A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions

When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), generalized linear models (GLMs) with fixed pair effects (four studies), within-pair difference analyses (three studies), and paired-sample tests (two studies). Four were marginal approaches (unpaired analyses): generalized estimating equations (GEE) models (five studies), GLMs with cluster-robust standard errors (six studies), GLMs (one study), and independent-sample tests (one study). ACE models are suitable for assessing heritability but require adaptations for binary outcomes and repeated measurements. Conditional models can adjust by design for shared confounders, and GLMMs are suitable for repeated measurements. Marginal models may lead to invalid inference. By highlighting the strengths and limitations of commonly applied statistical methods, this review may be helpful for researchers using twin designs.

COVID-19 Pandemic Initial Effects on the Idiosyncratic Risk in Latin America

This work aims to estimate the idiosyncratic risk of Latin American economies and emerging economies using heteroscedastic conditional models to verify the impact of the Covid-19 pandemic on the risk associated with productive projects. The methodology used is based on the portfolio theory to estimate the idiosyncratic risk. The results highlight that Latin American economies are more susceptible to sanitary crises, such as the current pandemic, than emerging economies. The inability of emerging countries to generate the necessary savings to provide for their development imposes the need to attract resources for project financing and investment. Thus, determining the specific risk of Latin American countries is fundamental for international investors giving them another parameter when deciding on investment or financing on the continent. Originally, this work demonstrates how the sanitary crisis deriving from the Covid-19 pandemic affected the idiosyncratic or specific risk of Latin American economies using their capital market indicators. This study contributes to the assessment of Latin American economies specific risk or country risk at the beginning of the pandemic.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

Body surface area (BSA) is an important trait used for many clinical purposes. People’s BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10−5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ –0.013 for T/T and 0.013 for G/G, −Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and –0.012 for G/G, −Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ –0.018 for G/G and 0.018 for C/C, −Log10 PEW = 19.737) and dominance (d≙ –0.038 for G/C, −Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

Establishment of a Cre-rat resource for creating conditional and physiological relevant models of human diseases

The goal of this study is to establish a Cre/loxP rat resource for conditional and physiologically predictive rat models of human diseases. The laboratory rat (R. norvegicus) is a central experimental animal in several fields of biomedical research, such as cardiovascular diseases, aging, infectious diseases, autoimmunity, cancer models, transplantation biology, inflammation, cancer risk assessment, industrial toxicology, pharmacology, behavioral and addiction studies, and neurobiology. Up till recently, the ability of creating genetically modified rats has been limited compared to that in the mouse mainly due to lack of genetic manipulation tools and technologies in the rat. Recent advances in nucleases, such as CRISPR/Cas9 (clustered regularly-interspaced short palindromic repeats/CRISPR associated protein 9), as well as TARGATT™ integrase system enables fast, efficient and site-specific introduction of exogenous genetic elements into the rat genome. Here, we report the generation of a collection of tissue-specific, inducible transgenic Cre rats as tool models using TARGATT™, CRISPR/Cas9 and random transgenic approach. More specifically, we generated Cre driver rat models that allow controlled gene expression or knockout (conditional models) both temporally and spatially through the Cre-ERT2/loxP system. A total of 10 Cre rat lines and one Cre reporter/test line were generated, including eight (8) Cre lines for neural specific and two (2) lines for cardiovascular specific Cre expression. All of these lines have been deposited with the Rat Resource and Research Center and provide a much-needed resource for the bio-medical community who employ rat models for their studies of human diseases.

Genome-Wide Conditional Association Study Reveals the Influences of Lifestyle Cofactors on Genetic Regulation of Body Surface Area in MESA Population

Backgrounds Body surface area (BSA) is an important trait used for many clinical purposes and is associated with a variety of diseases including cardiovascular diseases and cancer. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). Genome-wide association study of BSA was conducted on 5,336 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from MESA (The Multi-Ethnic Study of Atherocloris) data using unconditional and conditional full genetic models for analyzing genetic effects of additive, dominance, epistasis, and genetic by ethnicity interactions.Results Conditional association analyses revealed that lifestyle cofactors could affect the genetic effects of genes that regulate BSA. Moreover, impacts of the lifestyle cofactors on BSA could depend on the genotypes of several SNPs, and ethnicity of individuals. In this study, fifteen SNPs were identified with highly significant (Experiment-wise PEW < 1×10–5) genetic effects using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. Estimated heritabily was 72.88% using unconditional model and 74.85 ~ 79.87% using lifestyle cofactor-conditional models. It was revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes. From gene ontology analysis, it was observed that several genes are related to the metabolic pathway of calcium compounds, a main compound in several diseases related to obesity, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer.Conclusions In summary, our study provides novel insights into the genetic mechanism of BSA in MESA population, and influences of different lifestyle cofactors on the genetic effects of BSA associated loci.

Models of Multiple Events in the Analysis of Subsequent Registrations in the Labour Office

In many fields of science, it is necessary to analyse recurrent events. In medical science, the problem is to assess the risk of chronic disease recurrence. In economic and social sciences, it is possible to analyse the time of entering and leaving the sphere of poverty, the time of subsequent guarantee or insurance claims, as well as the time of subsequent periods of unemployment. In these studies, there are different ways of defining risk intervals, i.e. the time frame over which an event is at risk (or likely to occur) for an entity. Research on registered unemployment in Poland shows a high percentage of people returning to the labour office and registering again. The aim of the article is assessment of the risk of subsequent registrations in the labour office depending on selected characteristics of the unemployed: gender, age, education, and seniority. In the study, methods of survival analysis were used. The results obtained for four models being an extension of the Cox proportional hazard model were compared. The Anderson‑Gil model does not distinguish between first and next events. The number of events that occurred is important. Two Prentince‑Williams‑Peterson conditional models and the Wei, Lin and Weissfeld models are based on the Cox stratified model. The strata are consecutive events. They differ in the way risk intervals are determined. In the analysed period, only age and education influenced the risk of multiple registrations at the Poviat Labour Office in Szczecin. Gender and seniority did not have a significant impact on this risk. The analysis performed for subsequent registrations confirmed the impact of the same features on the first subsequent registration. In general, it can be stated that the analysed characteristics of the unemployed did not have a significant impact on the second and subsequent returns to the labour office.

Simultaneous modeling of Binary Responses: A sequence of binary models and Ordinal multinomial with Bayesian estimates impacting HIV/AIDS

Background: In this research, we examined several binary factors impact binary outcomes simultaneous and how the information of HIV/AIDS is perceived by the public is associated with outcomes to HIV/AIDS.Methods: We used polytomous responses through a sequence of binary models and a multinomial logistic regression model with Bayesian estimates to analyze the 2009 Mozambique survey data as it pertains to blood test, heard of HIV/AIDS and heard about campaign.Results: The analysis reveals that both heard about HIV and heard about the campaign are represented differentially in testing positive. Wealth, education and thinking of risk is positively associated with heard about HIV and heard about the campaign regardless of HIV. However, religious is a positive factor for social efforts of hearing of HIV/AIDS and the campaign. Both the polytomous response model and the ordinal model with model gave the same findings in regards to the marginal mean. However, the polytomous (conditional) models gave additional information about education.Conclusions: While knowledge of the disease continues to be important, the future social effort to combat HIV in Mozambique may need different strategies in different subpopulation groups.