ANK3 rs201547988 exon37 c.C11159T p.T3720M het
  • 0.0002
  • 0.00019968
  • 0.0002
  • 0.0002
  • TTN rs759415579 exon168 c.C67100T p.P22367L het
  • 0,008287
  • Absent
  • 0,03257
  • Absent
  • IV
  • ANK2 rs61734478 exon38 c.G6634A p.G2212S hom
  • 0.0047
  • 0.0219649
  • 0.0037
  • 0.014
  • CACNA1C rs185788586 exon43 c.C5689T p.R1897C het
  • 0.0075
  • 0.00858626
  • 0.0101
  • 0.0018
  • V
  • TTN rs75686037 exon27 c.C5093T p.P1698L het
  • 0.0063
  • 0.00698882
  • 0.0072
  • 0.0026

Table 1: Variants Selected for Analysis According to Population Frequencies with A MAF ≤ 0.05.

Of the sixteen variants detected, nine variants are of uncertain significance, two present conflicting interpretations of pathogenicity, the rest is benign or slightly benign. Ten variants associated with Catecholaminergic polymorphic ventricular tachycardia, Arrhythmogenic right ventricular cardiomyopathy, Dilated cardiomyopathy 1G, Progressive familial heart block type 1B, Long QT syndrome, Romano-Ward syndrome, Brugada syndrome, Primary dilated cardiomyopathy and Hypertrophic cardiomyopathy, (Table 2).

CasesChange AACLNDNCLNSIG
Ip.V66ICatecholaminergic polymorphic ventricular
tachycardia		Uncertain significance
p.W16260C---Uncertain significance
p.I24660T---Uncertain significance
p.Y62C---Uncertain significance
I, IIp.E1234VArrhythmogenic right ventricular cardiomyopathyConflicting interpretations of
pathogenicity
IIp.A1969TDilated cardiomyopathy 1GUncertain significance
p.S5499TDilated cardiomyopathy 1GUncertain significance
II, Vp.V920GArrhythmogenic right ventricular cardiomyopathyConflicting interpretations of
pathogenicity
IIIp.S789GProgressive familial heart block type 1BBenign
p.R1614QLong QT syndrome, Romano-Ward syndromeBenign, Likely benign
p.P1489S---Uncertain significance
p.T3720M---Uncertain significance
p.P22367L---Uncertain significance
IVp.G2212SLong_QT syndromeBenign
p.R1897CLong QT syndrome, Brugada syndromeBenign, Likely benign
Vp.P1698LHypertrophic cardiomyopathy, Dilated
cardiomyopathy 1G		Benign, Likely benign

Table 2: Clinical Significance of the Variants Found in Five Cases of Unexplained Death.

In silico pathogenic predictors, pathogenic (D), probably pathogenic (Pp), and benign (T) variants were determined. A cutoff point of 20 were used for the CADD algorithm.

Prediction of conservation of the sequence used a cutoff point greater than 4.4. Results are shown in (Table 3).

CasesChange AASIFT
pred		Polyphen2
HDIV pred		Polyphen2
HVAR pred		LRT
pred		Mutation
Taster
pred		Mutation
Assessor
pred		FATHMM
pred		Radial
SVM
pred		LR
pred		CADD
phred		GERP++
RS
Ip.V66ITDDDDLTTT19.575.82
p.W16260CDDD.DHDDD12.745.87
p.I24660TDPP.DLTTT14.535.59
p.Y62CDDDDDMDDD17.363.44
I, IIp.E1234VTDDDNLTTT20.45.2
IIp.A1969TDDD.DLTTT12.345.12
IIp.S5499TDDD.DMTTT14.296.03
II, Vp.V920GTPBNNLTTT43782.5
IIIp.S789GTDPDDMTTT26.55.09
p.R1614QTPBNNNTTT89251.43
p.P1489SDDDDDMTDD17.555.69
p.T3720MDDPNDLTTT16.375.3
p.P22367LDDD.DHTDD19.086.03
IVp.G2212S.DBNDLTTT13.773.85
p.R1897CTDPNNNTTT87084.26
Vp.P1698LDDD.DMTTT11.855.05

Table 3: Prediction of pathogenicity of SNPs in silico for SIFT, PolyPhen2, Mutation Taster, LRT, Mutation Assesor, FATHMM, MetaS

Prediction of SNP Impact on Protein Stability

Changes in the protein stability of variants were examined using I-Mutant 2.0 and MUpro software (Table 4). The results predicted either an increase or a decrease in the free energy upon amino acid substitutions. I-Mutant, it predicts the stability by examining the Gibbs free energy by the ΔΔG value = ΔG (New protein) - ΔG (Wild type) in kcal/ mol, which is calculated at pH 7 and 25 °C. Scores <0 are predicted by the algorithm to indicate decreased stability, whereas scores >0 are considered to indicate increased stability. The DDG (ΔΔG) prediction by I-Mutant 2.0 showed that the 17 (72.3%) nsSNPs had a decreased stability value with DDG < 0 whereas −10 (22.7%) nsSNPs had an increased stability value with DDG > 0. With regard to MUpro reported that 1 (4.5%) substitutions increased the stability protein structure while 21 (95.5%) substitutions decreased it.

GenrsIDAA ChangeI-Mutant 3.0
Prediction		RIDDG score
prediction		MuPro
Prediction		MuPro Score
TRDNrs372169818p.V66IDecrease6-0.45Decrease-0.76372025
TTNrs746749916p.W16260CDecrease8-1.64Decrease-0.3799672
TTN-------p.24660TDecrease7-2.01Decrease-1.8524381
CAV3rs753990961p.Y62Cincrease2-1.07Decrease-0.94970534
DSPrs78652302p.E1234VIncrease20.09Decrease-0.57600156
TTNrs776534823p.A1969TDecrease3-0.6Decrease-1.0005569
TTNrs181189778p.S5499TDecrease0-32Decrease-0.65558429
DSG2rs142841727p.V920GDecrease9-2.02Decrease-2.2447524
TRPM4rs138603244p.S789GDecrease9-1.12Decrease-1.3670468
  • AKAP9 rs2230768 p.R1614Q
  • Decrease
  • 4
  • -0.33
  • Decrease
  • -0.59876134
  • ANK3 rs41274676 p.P1489S
  • Decrease
  • 9
  • -1.9
  • Decrease
  • -1.4299976
  • ANK3 rs201547988 p.T3720M
  • Decrease
  • 4
  • -0.39
  • Decrease
  • -0.37011007
  • TTN rs759415579 p.P22367L
  • Increase
  • 2
  • -0.3
  • Decrease
  • -0.38920806
  • ANK2 rs61734478 p.G2212S
  • Decrease
  • 4
  • -1.01
  • Decrease
  • -0.40436484
  • CACNA1C rs185788586 p.R1897C
  • Decrease
  • 6
  • -1.08
  • Decrease
  • -0.67510594
  • TTN rs75686037 p.P1698L
  • Decrease
  • 4
  • -0.44
  • Increase
  • 0.83737709

Table 5: Protein Stability of the Snps by I-Mutant 3.0 and Mupro: Mupro: Predicts that the Mutation Could Relatively Destabilize

The analysis 3D structure prediction, visualization and physiochemical changes of substitutions: In order to study the biophysical properties of these mutations, Project HOPE server was used to serve this purpose, RaptorX was used to predict a 3D structure model for TTN, TRPM4 and KCND3 protein (Figure 1).

a-1) p.P1698L : Proline a Leucine TTNa-2)p.W16260C: Tryptophan a Cysteine TTN
a-3) p.I24660T: Isoleucine to Threonine TTNa-4) p.P22367L: Proline to Leucine TTN
b) p.S789G: Serine to GlycineTRPM4c) p.R571H: Arginine to Histidine KCND3

Figure 1: Close-up of the pathogenic variant TTN, TRPM4 and KCND3 genes. The protein is grey, and the side chains of the wild type and mutant residues are shown and colored green and red, respectively. Variant: a-1) p.P1698L, a-2) p.W16260C, a-3) p.I24660T, and a-4) p.P22367L; b) p.S789G, and c) p.R571H. For Figure 1: TTN gen: a-1) (p.P1698L): the amino acid Proline changes to Leucine at position 1698. Wild-type and mutant amino acids differ in size. The mutant residue is larger, this could lead to bumps, the hydrophobicity of the wild-type and mutant residue differs. Hydrophobic interactions, either in the core of the protein or on the surface, will be lost. a-2) p.W16260C: Wild-type and mutant amino acids differ in size, the mutated residue is smaller than the wild-type residue, the mutation will cause an empty space in the core of the protein. The mutated residue is located in a domain that is important for the binding of other molecules and in contact with residues in a domain that is also important for binding. The mutation could disturb the interaction between these two domains and, as such, affect the function of the protein. a-3) p.I24660T: Wild-type and mutant amino acids differ in size, the mutated residue is smaller than the wild-type residue, the mutation will cause an empty space in the core of the protein, the hydrophobicity of the wild-type and mutant residue differs. The mutation will cause the loss of hydrophobic interactions in the core of the protein. a-4) p.P22367L: Wild-type and mutant amino acids differ in size. The mutated residue is larger than the wild-type residue, the wild-type residue was buried in the core of the protein. The mutant residue is larger and probably won’t fit. TRPM4: b) p.S789G: Wild-type and mutant amino acids differ in size, the mutated residue is smaller than the wild-type residue. The mutation will cause an empty space in the core of the protein. KCND3: c) p.R571H: There is a difference in charge between the wild-type and the mutated amino acid, The charge of the wild- type residue will be lost, which can lead to loss of interactions with other molecules or residues, Wild-type and mutant amino acids differ in size. The mutated residue is located in a domain that is important for the main activity of the protein. Mutation of the residue could alter this function.

The analysis of the clinical exome in case I identified six variants: one in the DSP gene (rs78652302) heterozygous, associated with arrhythmogenic right ventricular cardiomyopathy (ARVC), however, in clinical significance it appears conflicting pathogenicity interpretation; a heterozygous variant in the TRDN gene (rs372169818), of moderate impact, associated with catecholaminergic polymorphic ventricular tachycardia (CPVT), of uncertain significance and four variants of uncertain significance in the TTN genes (rs746749916 and p.I24660T), in the gene CAV3 (rs753990961) and in the DSPP gene (p.D1143E). All four variants are heterozygous, nonsense, and of moderate impact.

In case II, four variants were identified: two in the TTN gene (rs776534823 and rs181189778) heterozygous, nonsense of moderate impact, of uncertain significance, associated with  Limb-gerdle muscular dystrophy, type 2 diabetic  cardiomyopathy  and dilated cardiomyopathy 1G; one in the DSP gene (rs78652302) also reported for case 1; and  one in the DSG2 gene (rs14841727) heterozygous, nonsense, of moderate impact, associated with ARVC, type 2 diabetic myopathy and cardiovascular phenotype, presents conflict of interpretation of pathogenic.

In Case III, five variants were identified: one in the TRPM4 gene (rs138603244) heterozygous, nonsense of moderate impact, associated with progressive familial heart block type 1B, cardiovascular phenotype, benign; one in the AKAP9 gene (rs2230768) heterozygous, nonsense, of moderate impact, associated with LQT syndrome, Romano-Ward syndrome, cardiovascular phenotype, considered benign/probably benign; three variants of uncertain significance in the TTN genes (rs759415579) and in the ANK3 gene (rs201547988 and rs41274676) the three heterozygous, nonsense and moderate impact variants.

In case IV, two variants were identified: one in the ANK2 gene (rs61734478) homozygous, nonsense, of moderate impact, associated with LQT syndrome, cardiovascular phenotype, benign; one in the CACNA1C gene (rs185788586) heterozygous, nonsense, moderate impact associated with LQT syndrome,  Brugada  syndrome, Timothy syndrome, cardiovascular phenotype, benign/probably benign.

In case V, two variants were identified: one in the TTN gene (rs75686037) heterozygous, nonsense, of moderate impact, associated with hypertrophic cardiomyopathy,  Limb-gerdle  muscular dystrophy, type 2 dilated myopathy, Markesbery-Griggs type distal myopathy, cardiomyopathy dilated 1G, hereditary myopathy with early respiratory failure, early-onset diabetic myopathy, diabetic myopathy with fatal cardiomyopathy, dominant diabetic myopathy, recessive diabetic myopathy, benign/probably benign

Discussion

Authors of this study, the use of NGS allowed the identification of possible variants associated with cardiac disease in five victims of sudden cardiac death (Table 1).

In the case of the infants, while each of these variants alone may not have contributed significantly to death, it may be plausible that each of the variants observed in these cases partially contributed to an overall genetic predisposition, in an infant going through critical stages in its development, it is possible that these factors together may have contributed to the death of infants, which is known as “polygenic risk score”, an additive effect caused by multiple low-risk variants, each with a low pathogenic effect, but which collectively can produce sub threshold functioning of physiological pathways of interaction [28, 29]. However, more tests should be done.

In all five cases the deaths occurred during sleep. Cardiac diseases responsible for sudden death are characterized by autosomal inheritance, locus heterogeneity, and variable expressivity [30], which is consistent with our results.

The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidance for interpretation of sequence variants based on in silico predictions, population frequencies and functional analysis, pathogenic and likely pathogenic variants have additional implications for sudden death diagnostic purposes [31].

We found three variants with pathogenicity interpretation conflict in the DSP, DSG2 and MYBPC3 genes and seven variants of unknown significance and based on ACMG/AMP, which is very complicated in the forensic context.

Which were limited in our study, in addition, the antemortem clinical history of the deceased persons was not available.

The proper interpretation of the variants found is very important, since the interpretation of pathogenicity cannot be underestimated or overestimated, since it could bring consequences of uncertainty for families, therefore the analysis must be multidisciplinary and use as many of analysis tools to determine if a variant is pathogenic.

Conclusion

This Clinical exome analysis of five unrelated SUD cases revealed 15 protein-altering variants (MAF < 0.05), some of them clinically relevant, and the need arises to contact relatives for more detailed studies. The detection of variants of uncertain significance leads to the need to develop specific forensic guidelines that allow an adequate interpretation of rare genetic variants, together with a multidisciplinary team.

The variants found in the TRDN, TTN, CAV3, DSP, DSG2, TRPM4, AKAP9, ANK3, ANK2, and CACNA1C genes alone would not explain the cause of death, however, considering the frequencies population, the results of pathogenicity predictors, the presence of two or more variants in the same gene or different genes could lead to sudden cardiac death.

Sudden unexpected death of infants or adults can be attributed to a myriad of different diseases, only one of which is a hereditary heart disease, and being able to explore the possibilities of other diseases in a deceased person brings many challenges, especially when not accounted for. With the medical history.

Acknowledgments

The financial support was provided by the University of Magdalena and the University of Applied Sciences and Environmental Sciences UDCA, within the agreement they have with the National Institute of Legal Medicine and Forensic Sciences.

Ethical Approval

Ethical approval for this study principles contained in the updated Declaration of Helsinki were followed, as well as Laws, Decrees, and Resolutions related to viscerotomies and the use of forensic samples for research and teaching at the National Institute of Legal Medicine and Forensic Sciences.

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@article{alape2023,
  title   = {Retrospective Molecular Analysis in Five Cases Probably
Associated with Sudden Death the National Institute of Legal
Medicine and Forensic Sciences of Colombia},
  author  = {Alape Ariza J, Medina Rocha AH, Cabrera Pérez R and Bermúdez-
Santana CI},
  journal = {International Journal of Forensic Sciences},
  year    = {2023},
  volume  = {8},
  number  = {2},
  doi     = {10.23880/ijfsc-16000304}
}
Alape Ariza J, Medina Rocha AH, Cabrera Pérez R and Bermúdez-
Santana CI (2023). Retrospective Molecular Analysis in Five Cases Probably
Associated with Sudden Death the National Institute of Legal
Medicine and Forensic Sciences of Colombia. International Journal of Forensic Sciences, 8(2). https://doi.org/10.23880/ijfsc-16000304
TY  - JOUR
TI  - Retrospective Molecular Analysis in Five Cases Probably
Associated with Sudden Death the National Institute of Legal
Medicine and Forensic Sciences of Colombia
AU  - Alape Ariza J, Medina Rocha AH, Cabrera Pérez R and Bermúdez-
Santana CI
JO  - International Journal of Forensic Sciences
PY  - 2023
VL  - 8
IS  - 2
DO  - 10.23880/ijfsc-16000304
ER  -