Setting Up the Stillbirth Registration System and Investigating the Related Causes in Iran in Selected Hospitals: Observational Cohort Study

Background One of the major causes of perinatal mortality is stillbirth. In many cases, the cause of stillbirth is difficult to identify, and the cause of many cases remain unexplained. Because of the lack of registration stillbirth system in our country we developed protocol and instructions for stillbirth and setting up a stillbirth registration system in selected hospitals around country. Methods Iranian Maternal and Neonatal Network (IMaN) registers information about almost all births (live & dead) around the country, but this network does not collect data about stillbirth causes. In this study, we developed the stillbirth evaluation protocol with experts' cooperation, and we designed forms for the stillbirth registration system electronically. Then we trained related individuals in 14 selected hospital from 12 provinces (14 cities) of Iran. After a year, we extracted, analyzed, and, based on the Relevant Condition of Death Classification (ReCoDe), interpreted the collected data. Results A total of 105,562 births and 762 stillbirths registered. In 742 registered stillbirth cases in 14 selected hospitals, the relevant causes were identified in 65.4% of cases, while 34.6% of cases remained unclassified. The most frequent relevant conditions were fetal (33.2%), maternal (9.1%), amniotic fluid (8.8%), placenta (7.7%), and umbilical cord (6.2%). Conclusions Our registration decreased the percentage of stillbirth with an unexplained cause from about 70–34.6%.

Evidence for third-party mediation but not punishment in Mentawai justice

Researchers argue that third-party involvement is critical for sustaining human cooperation, yet how third parties contribute remains unclear, especially in small-scale, politically decentralized societies. In a study of wrongdoing and punishment among the Mentawai horticulturalists of Indonesia, we test two hypotheses of third-party involvement: punishment and mediation. From a sample of 444 transgressions, most of which were followed by the payment of a fine (usually in pigs, durian trees, etc.), we find no evidence of third-party punishment. Victims or aggrieved family members demanded fines, and if an aggressor was punished for failing to pay, punishment was always imposed by the victim or an aggrieved party and never by third parties. We also find little evidence of indirect sanctions by third parties. Nearly 20% of transgressions were followed by no punishment, and as predicted by dyadic models of punishment, punishment was less likely when transgressions were among related individuals. At the same time, third parties—especially shamans and elders—were often called as mediators, and mediators were called more as cooperation was threatened. Moreover, government officials appear to fill similar roles as community mediators, demonstrating how governmental intervention might contribute to the decline of local leadership institutions. These findings suggest that, among the Mentawai, institutionalized punishment functions more to restore dyadic cooperation than to enforce norms.

Emergent Myxobacterial Behaviors Arise from Reversal Suppression Induced by Kin Contacts

In many species, large populations exhibit emergent behaviors whereby all related individuals move in unison. For example, fish in schools can all dart in one direction simultaneously to avoid a predator.

NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses

Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness can not be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to large datasets. NAToRA outperformed two popular methodologies (implemented in software PLINK and KING) by showing the best combination of effective relatedness-pruning, removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar or lesser reduction in genetic diversity. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also present a genealogies simulator software used for different tests performed in the manuscript.

The flashfm approach for fine-mapping multiple quantitative traits

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

On the Use of Z-Scores for Fine-Mapping with Related Individuals

Using summary statistics from genome-wide association studies (GWAS) has been widely used for fine-mapping complex traits in humans. The statistical framework was largely developed for unrelated samples. Though it is possible to apply the framework to fine-mapping with related individuals, extensive modifications are needed. Unfortunately, this has often been ignored in summary-statistics-based fine-mapping with related individuals. In this paper, we show in theory and simulation what modifications are necessary to extend the use of summary statistics to related individuals. The analysis also demonstrates that though existing summary-statistics-based fine-mapping methods can be adapted for related individuals, they appear to have no computational advantage over individual-data-based methods.

Inferring the mode and strength of ongoing selection

Genome sequence data is no longer scarce. The UK Biobank alone comprises 200,000 individual genomes, with more on the way, leading the field of human genetics towards sequencing entire populations. Within the next decades, other model organisms will follow suit, especially domesticated species such as crops and livestock. Having sequences from most individuals in a population will present new challenges for using these data to improve health and agriculture in the pursuit of a sustainable future. Existing population genetic methods are designed to model hundreds of randomly sampled sequences, but are not optimized for extracting the information contained in the larger and richer datasets that are beginning to emerge, with thousands of closely related individuals. Here we develop a new method called TIDES (Trio-based Inference of Dominance and Selection) that uses data from tens of thousands of family trios to make inferences about natural selection acting in a single generation. TIDES further improves on the state-of-the-art by making no assumptions regarding demography, linkage or dominance. We discuss how our method paves the way for studying natural selection from new angles.

Non-kin Cooperation in Ants

Eusociality represents an extreme form of social behavior characterized by a reproductive division of labor. Eusociality necessarily evolved through kin selection, which requires interactions among related individuals. However, many eusocial taxa also show cooperation between non-kin groups, challenging the idea that cooperative actions should only occur among relatives. This review explores the causes and consequences of non-kin cooperation in ants. Ants display a diversity of behaviors that lead to non-kin cooperation within and between species. These interactions occur among both reproductive and non-reproductive individuals. The proximate and ultimate mechanisms leading to non-kin cooperative interactions differ substantially depending on the biotic and abiotic environment. We end this review with directions for future research and suggest that the investigation of non-kin cooperative actions provides insight into processes leading to social evolution.

COPILOT: a Containerised wOrkflow for Processing ILlumina genOtyping daTa

Background: The Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses. This process can be time-consuming, lead to reproducibility errors, and be a daunting task for novice bioinformaticians. Results: Here we introduce the COPILOT (Containerised wOrkflow for Processing ILlumina genOtyping daTa) protocol, which provides an in-depth and clear guide to process raw Illumina genotype data in GenomeStudio, followed by a containerised workflow to automate an array of complex bioinformatics analyses involved in a GWAS quality control (QC). The COPILOT protocol was applied to two independent cohorts consisting of 2791 and 479 samples genotyped on the Infinium Global Screening (GSA) array with Multi-disease (MD) drop-in (~750,000 markers) and the Infinium H3Africa consortium array (~2,200,000 markers) respectively. Following the COPILOT protocol, an average sample quality improvement of 1.24% was observed across sample call rates, with notable improvement for low-quality samples. For example, from the 3270 samples processed, 141 samples had an initial sample call rate below 98%, averaging 96.6% (95% CI 95.6-97.7%), which is considered below the acceptable sample call rate threshold for a typical GWAS analysis. However, following the COPILOT protocol, all 141 samples had a call rate above 98% after QC and averaged 99.6% (95% CI 99.5-99.7%). In addition, the COPILOT pipeline automatically identified potential data issues, including gender discrepancies, heterozygosity outliers, related individuals, and population outliers through ancestry estimation. Conclusions: The COPILOT protocol makes processing Illumina genotyping data transparent, effortless and reproducible. The container is deployable on multiple platforms, improves data quality, and the end product is analysis-ready PLINK formatted data, with a comprehensive and interactive summary report to guide the user for further data analyses.

Kin discrimination in allelopathy and consequences for agriculture

Recent research has shown that plants can distinguish genetically-related individuals from strangers (kin recognition) and exhibit more cooperative behaviours towards these more related individuals (kin discrimination). The first evidence for this was found when Cakile edentula plants growing with half-sibs allocated relatively less biomass to roots than plants growing with unrelated individuals, indicating that kin recognition can reduce the intensity of competition (Dudley & File, 2007). Since then, kin discrimination has been shown to result in reduced competition for soil resources (Semchenko, Saar, & Lepik, 2014), light (Crepy & Casal, 2015) and pollinators (Torices, Gómez, & Pannell, 2018). On the other hand, allelopathy, plants producing chemical compounds that negatively affect performance of neighbour plants, has also been widely documented (Inderjit & Duke, 2003) and shown to profoundly affect local species coexistence and plant community structure (Meiners, Kong, Ladwig, Pisula, & Lang, 2012). In crops allelopathy can also be beneficial in suppressing weeds (Macías, Mejías, & Molinillo, 2019). In the current issue, Xu, Cheng, Kong, and Meiners (2021) published the first study to show that kin discrimination can also affect the balance between direct competition for resources and allelopathy, and this together may lead to improved weed suppression in rice.