Computational pharmacogenotype extraction from clinical next-generation sequencing.

Background: Next-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively. Methods: Germline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, TPMT, and VKORC1. Results: Mean WGS read depth was >30x for all variant regions except for G6PD (average read depth was 29 reads), and mean WES read depth was >30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and CYP2D6 hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within CYP2B6, CYP2C19, DPYD, SLCO1B1, and NUDT15. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with CYP2D6 copy number variation, all Aldy v4.4 diplotype calls except for one CYP3A4 diplotype call were concordant with genotyping for 161 patients in the WES cohort. Conclusion: Aldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with >99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: Large-Scale, 52-Week Observational Study.

BACKGROUND: Intermittent fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile apps to help them track and adhere to their fasts and monitor changes in their weight and other biometrics. OBJECTIVE: This study aimed to quantify user retention, fasting patterns, and weight loss by users of 2 IF mobile apps. We also sought to describe and model starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change. METHODS: We assembled height, weight, fasting, and demographic data of adult users (ages 18-100 years) of the LIFE Fasting Tracker and LIFE Extend apps from 2018 to 2020. Retention for up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least 2 readings of weight and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as extended fasting hours (EFH; hours beyond 12 in a fast) averaged per day (EFH per day). Retention was modeled using a Cox proportional hazards regression. Weight loss was analyzed using linear regression. RESULTS: A total of 792,692 users were followed for retention based on 26 million recorded fasts. Of these, 132,775 (16.7%) users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. The survival analysis using Cox regression indicated that retention was positively associated with age and exercise and negatively associated with stress and smoking. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH per day, which displayed a positive interaction. Users with a BMI ≥40 kg/m2 lost 13.9% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23 kg/m2. EFH per day was an approximately linear predictor of weight loss. By week 26, users lost over 1% of their starting weight per EFH per day on average. The regression analysis using all variables was highly predictive of weight change at 26 weeks (R2=0.334) with starting BMI and EFH per day as the most significant predictors. CONCLUSIONS: IF with LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Users who are obese lose substantial weight over time, with more weight loss in those who fast more.

Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing.

Germline whole exome sequencing from molecular tumor boards has the potential to be repurposed to support clinical pharmacogenomics. However, accurately calling pharmacogenomics-relevant genotypes from exome sequencing data remains challenging. Accordingly, this study assessed the analytical validity of the computational tool, Aldy, in calling pharmacogenomics-relevant genotypes from exome sequencing data for 13 major pharmacogenes. Germline DNA from whole blood was obtained for 164 subjects seen at an institutional molecular solid tumor board. All subjects had whole exome sequencing from Ashion Analytics and panel-based genotyping from an institutional pharmacogenomics laboratory. Aldy version 3.3 was operationalized on the LifeOmic Precision Health Cloud with copy number fixed to two copies per gene. Aldy results were compared with those from genotyping for 56 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, and TPMT. Read depth was >100× for all variants except CYP3A4∗22. For 75 subjects in the validation cohort, all 3393 Aldy variant calls were concordant with genotyping. Aldy calls for 736 diplotypes containing alleles assessed by both platforms were also concordant. Aldy identified additional star alleles not covered by targeted genotyping for 139 diplotypes. Aldy accurately called variants and diplotypes for 13 major pharmacogenes, except for CYP2D6 variants involving copy number variations, thus allowing repurposing of whole exome sequencing to support clinical pharmacogenomics.

Genetic testing for long-QT syndrome: distinguishing pathogenic mutations from benign variants.

BACKGROUND: Genetic testing for long-QT syndrome (LQTS) has diagnostic, prognostic, and therapeutic implications. Hundreds of causative mutations in 12 known LQTS-susceptibility genes have been identified. Genetic testing that includes the 3 most commonly mutated genes is available clinically. Distinguishing pathogenic mutations from innocuous rare variants is critical to the interpretation of test results. We sought to quantify the value of mutation type and gene/protein region in determining the probability of pathogenicity for mutations. METHODS AND RESULTS: Type, frequency, and location of mutations across KCNQ1 (LQT1), KCNH2 (LQT2), and SCN5A (LQT3) were compared between 388 unrelated "definite" (clinical diagnostic score >or=4 and/or QTc >or=480 ms) cases of LQTS and >1300 healthy controls for each gene. From these data, estimated predictive values (percent of mutations found in definite cases that would cause LQTS) were determined according to mutation type and location. Mutations were 10 times more common in cases than controls (0.58 per case versus 0.06 per control). Missense mutations were the most common, accounting for 78%, 67%, and 89% of mutations in KCNQ1, KCNH2, and SCN5A in cases and >95% in controls. Nonmissense mutations have an estimated predictive value >99% regardless of location. In contrast, location appears to be critical for characterizing missense mutations. Relative frequency of missense mutations between cases and controls ranged from approximately 1:1 in the SCN5A interdomain linker to infinity in the pore, transmembrane, and linker in KCNH2. These correspond to estimated predictive values ranging from 0% in the interdomain linker of SCN5A to 100% in the transmembrane/linker/pore regions of KCNH2. The estimated predictive value is also high in the linker, pore, transmembrane, and C terminus of KCNQ1 and the transmembrane/linker of SCN5A. CONCLUSIONS: Distinguishing pathogenic mutations from rare variants is of critical importance in the interpretation of genetic testing in LQTS. Mutation type, mutation location, and ethnic-specific BACKGROUND: should be viewed as variants of uncertain significance and prompt further investigation to clarify the likelihood of disease causation. However, mutations in regions such as the transmembrane, linker, and pore of KCNQ1 and KCNH2 may be defined confidently as high-probability LQTS-causing mutations. These findings will have implications for other genetic disorders involving mutational analysis.

The structure of common genetic variation in United States populations.

The common-variant/common-disease model predicts that most risk alleles underlying complex health-related traits are common and, therefore, old and found in multiple populations, rather than being rare or population specific. Accordingly, there is widespread interest in assessing the population structure of common alleles. However, such assessments have been confounded by analysis of data sets with bias toward ascertainment of common alleles (e.g., HapMap and Perlegen) or in which a relatively small number of genes and/or populations were sampled. The aim of this study was to examine the structure of common variation ascertained in major U.S. populations, by resequencing the exons and flanking regions of 3,873 genes in 154 chromosomes from European, Latino/Hispanic, Asian, and African Americans generated by the Genaissance Resequencing Project. The frequency distributions of private and common single-nucleotide polymorphisms (SNPs) were measured, and the extent to which common SNPs were shared across populations was analyzed using several different estimators of population structure. Most SNPs that were common in one population were present in multiple populations, but SNPs common in one population were frequently not common in other populations. Moreover, SNPs that were common in two or more populations often differed significantly in frequency from one population to another, particularly in comparisons of African Americans versus other U.S. populations. These findings indicate that, even if the bulk of alleles underlying complex health-related traits are common SNPs, geographic ancestry might well be an important predictor of whether a person carries a risk allele.

Expression of a common LQT1 mutation in five apparently unrelated families in a regional inherited arrhythmia clinic.

BACKGROUND: The Inherited Arrhythmia Clinic at the University of Western Ontario services a catchment area of 1.5 million people and follows families with inherited arrhythmia syndromes. METHODS: Patients referred for evaluation of long-QT Syndrome (LQTS) are evaluated with resting and standing ECGs, and treadmill exercise testing. Patients with findings consistent with LQTS are offered comprehensive genetic testing with screening of all first-degree relatives of genotype-positive patients. RESULTS: Among 31 probands with disease-causing LQTS mutations, 5 probands from apparently unrelated families of Irish descent were found to have an identical disease causing transmembrane mutation in KCNQ1 (Leu266Pro). Systematic screening of 33 first-degree relatives of genotype-positive individuals detected 15 unaffected and 18 asymptomatic affected family members. Symptoms in 6 patients occurred later in life than reported LQT1 populations (61 +/- 18 years, range 44-89). In this cohort, several family members presented with cardiac arrest during acute myocardial ischemia (n = 2), sudden death, unexplained drowning, and torsade de pointes during exercise testing. There was no identifiable common relative for this cohort after pedigree construction of the previous 4-7 generations. Affected patients had mild QT prolongation at rest with dramatic QT prolongation with exercise. CONCLUSIONS: Genetic testing in this LQTS population suggests a common KCNQ1 Leu266Pro founder effect, with the descendants clustering in our geographical region even though no common relative has been identified. The observations highlight the utility of genotypic and phenotypic correlation and a specialized clinic.

A genetic association study of maternal and fetal candidate genes that predispose to preterm prelabor rupture of membranes (PROM).

OBJECTIVE: We sought to determine whether maternal/fetal single-nucleotide polymorphisms (SNPs) in candidate genes are associated with preterm prelabor rupture of membranes (pPROM). STUDY DESIGN: A case-control study was conducted in patients with pPROM (225 mothers and 155 fetuses) and 599 mothers and 628 fetuses with a normal pregnancy; 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; false discovery rate was used to correct for multiple testing (q* = 0.15). RESULTS: First, a SNP in tissue inhibitor of metalloproteinase 2 in mothers was significantly associated with pPROM (odds ratio, 2.12; 95% confidence interval, 1.47-3.07; P = .000068), and this association remained significant after correction for multiple comparisons. Second, haplotypes for Alpha 3 type IV collagen isoform precursor in the mother were associated with pPROM (global P = .003). Third, multilocus analysis identified a 3-locus model, which included maternal SNPs in collagen type I alpha 2, defensin alpha 5 gene, and endothelin 1. CONCLUSION: DNA variants in a maternal gene involved in extracellular matrix metabolism doubled the risk of pPROM.

Identification of fetal and maternal single nucleotide polymorphisms in candidate genes that predispose to spontaneous preterm labor with intact membranes.

OBJECTIVE: The purpose of this study was to determine whether maternal/fetal single nucleotide polymorphisms (SNPs) in candidate genes are associated with spontaneous preterm labor/delivery. STUDY DESIGN: A genetic association study was conducted in 223 mothers and 179 fetuses (preterm labor with intact membranes who delivered <37 weeks of gestation [preterm birth (PTB)]), and 599 mothers and 628 fetuses (normal pregnancy); 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; the false discovery rate was used to correct for multiple testing. RESULTS: The strongest single locus associations with PTB were interleukin-6 receptor 1 (fetus; P=.000148) and tissue inhibitor of metalloproteinase 2 (mother; P=.000197), which remained significant after correction for multiple comparisons. Global haplotype analysis indicated an association between a fetal DNA variant in insulin-like growth factor F2 and maternal alpha 3 type IV collagen isoform 1 (global, P=.004 and .007, respectively). CONCLUSION: An SNP involved in controlling fetal inflammation (interleukin-6 receptor 1) and DNA variants in maternal genes encoding for proteins involved in extracellular matrix metabolism approximately doubled the risk of PTB.

Allelic dropout in long QT syndrome genetic testing: a possible mechanism underlying false-negative results.

BACKGROUND: Genetic testing for congenital long QT syndrome (LQTS) has been performed in research laboratories for the past decade. Approximately 75% of patients with high clinical probability for LQTS have a mutation in one of five LQTS-causing cardiac channel genes. Possible explanations for the remaining genotype-negative cases include LQTS mimickers, novel LQTS-causing genes, unexplored regions of the known genes, and genetic testing detection failures. OBJECTIVES: The purpose of this study was to explore the possibility of allelic dropout as a possible mechanism underlying false-negative test results. METHODS: The published primers currently used by many research laboratories to conduct a comprehensive analysis of the 60 translated exons in the KCNQ1 (LQT1), KCNH2 (LQT2), SCN5A (LQT3), KCNE1 (LQT5), and KCNE2 (LQT6) genes were analyzed for the presence of common intronic single nucleotide polymorphisms (SNPs). Repeat mutational analysis, following primer/amplicon redesign using polymerase chain reaction, denaturing high-performance liquid chromatography, and DNA sequencing, was performed on a cohort of 541 consecutive, unrelated patients referred for LQTS genetic testing. RESULTS: Common (>1% minor allele frequency) intronic SNPs were discovered within the primer sequences of five of 60 translated exons. Following primer redesign to eliminate the possibility of allelic dropout, four previously genotype-negative index cases were found to possess LQTS-causing mutations: R591H-KCNQ1 and R594Q-KCNQ1 for exon 15 and E229X-KCNH2 in two unrelated cases. Repeat examination of these two amplicons in 400 reference alleles did not identify these or any additional amino acid variants. CONCLUSION: Allelic dropout secondary to intronic SNP-primer mismatch prevented the discovery of LQTS-causing mutations in four cases. Considering that many LQTS genetic testing research laboratories have used these primers, patients who reportedly are genotype negative may benefit from re-examination of those regions susceptible to allelic dropout due to primer-disrupting SNPs, particularly exon 15 in KCNQ1 and exon 4 in KCNH2.

Enhancing the Predictive Power of Mutations in the C-Terminus of the KCNQ1-Encoded Kv7.1 Voltage-Gated Potassium Channel.

Despite the overrepresentation of Kv7.1 mutations among patients with a robust diagnosis of long QT syndrome (LQTS), a background rate of innocuous Kv7.1 missense variants observed in healthy controls creates ambiguity in the interpretation of LQTS genetic test results. A recent study showed that the probability of pathogenicity for rare missense mutations depends in part on the topological location of the variant in Kv7.1's various structure-function domains. Since the Kv7.1's C-terminus accounts for nearly 50 % of the overall protein and nearly 50 % of the overall background rate of rare variants falls within the C-terminus, further enhancement in mutation calling may provide guidance in distinguishing pathogenic long QT syndrome type 1 (LQT1)-causing mutations from rare non-disease-causing variants in the Kv7.1's C-terminus. Therefore, we have used conservation analysis and a large case-control study to generate topology-based estimative predictive values to aid in interpretation, identifying three regions of high conservation within the Kv7.1's C-terminus which have a high probability of LQT1 pathogenicity.

The Use of Non-Variant Sites to Improve the Clinical Assessment of Whole-Genome Sequence Data.

Genetic testing, which is now a routine part of clinical practice and disease management protocols, is often based on the assessment of small panels of variants or genes. On the other hand, continuous improvements in the speed and per-base costs of sequencing have now made whole exome sequencing (WES) and whole genome sequencing (WGS) viable strategies for targeted or complete genetic analysis, respectively. Standard WGS/WES data analytical workflows generally rely on calling of sequence variants respect to the reference genome sequence. However, the reference genome sequence contains a large number of sites represented by rare alleles, by known pathogenic alleles and by alleles strongly associated to disease by GWAS. It's thus critical, for clinical applications of WGS and WES, to interpret whether non-variant sites are homozygous for the reference allele or if the corresponding genotype cannot be reliably called. Here we show that an alternative analytical approach based on the analysis of both variant and non-variant sites from WGS data allows to genotype more than 92% of sites corresponding to known SNPs compared to 6% genotyped by standard variant analysis. These include homozygous reference sites of clinical interest, thus leading to a broad and comprehensive characterization of variation necessary to an accurate evaluation of disease risk. Altogether, our findings indicate that characterization of both variant and non-variant clinically informative sites in the genome is necessary to allow an accurate clinical assessment of a personal genome. Finally, we propose a highly efficient extended VCF (eVCF) file format which allows to store genotype calls for sites of clinical interest while remaining compatible with current variant interpretation software.

DNA variability of human genes.

We have investigated the level of DNA-based variation (both SNPs and haplotypes) for several thousand human genes. In addition, we have characterized how this variation is distributed in a number of biologically and clinically important ways. First, we have determined how SNPs are distributed within human genes: where they occur relative to various functional regions; levels of variability of human SNPs; pattern of the molecular sequence of SNPs; and how these compare with the corresponding sequence of a chimpanzee. Second, we have determined how these aspects of SNP distribution vary among four human population samples. All genes were sequenced on DNA obtained from 82 unrelated individuals: 20 African-Americans, 20 East Asians, 21 European-Americans, 18 Hispanic-Latinos and three Native Americans. In particular, we looked at patterns of SNP and haplotype sharing among the four larger population samples. Third, we have determined the patterns of linkage disequilibrium among SNPs, which also determines the haplotype variability of each gene. These characteristics also vary substantially among populations. A deeper understanding of these aspects of human genetic variation will be of vital importance when trying to identify the genetic contribution to complex phenotypes such as aging.

Genetic variability and evolution of two pharmacologically important classes of genes.

We have studied the human genetic variability of single nucleotide polymorphisms (SNPs) and haplotypes in two pharmaceutically important classes of genes that might be expected to experience different evolutionary pressures: antigen presentation and processing (APP) and nuclear hormone receptor (NHR) genes. We compared the variation pattern in these two classes of genes with 5119 reference (REF) genes. We assessed this variability by sequencing and discovering SNPs in 5'-upstream, 5'-untranslated region (5'UTR), exon, intron, 3'UTR and 3'-downstream regions of all these genes in 79 unrelated humans from diverse ethnic backgrounds, one chimpanzee (Pan troglodytes) and a gorilla (Gorilla gorilla). SNP density and nucleotide diversity were higher in the APP genes than the REF genes. Relative to the REF genes, APP SNP density was significantly higher in the coding and 3'UTR regions. Higher variation in the coding region of the APP genes was due specifically to having more non-synonymous changes, which suggests that natural selection may be acting to promote change or diversity in these proteins. In contrast, the NHR genes showed lower SNP density and diversity relative to REF genes. The NHR genes consistently showed lower nucleotide diversity in all the genomic regions except in the 3'downstream region. SNP frequency data on the non-synonymous SNPs also suggested that the coding region in the NHR genes is conserved to a higher degree than the coding region in the REF genes. Significantly lower SNP density was observed in the 5'-upstream and 5'UTR regions of the NHR genes, perhaps reflecting selective conservation of these regions. Heterozygosity in the APP genes was significantly higher than in the NHR genes in each of the three species tested. Moreover, between species there were more fixed differences in the APP genes than in the NHR genes. Substantial variability exists in these two classes of genes. It is important to consider this interindividual variability pattern while developing drugs that act on such targets.

Strongest Evidence Revisited.

In response to comments by J. S. Farris (2000, Cladistics 16, 403-410) on the strongest evidence (SE) approach to phylogenetic analysis, I examine the concepts on which it is founded and reevaluate its merits. SE's null model of signal absence in characters is not treated as background knowledge, but as a reference point for evaluating a data set's phylogenetic signal in a tree-specific manner. In simulation tests, the SE methods perform reasonably well; although parsimony is generally more accurate and less biased than SE, SE is distinctly more accurate in some circumstances. Simulations further indicate that jackknifing is often beneficial in both SE and parsimony analyses. Iterative fixation of splits shows promise as an auxiliary procedure for SE and other methods that weight according to apparent homoplasy.

Strongest Evidence: Maximum Apparent Phylogenetic Signal as a New Cladistic Optimality Criterion.

A new method for phylogenetic inference, Strongest Evidence (SE), is described. In this method, a character's support for a phylogenetic hypothesis, its apparent phylogenetic signal, is greatest when the amount of implied homoplasy is most remarkably small given background knowledge alone. Because evolutionary rates are not assumed to be slow, background expectations for character length can be derived through modeling complete dissociation between branching pattern and character state assignments. As in unweighted parsimony, SE holds that fewer required evolutionary steps in a character indicates stronger support for a tree. However, in SE, the relationship between steps and support differs by unlabeled tree topology and character state distribution. Strongest evidence is contrasted in detail with both unweighted parsimony and Goloboff's method of implied weights. An iterative process is suggested for incrementally resolving a phylogenetic hypothesis while conducting cladistic analyses at increasingly local levels.

SNP and haplotype variation in the human genome.

We have surveyed and summarized several aspects of DNA variability among humans. The variation described is the result of mutation followed by a combination of drift, migration and selection bringing the frequencies high enough to be observed. This paper describes what we have learned about how DNA variability differs among genes and populations. We sequenced functional regions of a set of 3950 genes. DNA was sampled from 82 unrelated humans: 20 African-Americans, 20 East Asians, 21 Caucasians, 18 Hispanic-Latinos and 3 Native Americans. Different aspects of variability showed a great deal of concordance. In particular, we studied patterns of single nucleotide polymorphism (SNP) allele and haplotype sharing among the four, large sample populations. We also examined how linkage disequilibrium (LD) between SNPs relates to physical distance in the different populations. It is clear from our findings that while many variants are common to all populations, many others have a more restricted distribution. Research that attempts to find genetic variants that explain phenotypic variants must be careful in their choice of study population.

Distinguishing hypertrophic cardiomyopathy-associated mutations from background genetic noise.

Despite the significant progress that has been made in identifying disease-associated mutations, the utility of the hypertrophic cardiomyopathy (HCM) genetic test is limited by a lack of understanding of the background genetic variation inherent to these sarcomeric genes in seemingly healthy subjects. This study represents the first comprehensive analysis of genetic variation in 427 ostensibly healthy individuals for the HCM genetic test using the "gold standard" Sanger sequencing method validating the background rate identified in the publically available exomes. While mutations are clearly overrepresented in disease, a background rate as high as ∼5 % among healthy individuals prevents diagnostic certainty. To this end, we have identified a number of estimated predictive value-based associations including gene-specific, topology, and conservation methods generating an algorithm aiding in the probabilistic interpretation of an HCM genetic test.

Phylogenetic and physicochemical analyses enhance the classification of rare nonsynonymous single nucleotide variants in type 1 and 2 long-QT syndrome.

BACKGROUND: Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes (KCNQ1 and KCNH2). Unfortunately, an ≈3% BACKGROUND: and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants. METHODS AND RESULTS: In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology-derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1, whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide-binding domain, the topology-specific estimated predictive value improved from 56% to 91%. CONCLUSIONS: Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1's difficult to interpret C-terminal region.

Candidate gene analysis identifies a polymorphism in HLA-DQB1 associated with clozapine-induced agranulocytosis.

OBJECTIVE: Clozapine is considered to be the most efficacious drug to treat schizophrenia, although it is underutilized, partially due to a side effect of agranulocytosis. This analysis of 74 candidate genes was designed to identify an association between sequence variants and clozapine-induced agranulocytosis (CIA). METHOD: Blood and medical history were collected for 33 CIA cases and 54 clozapine-treated controls enrolled between April 2002 and December 2003. Significant markers from 4 genes were then assessed in an independently collected case-control cohort (49 CIA cases, 78 controls). RESULTS: Sequence variants in 5 genes were found to be associated with CIA in the first cohort: HLA-DQB1, HLA-C, DRD1, NTSR1, and CSF2RB. Sequence variants in HLA-DQB1 were also found to be associated with CIA in the second cohort. After refinement analyses of sequence variants in HLA-DQB1, a single SNP (single nucleotide polymorphism), 6672G>C, was found to be associated with risk for CIA; the odds of CIA are 16.9 times greater in patients who carry this marker compared to those who do not. CONCLUSIONS: A sequence variant (6672G>C) in HLA-DQB1 is associated with increased risk for CIA. This marker identifies a subset of patients with an exceptionally high risk of CIA, 1,175% higher than the overall clozapine-treated population under the current blood-monitoring system. Assessing risk for CIA by testing for this and other genetic variants yet to be determined may be clinically useful when deciding whether to begin or continue treatment with clozapine.

Distinguishing arrhythmogenic right ventricular cardiomyopathy/dysplasia-associated mutations from background genetic noise.

OBJECTIVES: The aims of this study were to determine the spectrum and prevalence of "background genetic noise" in the arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC) genetic test and to determine genetic associations that can guide the interpretation of a positive test result. BACKGROUND: ARVC is a potentially lethal genetic cardiovascular disorder characterized by myocyte loss and fibrofatty tissue replacement of the right ventricle. Genetic variation among the ARVC susceptibility genes has not been systematically examined, and little is known about the background noise associated with the ARVC genetic test. METHODS: Using direct deoxyribonucleic acid sequencing, the coding exons/splice junctions of PKP2, DSP, DSG2, DSC2, and TMEM43 were genotyped for 93 probands diagnosed with ARVC from the Netherlands and 427 ostensibly healthy controls of various ethnicities. Eighty-two additional ARVC cases were obtained from published reports, and additional mutations were included from the ARVD/C Genetic Variants Database. RESULTS: The overall yield of mutations among ARVC cases was 58% versus 16% in controls. Radical mutations were hosted by 0.5% of control individuals versus 43% of ARVC cases, while 16% of controls hosted missense mutations versus a similar 21% of ARVC cases. Relative to controls, mutations in cases occurred more frequently in non-Caucasians, localized to the N-terminal regions of DSP and DSG2, and localized to highly conserved residues within PKP2 and DSG2. CONCLUSIONS: This study is the first to comprehensively evaluate genetic variation in healthy controls for the ARVC susceptibility genes. Radical mutations are high-probability ARVC-associated mutations, whereas rare missense mutations should be interpreted in the context of race and ethnicity, mutation location, and sequence conservation.