Genetic anticipation inBRCA1/BRCA2families after controlling for ascertainment bias and cohort effect

Introduction: To identify HCM at the earliest time possible, estimating the onset of disease based on diagnostic criteria is vital in timing the initiation of screening and interventions. Hypothesis: Clinic observations indicate an early diagnose of HCM in second generation of most affected families. From the pedigrees with or without mutations in beta-myosin heavy chain gene (MYH7) affected in two or more generations with hypertrophic cardiomyopathy (HCM), ages and maximal wall thickness (MWT) of left ventricles at diagnosis were evaluated to investigate the potential genetic anticipation in patients with familiar HCM (FHCM). Methods: 56 individuals from 25 families were analyzed. Linear mixed effects models were adopted to prevent misinterpretation resulting from the cohort effect. Published data containing 9 sarcomere mutations (181 individuals in 52 families) also were extracted. Results: MYH7 mutations were detected in 9 of the 25 probands, The median age of HCM diagnosis was 24 in the younger generation and 55 in the older (p < 0.001). In the parametric model, the estimated change in the expected age at diagnosis for the entire cohort was 25.8 years (p <0.001). Statistically significant earlier ages at diagnoses were also observed within subgroups of MYH7+ and MYH7- mutations, and probands older and younger than 30 years old. Although the estimated change in MWT at diagnosis for the entire cohort was only 2.161 mm (p =0.212), the subgroup of probands in the younger generation aged less than 30 years had 10.393 mm (p=0.018) thicker MWT and showed a significant reverse correlation with age. Analysis of published data also supports these findings. Conclusions: Genetic anticipation was observed in patients with FHCM. FHCM is prone to be diagnosed at an earlier age in younger generations. Patients who are younger with relatives affected by HCM, especially those who are diagnosed before 30 years of age, should continue to be tracked to offer appropriate screening modalities as earlier as possible.

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The Apparent Genetic Anticipation in PMS2-Associated Lynch Syndrome Families Is Explained by Birth-cohort Effect

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-18-0576 ◽

2019 ◽

Vol 28 (6) ◽

pp. 1010-1014 ◽

Cited By ~ 2

Author(s):

Sanne W. ten Broeke ◽

Mar Rodríguez-Girondo ◽

Manon Suerink ◽

Stefan Aretz ◽

Inge Bernstein ◽

...

Keyword(s):

Lynch Syndrome ◽

Birth Cohort ◽

Cohort Effect ◽

Genetic Anticipation ◽

Birth Cohort Effect

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Development of Microsatellite Loci Exhibiting Reverse Ascertainment Bias and a Sexing Marker for use in Emperor Geese (Chen Canagica)

Avian Biology Research ◽

10.3184/175815617x14969254461396 ◽

2017 ◽

Vol 10 (4) ◽

pp. 201-210 ◽

Cited By ~ 1

Author(s):

Meg C. Gravley ◽

George K. Sage ◽

Joel A. Schmutz ◽

Sandra L. Talbot

Keyword(s):

Microsatellite Loci ◽

Statistical Power ◽

Ascertainment Bias ◽

Next Generation Sequencing Technology ◽

Branta Bernicla ◽

Black Brant ◽

Expected Heterozygosity ◽

Cackling Geese ◽

The 1960S ◽

Goose Species

The Alaskan population of Emperor Geese ( Chen canagica) nests on the Yukon–Kuskokwim Delta in western Alaska. Numbers of Emperor Geese in Alaska declined from the 1960s to the mid-1980s and since then, their numbers have slowly increased. Low statistical power of microsatellite loci developed in other waterfowl species and used in previous studies of Emperor Geese are unable to confidently assign individual identity. Microsatellite loci for Emperor Goose were therefore developed using shotgun amplification and next-generation sequencing technology. Forty-one microsatellite loci were screened and 14 were found to be polymorphic in Emperor Geese. Only six markers – a combination of four novel loci and two loci developed in other waterfowl species – are needed to identify an individual from among the Alaskan Emperor Goose population. Genetic markers for identifying sex in Emperor Geese were also developed. The 14 novel variable loci and 15 monomorphic loci were screened for polymorphism in four other Arctic-nesting goose species, Black Brant ( Branta bernicla nigricans), Greater White-fronted ( Anser albifrons), Canada ( B. canadensis) and Cackling ( B. hutchinsii) Goose. Emperor Goose exhibited the smallest average number of alleles (3.3) and the lowest expected heterozygosity (0.467). Greater White-fronted Geese exhibited the highest average number of alleles (4.7) and Cackling Geese the highest expected heterozygosity (0.599). Six of the monomorphic loci were variable and able to be characterised in the other goose species assayed, a predicted outcome of reverse ascertainment bias. These findings fail to support the hypothesis of ascertainment bias due to selection of microsatellite markers.

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Faculty Opinions recommendation of Variable locus length in the human genome leads to ascertainment bias in functional inference for non-coding elements.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1157594.617784 ◽

2009 ◽

Author(s):

Casey Bergman

Keyword(s):

Human Genome ◽

Ascertainment Bias ◽

Functional Inference

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JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

Journal of Biological System ◽

10.1142/s0218339017400022 ◽

2017 ◽

Vol 25 (04) ◽

pp. 587-603 ◽

Cited By ~ 2

Author(s):

YUSUKE ASAI ◽

HIROSHI NISHIURA

Keyword(s):

Real Time ◽

Ebola Virus ◽

Virus Disease ◽

Critical Role ◽

Estimation Method ◽

Case Fatality ◽

Ascertainment Bias ◽

Joint Estimation ◽

Ebola Virus Disease ◽

Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

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Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Risks ◽

10.3390/risks9010005 ◽

2020 ◽

Vol 9 (1) ◽

pp. 5

Author(s):

Karim Barigou ◽

Stéphane Loisel ◽

Yahia Salhi

Keyword(s):

Life Insurance ◽

Cross Validation ◽

Cohort Effect ◽

Information Criterion ◽

Mortality Forecasting ◽

Hand Model ◽

Regularization Techniques ◽

Bayes Information Criterion ◽

The One ◽

Regularized Model

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

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THU0173 Genetic anticipation in familial rheumatoid arthritis

10.1136/annrheumdis-2001.1075 ◽

2001 ◽

Author(s):

EP Sharapova ◽

LI Alexeeva ◽

IA Guseva ◽

SA Finogenova ◽

MY Krylov ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Genetic Anticipation

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Appetite disinhibition rather than hunger explains genetic effects on adult BMI trajectory

International Journal of Obesity ◽

10.1038/s41366-020-00735-9 ◽

2021 ◽

Author(s):

Eric J. Brunner ◽

Koutatsu Maruyama ◽

Martin Shipley ◽

Noriko Cable ◽

Hiroyasu Iso ◽

...

Keyword(s):

Weight Gain ◽

Genetic Susceptibility ◽

Eating Behaviors ◽

Mixed Model ◽

Adult Life ◽

Cohort Effect ◽

The Body ◽

European Ancestry ◽

Nucleotide Polymorphisms ◽

Bmi Trajectories

Abstract Background/objectives The mediating role of eating behaviors in genetic susceptibility to weight gain during mid-adult life is not fully understood. This longitudinal study aims to help us understand contributions of genetic susceptibility and appetite to weight gain. Subjects/methods We followed the body-mass index (BMI) trajectories of 2464 adults from 45 to 65 years of age by measuring weight and height on four occasions at 5-year intervals. Genetic risk of obesity (gene risk score: GRS) was ascertained, comprising 92 BMI-associated single-nucleotide polymorphisms and split at a median (=high and low risk). At the baseline, the Eating Inventory was used to assess appetite-related traits of ‘disinhibition’, indicative of opportunistic eating or overeating and ‘hunger’ which is susceptibility to/ability to cope with the sensation of hunger. Roles of the GRS and two appetite-related scores for BMI trajectories were examined using a mixed model adjusted for the cohort effect and sex. Results Disinhibition was associated with higher BMI (β = 2.96; 95% CI: 2.66–3.25 kg/m2), and accounted for 34% of the genetically-linked BMI difference at age 45. Hunger was also associated with higher BMI (β = 1.20; 0.82–1.59 kg/m2) during mid-life and slightly steeper weight gain, but did not attenuate the effect of disinhibition. Conclusions Appetite disinhibition is most likely to be a defining characteristic of genetic susceptibility to obesity. High levels of appetite disinhibition, rather than hunger, may underlie genetic vulnerability to obesogenic environments in two-thirds of the population of European ancestry.

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