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Background Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available. Results To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package Conclusions We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z ) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.

We construct a comprehensive database of educational quality by cohort for 92 countries from 1970 to 2015 and analyze its impact on disparities in income and growth worldwide. To estimate educational quality, we utilize secondary students’ scores on international mathematics and science tests. Additionally, we impute unobserved test scores for individual countries in non-participating survey years. Wage regressions using individual earnings data reveal considerable returns to educational quality. We estimate human capital stock by incorporating differences in educational quantity and quality by age group across countries and over time. Our newly-constructed human capital dataset enabled us to explore the role of educational quality and human capital in understanding cross-country income disparities. The findings from development and growth accounting exercises indicated a discernible contribution of educational quality to per capita income and its growth rate.

This paper formulates and estimates multistage production functions for children's cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stocks of skills in that period to assess the benefits of early investment in children compared to later remediation. We establish nonparametric identification of a general class of production technologies based on nonlinear factor models with endogenous inputs. A by-product of our approach is a framework for evaluating childhood and schooling interventions that does not rely on arbitrarily scaled test scores as outputs and recognizes the differential effects of the same bundle of skills in different tasks. Using the estimated technology, we determine optimal targeting of interventions to children with different parental and personal birth endowments. Substitutability decreases in later stages of the life cycle in the production of cognitive skills. It is roughly constant across stages of the life cycle in the production of noncognitive skills. This finding has important implications for the design of policies that target the disadvantaged. For most configurations of disadvantage it is optimal to invest relatively more in the early stages of childhood than in later stages.

Black-white test score gaps form in early childhood and widen over elementary school. Sociologists have debated the roles that socioeconomic status (SES) and school quality play in explaining these patterns. In this study, I replicate and extend past research using new nationally representative data from the Early Childhood Longitudinal Study-Kindergarten Class of 2010-2011. I find black-white test score gaps at kindergarten entry in 2010 in reading (SD = .32), math (SD = .54), and working memory (SD = .52 among children with valid scores). Math and reading gaps widened by approximately .06 standard deviations over kindergarten, but the working memory gap was constant. Multivariate regressions show that student SES explained the reading gap at school entry, but gap decompositions suggest that school quality differences were responsible for the widening of the reading gap over kindergarten. SES explained much of the math gap at school entry, but the widening of the math gap could not be explained by SES, school quality, or other hypotheses.

Introduction The ever-evolving landscape of medical science necessitates a curriculum that fosters specialized knowledge and expertise. Electives in Biochemistry offer a pivotal opportunity for medical students to explore the intricacies of biochemical processes, diagnostic technologies, and therapeutic interventions. By delving into specific areas of biochemistry, students can develop a nuanced understanding of the molecular mechanisms underlying human health and disease. The present study emphasised a newly adopted elective module schedule designed by department of biochemistry, at the National Institute of Medical Sciences and Research and also the present study assessed the insights and performance of medical students following elective postings. Materials and methods A combined qualitative and quantitative observational study was conducted in the Department of Biochemistry, NIMS & R, Jaipur. The study involved 45 students who are in phase III, part I of the MBBS Program in the Biochemistry Department. The students were divided into three small groups BE1, BE2 and BE3, with 15 students in each group, selected via a randomized lottery system. A newly adopted elective module was implemented, a pre-test and post-test were conducted and the scores obtained were compared for statistical significance. The students insights into electives were gathered via open-ended and closed ended questions. A Likert scale was used for scoring the closed -ended questions. Results All the Participants were assessed with the pre and post-test survey which assessed their overall impression of the elective course. Compared to pre-test results, the post-test demonstrates an improved impression of electives which was statistically significant ( p  < 0.0001). A total of 33.33% of the students had pre-test scores < 40% and none of the students had < 40% in the post-test. The mean scores obtained in the post-test were significantly higher than mean pre-test scores ( p  < 0.001). A total of 82.3% strongly agree that the newly adopted electives help in understanding the importance of Biochemistry, 73.33% strongly agreed that electives increased critical thinking and, 77.78% strongly agreed that electives increased the interest in the topic, 68.89% strongly agreed that time allocated for electives was adequate, 66.67% strongly agreed that electives enhance the knowledge in the subject, 64.45% strongly agree that electives encourage the understanding of the applied aspect of the topic, 82.3% strongly agreed that electives enhanced interest in the research project. Furthermore, 62.22% strongly agreed that electives encouraged them to pursue postgraduate programs in Biochemistry. Conclusion The introduction of the elective module in biochemistry has yielded promising results, fostering a deeper understanding and appreciation of the subject among medical students. The significant improvement in students’ performance and enthusiasm is a test of the module’s effectiveness. Ultimately, this initiative has the potential to nurture a new generation of medical professionals with a strong foundation in biochemistry.

Background Residential instability and neighborhood conditions may shape children’s health and development, but it is unclear whether all residential moves are equally destabilizing, and the extent to which moving to neighborhoods with different conditions can improve children’s outcomes. Most studies estimating causal effects of these factors on children’s health or development use smaller, geographically constrained, urban cohorts. Objective In a racially/ethnically and socioeconomically diverse statewide cohort including urban and rural communities, we investigate effects of residential instability, neighborhood deprivation, and their intersection on childhood educational outcomes. Methods We construct a statewide dataset that links North Carolina birth records (2002–2005) with lead testing data (2003–2015) and 4th grade standardized test scores (2013–2016). A composite census tract-level neighborhood deprivation index (NDI) is linked with individuals based on residence at birth, lead testing, and 4th grade. Outcomes of interest are 4th grade test scores in reading and mathematics. We use multinomial propensity scores to estimate effects of residential instability and neighborhood deprivation on test scores. Results Children who moved between only high deprivation neighborhoods had lower reading test scores (-0.29 [95% CI: -0.59, -0.015]) compared to children who resided in high deprivation neighborhoods but did not move. Children who resided in a high deprivation neighborhood at birth and subsequently moved to a low deprivation neighborhood(s) had higher test scores compared to those who moved between only high deprivation neighborhoods (1.59 [0.90, 2.28]). Additionally, children who move from high to low deprivation neighborhoods earlier had larger improvements. Conclusion Being residentially stable, even while residing in a high deprivation neighborhood, is associated with improved educational outcomes. However, there is also a larger positive effect of moving from high to low deprivation neighborhoods. Our findings have important implications, particularly given the increasing segregation of neighborhoods by socioeconomic status and the housing affordability crisis in the United States. Partnerships between housing programs, early childhood education and services, and health care providers, which address evictions and broader issues, may help address health inequalities rooted in childhood exposures and experiences.

This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.

Using longitudinal data from nearly all U.S. public school districts, I estimated average learning rates (within cohort change) and improvement rates (between cohort change) in mathematics for Black students using standardized test scores. Results reveal substantial district-level variation. In about 11% of districts reporting Black student outcomes, Black student learning rates exceeded the national average for all students. Around 47% of districts showed above-average improvement in Black student achievement. However, only 3% of districts had mean Black student achievement exceeding the national average for all students. These findings highlight wide variability in Black student success across districts and suggest a need to examine how districts can better structure themselves to support widespread academic success.

Because it is often impractical or impossible to obtain school transcripts or records on subjects, many researchers rely on college students to accurately self-report their academic record as part of their data collection procedures. The purpose of this study is to investigate the validity and reliability of student self-reported academic performance. As expected the study finds overall validity of self-reported test scores to be high. However, correlations between self-reported and actual SAT scores are significantly lower than correlations for self-reported and actual ACT Composite scores. This study also confirms prior research which found that when students are inaccurate in reporting their scores, a disproportionate number of them over-report their scores. Also consistent with other studies, this study finds that lower achieving students for both tests are much less accurate when reporting their scores.

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance. •Meeting the pressing need for more powerful analysis of multivariate neuroimaging traits•Introducing to the neuroimaging community some recently proposed association tests•Developing new, more powerful and versatile association tests•Establishing connections among the existing and new association tests•Demonstrating the use and performance of the methods with simulated and the ADNI data