Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

A familial longitudinal count data study

textIn this report, I study familial longitudinal count data with a Poisson regression model. The data is collected from individuals who are nested in families. I focus on two main issues to fit a model. The first one is the large number of excess zeros and the second one is multi-level random effects. My approach for solving these problems are to use either Zero Inflated Poisson (ZIP) or Negative Binomial (NB) models to control for the excess zeros which allow for estimation of another parameter for over dispersion while developing the model with individual and familial random effects. First, I use a Poisson regression model with only main effects. After that, I fit a ZIP model to control for the extra zeros. I provide information about general form of the exponential families and a discussion about the dispersion parameter. I also fit a Negative Binomial model instead of the ZIP model. I also build these models with only individual random effects and with both individual and familial random effects as well. I discuss the generalized estimating equation (GEE) approach to estimate the parameters of a generalized linear model with auto regressive correlation between outcomes.Statistic

On the estimation of penetrance in the presence of competing risks with family data

In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the penetrance function under high familial correlation. However the competing risks model without frailty provided a good alternative under low familial correlation. We illustrate the proposed model using Colon Cancer Family Registry data

Model consent clauses for rare disease research

Background: Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants' overall protection. Methods: A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses. Results: The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits. Conclusion: The model consent clauses presented in this article have been drafted to highlight consent elements that bear in mind the trends in rare disease research, while providing a tool to help foster harmonization and collaborative efforts

Mutant MYO1F alters the mitochondrial network and induces tumor proliferation in thyroid cancer

Familial aggregation is a significant risk factor for the development of thyroid cancer and Familial Non-Medullary Thyroid Cancer (FNMTC) accounts for 5-7% of all NMTC. Whole Exome Sequencing analysis in the family affected by FNMTC with oncocytic features where our group previously identified a predisposing locus on chromosome 19p13.2, revealed a novel heterozygous mutation (c.400G>A, NM_012335; p.Gly134Ser) in exon 5 of MYO1F, mapping to the linkage locus. In the thyroid FRTL-5 cell model stably expressing the mutant MYO1F p.Gly134Ser protein we observed an altered mitochondrial network, with increased mitochondrial mass and a significant increase of both intracellular and extracellular Reactive Oxygen Species, compared to cells expressing the wild-type protein or carrying the empty vector. The mutation conferred a significant advantage in colony formation, invasion and anchorage independent growth. These data were corroborated by in vivo studies in zebrafish, since we demonstrated that the mutant MYO1F p.Gly134Ser, when overexpressed, can induce proliferation in whole vertebrate embryos, compared to the wild-type one. MYO1F screening in additional 192 FNMTC families identified another variant in exon 7, which leads to exon skipping, and is predicted to alter the ATP-binding domain in MYO1F. Our study identified for the first time a role for MYO1F in NMTC. This article is protected by copyright. All rights reserved

Culture and Income across Countries: Evidence from Family Ties,

Purpose – The purpose of the paper is to examine how much difference in income can be explained by familial culture that persists in different societies. Design/methodology/approach – We employ a two-step methodology to evaluate the impact of familial culture on income across countries. In the first step, we construct the macro measures of familial culture from micro survey data. In the second step, the growth model is estimated.Findings – First-step micro regression results show that family is more important to female, richer, highly educated, unemployed and married individuals. Male, poorer, less educated and unemployed individuals are more likely to respect and love parents unconditionally. The same group is also more likely to think that parents must do the best for their kids. Finally, the macro results show that the strength of national familial ties explains significant differences in income across countries. Research limitations/implications – We show that countries with weak family ties are richer than those with strong family ties. These results are useful for policymakers who design public policies that accommodate the type of familial culture that persists in their society. Originality/value – We construct the macro measures of familial culture from the micro survey data. The paper adds to the literature on the effect of culture on income at the macro level

Is Extended Family in Low-Income Countries Altruistically Linked?

Using a micro data from Bangladesh, this paper tests whether an extended family in low-income countries is altruistically linked. Based on theoretical implications of altruism model, the paper tests whether interhousehold transfer is negatively related to the recipient's income and whether consumption of each of related households is uncorrelated with its own income, controlling for pooled income of the family. Test results do not support altruism as the basis for familial economic ties in low-income countries. We fail to reject that transfer from father, child, or sibling is uncorrelated to the recipient's income or wealth in most cases; and households' non-food consumption is estimated to be strongly correlated with their own income and wealth, even after related households' pooled income is controlled for.altruism, interhousehold transfer, dynasty model, low-income country

Application of the disposition model to breast cancer data

In this paper, we have presented the second level nesting of Bonney's disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox's regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox's model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox's model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis. --Second level nesting,Proportional hazards model,Quadratic exponential form,Partial likelihood,Familial aggregation,Second-order methods,Marginal models,Conditional models