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Estimating the prevalence of allelic variants in the transthyretin gene by analysing large-scale sequencing data.

ABSTRACT: Transthyretin amyloidosis (ATTR amyloidosis) is a rare disease characterised by extracellular deposition of amyloid fibrils composed by transthyretin. ATTR amyloidosis can be sub-classified as wild-type ATTR (ATTR-wt) or as hereditary amyloidosis (ATTR-m); the prevalence of both types are likely underestimated. There are tools that can help us to study ATTR-m, as gnomAD database. Our primary aim was to estimate prevalence of variants, especially amyloidogenic variants, in the TTR gene using gnomAD database. We analysed TTR missense variants found in gnomAD. The variables studied were classified according to their clinical significance and according to the different populations. We found 71 missense variants in the TTR gene. Eleven variants were described as affects function variants (prevalence 1:230). The most frequently detected variant were c.424G>A (p.(Val142Ile)) (prevalence 1:332, MAF 0.00151) and c.148G>A (p.(Val50Met)) (prevalence 1:4924, MAF 0.000102), which represented 88% and 5%, respectively, of all affects function variants detected. Seventeen variants were classified as probably affects function, 29 as unknown variants, 4 as probably does not affect function and 10 as does not affect function variants. In terms of different populations, c.424G>A (p.(Val142Ile)) was especially prevalent in African population (MAF 0.01602; prevalence of 1:31) and c.148G>A (p.(Val50Met)) in European population (MAF 0.000179; prevalence of 1:2792). Prevalence of amyloidogenic variants in the general population was higher than prevalence heretofore described. This difference could be explained by incomplete penetrance of the disease, but other factors contributing to this fact, fundamentally the underdiagnosis of the disease.

SUBMITTER: Lahuerta Pueyo C

PROVIDER: S-EPMC6461877 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Estimating the prevalence of allelic variants in the transthyretin gene by analysing large-scale sequencing data.

Lahuerta Pueyo Carmen C Aibar Arregui Miguel Ángel MÁ Gracia Gutierrez Anyuli A Bueno Juana Esperanza E Menao Guillén Sebastián S

European journal of human genetics : EJHG 20190125 5

Transthyretin amyloidosis (ATTR amyloidosis) is a rare disease characterised by extracellular deposition of amyloid fibrils composed by transthyretin. ATTR amyloidosis can be sub-classified as wild-type ATTR (ATTR-wt) or as hereditary amyloidosis (ATTR-m); the prevalence of both types are likely underestimated. There are tools that can help us to study ATTR-m, as gnomAD database. Our primary aim was to estimate prevalence of variants, especially amyloidogenic variants, in the TTR gene using gnom ...[more]

PMID: 30683924

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Project description:IntroductionTransthyretin amyloid cardiomyopathy (ATTR-CM)-a debilitating, fatal disease resulting from the deposition of transthyretin (TTR) amyloid fibrils-can be hereditary due to mutations in the TTR gene (ATTRm) or wild type (ATTRwt). The global prevalence of ATTR-CM is largely unknown, although likely underestimated, with no formal epidemiological prevalence studies in Japan. This study aimed to estimate the prevalence of ATTR-CM in a large in-hospital database in Japan.MethodsThis was a retrospective, observational, cross-sectional study which utilized data from all adult patients (aged ≥ 20 years) in the hospital-based Japan Medical Data Vision (MDV) database from January 2010 to September 2018 to estimate the number of currently diagnosed ATTR-CM patients and describe their demographic and clinical characteristics and diagnostic modalities. ATTR-CM patients (ATTRwt and ATTRm) were identified using a range of diagnosis codes that were applied to create broad and narrow definitions of the disease.ResultsOver the 9 years of this study, there were 3255 (155.8 per million adult patients in the MDV database) to 3992 (191.1 per million) diagnoses of ATTRwt and 67 (3.2 per million) to 106 (5.1 per million) diagnoses of ATTRm in the MDV database (based on the narrow and broad definitions, respectively). There were 444 (21.2 per million) diagnoses of amyloid light-chain (AL) amyloidosis. Considering only those patients who were also diagnosed with heart failure, there were 1468 (70.3 per million) to 1798 (86.1 per million) diagnoses of ATTRwt and 50 (2.4 per million) to 61 (2.9 per million) diagnoses of ATTRm. Most ATTRwt patients (~ 90%) did not have a record of endomyocardial or abdominal wall biopsy, or of scintigram.ConclusionThis retrospective study provides an estimate of the number of patients diagnosed with ATTR-CM in a large in-hospital database in Japan over a period of 9 years. Improving awareness of disease prevalence may improve diagnosis and treatment.FundingPfizer.

Project description:Techniques of data mining and machine learning were applied to a large database of medical and facility claims from commercially insured patients to determine the prevalence, gender demographics, and costs for individuals with provider-assigned diagnosis codes for myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS). The frequency of diagnosis was 519-1,038/100,000 with the relative risk of females being diagnosed with ME or CFS compared to males 1.238 and 1.178, respectively. While the percentage of women diagnosed with ME/CFS is higher than the percentage of men, ME/CFS is not a "women's disease." Thirty-five to forty percent of diagnosed patients are men. Extrapolating from this frequency of diagnosis and based on the estimated 2017 population of the United States, a rough estimate for the number of patients who may be diagnosed with ME or CFS in the U.S. is 1.7 million to 3.38 million. Patients diagnosed with CFS appear to represent a more heterogeneous group than those diagnosed with ME. A machine learning model based on characteristics of individuals diagnosed with ME was developed and applied, resulting in a predicted prevalence of 857/100,000 (p > 0.01), or roughly 2.8 million in the U.S. Average annual costs for individuals with a diagnosis of ME or CFS were compared with those for lupus (all categories) and multiple sclerosis (MS), and found to be 50% higher for ME and CFS than for lupus or MS, and three to four times higher than for the general insured population. A separate aspect of the study attempted to determine if a diagnosis of ME or CFS could be predicted based on symptom codes in the insurance claims records. Due to the absence of specific codes for some core symptoms, we were unable to validate that the information in insurance claims records is sufficient to identify diagnosed patients or suggest that a diagnosis of ME or CFS should be considered based solely on looking for presence of those symptoms. These results show that a prevalence rate of 857/100,000 for ME/CFS is not unreasonable; therefore, it is not a rare disease, but in fact a relatively common one.

Project description:BackgroundThe malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.MethodsEven though statistical tests have been utilized to conduct case-control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann-Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.ResultsThe findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.ConclusionsThese findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann-Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.

Project description:BackgroundThe Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years.ResultsWe evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC.ConclusionsWe conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

Dataset Information

Estimating the prevalence of allelic variants in the transthyretin gene by analysing large-scale sequencing data.

Publications

Estimating the prevalence of allelic variants in the transthyretin gene by analysing large-scale sequencing data.

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