FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

The economics of infertility

Infertility is a growing issue that affects thousands of Spanish couples; increasingly there are more and more people who cannot raise a family by themselves. Instead they have to resort to ART, adoption -local or international- or surrogacy abroad, a practice that many couples consider but that isn’t legalized in Spain. In this paper we have broken down each option in detail by analysing the market, differentiating its variants and specific steps, calculating its “price” and suggesting possible improvements that could be made from an economic perspective. We end our paper with the “SWOT” analysis of a hypothetical surrogacy market in Spain by calculating an estimated price, using the US as a reference, and the degree of acceptance it would have, based on the results of random surveys.

The Price of luck

We find that the vast majority of students taking an advanced undergraduate finance course show a preference for luck in a classroom experiment. In Phase I of the experiment part of the students, group A, were asked to guess a coin toss five times in a row. In Phase II the rest of the students, group B, were given 10 EUR to bet on some of the Group A students taking a second go at guessing a sequence of five coin tosses (Phase III). Group B students’ bets were by default allocated to the worse performing student in Phase I. Switching to better performing Group A students was costly. A total of 23 out of 28 students were willing to pay for switching and thus showed a preference for luck.