Leveraging pleiotropic association using sparse group variable selection in genomics data

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Pengaruh Citra Merek Dan Kelompok Referensi Terhadap Keputusan Pembelian Smartphone Vivo

Background - In Indonesia, there are various smartphone brands with attractive competition because vendors continue to compete to win consumers, but one of the vendors is ranked first in the market share in 2020, namely Vivo. therefore it is necessary to know how the brand image of vivo and whether a group can influence consumer choice. Purpose - The purpose of this study was to determine the influence of brand image and reference groups on purchasing decisions of Vivo smartphones on students of the Faculty of Economics, Uin Malang. Design/ Methodology/ Approach - This type of research is quantitative. The population of this study were students of the Faculty of Economics, UIN Malang. With a sample of 90 students. The analytical method used is multiple regression, while the instrument test uses a validity test, reliability test and classical assumption test with the help of SPSS 16.0 software for windows. Result and Discussion – Testing on the brand image variable (X1), the T-count value (2.385) is greater than T table (1.987) and the significance value (0.019) is less than alpha (0.05) which indicates there is a significant positive effect on purchasing decisions (Y). Testing on the Reference Group variable, the T-count (2.167) is more than the T-table (1.987) and the significance value (0.033 is less than alpha (0.05) which indicates there is a significant positive effect on purchasing decisions (Y). Based on the test results Simultaneously, the F-count value (9.114) is greater than the F-table (3.10) and the significance value (0.000) is smaller than alpha (0.05) which indicates the influence of the brand image variable (X1) and the reference group ( X2) simultaneously has a significant positive effect on purchasing decisions (Y). Conclusion – The results of the study showed that the variable brand image and reference group simultaneously had a significant positive effect on purchasing decisions. While the partial test results that brand image has a significant positive effect on purchasing decisions and the reference group variable has a significant positive effect on purchasing decisions.

Perilaku Konsumtif Mahasiswa dalam Membeli Produk Fashion

The student's decision to consume an item is also influenced by the student's personal characteristics. This research is a descriptive quantitative approach with survey techniques and online interviews using questionnaires filled out online using google form. There are five variables that are measured to determine the consumer behavior of students in buying fashion products, including: cultural factors, reference groups, roles and status, lifestyle, and learning. The population in this study were students of Economics Education, Faculty of Economics and Business, Makassar State University, batch 2019-2020, totaling 222 students. The sample amounted to 70 students. Based on the research data, it is known that the influence of cultural factors on the purchase of fashion products is 47.15%, the influence of reference group factors on the purchase of fashion products has a percentage of 72.62%, the influence of role and status factors has a percentage of 73.86%, the influence of style factors living has a percentage of 71.42%. And the influence of learning factors is 55.59%. So the role and status factors, reference groups, and lifestyle factors are more dominant that influence the consumer behavior of students in buying fashion products. Meanwhile, in simple linear regression analysis, the research results show that the research results prove that the variable that has a dominant influence on the fashion product purchase variable (Y) is the reference group variable (X2) where the regression coefficient value (β) is 0.368 it can be concluded that the reference group variable (X2) is the variable that has the most dominant influence on the variable purchasing fashion products (Y).

Divergent Selection on Height and Cognitive Ability: Evidence from Both Genetic Distance (Fst) and Polygenic Scores

Tests of selection based on population differentiation were performed on two highly polygenic traits important for success and quality of life: body height and educational attainment (EA). Polygenic scores (PGS) of EA and height, computed across three public genomic databases revealed differences between populations (1000 Genomes, HGDP, gnomAD) that matched the average IQ and height of ethnic groups (r ~0.9). A moderately strong correlation between latitude and EA PGS (r= 0.67) implies the effect of climate (seasonality or winter temperature) on selection for cognitive ability. The effect of latitude was reduced (β= 0.28) but remained significant after adding the sub-continental group variable to the regression model.The global Fst index revealed population differentiation at height and EA loci, significantly deviating from random SNPs. Substantial Linkage Equilibrium (LD) Decay between YRI and CEU was found (r= 0.6) but there was no correlation between LD decay and population differences in polygenic scores for EA (r= 0.015, p= 0.45), and slight inflation of height PGS difference due to LD decay (r= -0.04, p= 0.0315). Selecting the SNPs most robust to LD decay (r>0.8) resulted in larger PGS gaps for EA, but smaller for height. Finally, it is shown that PGS differences are more sensitive to the significance of GWAS loci than Fst, reflecting the major limitations of Fst as an index of selection.

Catechol-O-Methyltransferase Gene Val158Met Polymorphism Moderates the Effect of Social Exclusion and Inclusion on Aggression in Men: Findings From a Mixed Experimental Design

Accumulating research has identified the interactive effects of catechol-O-methyltransferase (COMT) gene Val158Met polymorphism and environmental factors on aggression. However, available evidence was mainly based upon correlational design, which yields mixed findings concerning who (Val vs. Met carriers) are more affected by environmental conditions and has been challenged for the low power of analyses on gene–environment interaction. Drawing on a mixed design, we scrutinized how COMT Val158Met polymorphism (between-group variable) impacts on aggression, assessed by hostility, aggressive motivation, and aggressive behavior, under different social conditions (exclusion vs. inclusion, within-group variable) in a sample of 70 Chinese male undergraduate students. We found that both Val/Val homozygote and Met alleles carriers showed differences in the feelings of hostility and aggressive motivation under conditions of exclusion versus inclusion, but these differences were more pronounced for Met allele carriers. These findings implied that COMT Val158Met polymorphism did not respond to environmental stimuli in an all-or-none way and shed light on the importance of examining the gene–environment interaction using a mixed design.

Multigroup Segregation Analyses with Covariates

The author introduces methods for the decomposition analysis of multigroup segregation measured by the index of dissimilarity, the squared coefficient of variation, and Theil’s entropy measure. Using a new causal framework, the author takes a unified approach to the decomposition analysis by specifying conditions that must be satisfied to decompose segregation into unexplained and explained components. Here, the unexplained component represents the direct effects of the group variable on the conditional probability of acquiring a social position—such as a residential district in an analysis of residential segregation or an occupation in an analysis of occupational segregation—and the explained component represents indirect effects of the group variable on the outcome through covariates. The major merit of this approach is its ability to control individual-level covariates for the decomposition analysis of segregation. Two methods, one for semiparametric outcome models with the identity link function and the other for semiparametric outcome models with the multinomial logit link function, are introduced in this unified framework. The application of these methods focuses on occupational segregation among racial/ethnic groups. Father’s occupation, subject’s educational attainment, and the region of interview are included as covariates, using data from the General Social Surveys.

Ethnicity and Household Savings in Indonesia

Household saving is very important, not only for securing the future spending of the family but also for the country's economy. Using the logit analysis on data of The Indonesia Family Life Survey (IFLS) wave three, four, and five, this chapter analyzes factors influencing, especially the role of ethnicity, upon the household savings in Indonesia. The result indicates that gender, location, and level of education are the consistent variables affecting the household saving behaviour in Indonesia across the three wave surveys. Meanwhile, as for the ethnic group variable, there are only Sunda, Batak, and Bima-Dompu that can significantly influence the people's saving behavior across the three wave surveys.