The BOLT-LMM software package computes statistics for association between phenotype and genotypes using a linear mixed model (LMM) [1]. By default, BOLT-LMM assumes a Bayesian mixture-of-normals prior for the random effect attributed to SNPs other than the one being tested. This model generalizes the standard "infinitesimal" mixed model used by existing mixed model association methods (e.g., EMMAX [2], FaST-LMM [3-6], GEMMA [7], GRAMMAR-Gamma [8], GCTA-LOCO [9]), providing an opportunity for increased power to detect associations while controlling false positives. Additionally, BOLT-LMM applies algorithmic advances to compute association statistics much faster than existing methods, both when using the Bayesian mixture model and when specialized to standard mixed model association.

BOLT-LMM v1.0 User Manual

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany.

The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G). The sample includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 triads, which overlap with the parent-offspring pairs. Leveraging the results from well-powered genome-wide association studies, we created a repository comprising 66 polygenic indices (PGIs) in the SOEP-G sample. We show that the PGIs for height, BMI, and educational attainment capture 22~24%, 12~13%, and 9% of the variance in the respective phenotypes. Using the PGIs for height and BMI, we demonstrate that the considerable increase in average height and the decrease in average BMI in more recent birth cohorts cannot be attributed to genetic shifts within the German population or to age effects alone. These findings suggest an important role of improved environmental conditions in driving these changes. Furthermore, we show that higher values in the PGIs for educational attainment and the highest math class are associated with better self-rated health, illustrating complex relationships between genetics, cognition, behavior, socio-economic status, and health. In summary, the SOEP-G data and the PGI repository we created provide a valuable resource for studying individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and the environment.

Cohort Profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G)

Machine learning has become a pivotal tool in various fields, revolutionizing industries and enabling innovative solutions. This manuscript explores the fundamentals of machine learning, its types, and common algorithms, focusing on supervised, unsupervised, and reinforcement learning. Furthermore, it discusses the impact of machine learning on healthcare, finance, and education, providing case studies that highlight its real-world applications.

Exploring the Wonders of Machine Learning

"Function calls as citations" refers to the ability to compose a novel research object by re-using an existing component, such as code or data, from a prior research object stored in an immutable and verifiable way. This idea originates from Balaji Srinivasan, who recently issued it as a challenge to builders in the DeSci space. In this preliminary report, we answer this challenge with a proof of concept. We created two Research Objects stored on an Open State Repository, each containing code components. A new Research Object calls functions from the code components of another Research Object. Both Research Objects are stored on an Open State Repository, and interoperate using decentralized persistent identifiers.

test

We examine whether having previously been self-employed is a negative signal on the job market. In a UK field experiment where two applications of otherwise equally qualified individuals were sent out in response to the same vacancies in human resource management, we find that entrepreneurs systematically receive fewer responses than non-entrepreneurs. Empirical studies that treat market wages as the opportunity cost of remaining self-employed are therefore likely to overestimate alternative earnings to entrepreneurship.

Self-Employed but Looking: A Labor Market Experiment

Machine agents are transforming the way we interact with information. The dPID (Decentralized Persistent Identifier) is designed to support machine actionability while adhering to the FAIR principles. It guarantees global uniqueness, an immutable designation to their mapped resource, and consistent resolution to their (meta)data for both humans and machines.

dPID: A Persistent Identifier for Machine Actionability

We conducted a genome-wide association study on income among individuals of European descent (N=668,288) to investigate the relationship between socioeconomic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our polygenic index captures 1-5% of income variance, with only one-fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma, and back pain. The income factor had a substantial genetic correlation (0.92, s.e.=0.006) with educational attainment (EA). Accounting for EA's genetic overlap with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.