Regarding the F-word: The effects of data filtering on inferred genotype-environment associations.

Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to best filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results, negatively affecting management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for assessment of adaptation to environment. We use empirical and simulated data sets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the data set, lessening the power to detect adaptive variants (i.e., simulated true positives) with strong and weak strengths of selection. Regardless, strength of selection was a good predictor for GEA detection, but even some SNPs under strong selection went undetected. False positive rates varied depending on the species and GEA method, and filtering significantly impacted the predictions of adaptive capacity in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending on the study system, availability of genomic resources, and desired objectives.

The minority report: Hazard reporting in an Australian University.

BACKGROUND: Understanding what influences reporting is critical to underpinning the knowledge base around reporting behaviour and assisting in developing effective strategies to increase reporting levels within an organisation. Universities should investigate reporting behaviour in their own organisation and investigate why differences exist between different job profiles. OBJECTIVE: The aim of this study was to investigate hazard reporting in an Australian University and to assess what factors (employment type, level of safety responsibility and prior injury) influence reporting behaviour. METHODS: A total of 256 university employees and postgraduate students completed a survey on their hazard reporting. RESULTS: The study indicated that demographic variables such as department type, role in the organisation, level of safety activity in role, and prior injury effected the reporting of hazards. Issues for non-reporting were also found to support findings in other studies. A unique finding was that while teaching-focused academics were not more likely to have been injured at work they were more likely to have reported a hazard, while researchers were less likely to report despite not being more or less likely to have received an injury. CONCLUSIONS: Although this study indicates there are differences in an organisations hazard reporting based on demographics and prior injury occurrence further research is required to evaluate the impact across other organisations and sectors.

Tight control of disease activity fails to improve body composition or physical function in rheumatoid arthritis patients.

OBJECTIVE: RA typically features rheumatoid cachexia [loss of muscle mass (MM) and excessive total fat mass (TFM), especially trunk FM], which contributes to physical disability. Since rheumatoid cachexia is driven by inflammation, it would be anticipated that the success of tight control of disease activity, such as treat-to-target (T2T), in attenuating inflammation would benefit body composition and physical function. This aim of this cross-sectional study was to assess the impact of T2T on body composition and objectively assessed function in RA patients. METHODS: A total of 82 RA patients exclusively treated by T2T, were compared with 85 matched sedentary healthy controls (HCs). Body composition was estimated by DXA, with appendicular lean mass the surrogate measure of total MM. Physical function was assessed by knee extensor strength, handgrip strength, 30 s sit-to-stands, 8' up and go, and 50' walk (tests which reflect the ability to perform activities of daily living). RESULTS: Although generally well treated (mean DAS28 = 2.8, with 49% in remission), RA patients had ∼10% proportionally less appendicular lean mass and were considerably fatter (by ∼27%), particularly in the trunk (∼32%), than HCs. All measures of function were 24-34% poorer in the RA patients relative to HC. CONCLUSIONS: Despite marked improvements in disease control (most patients achieving or approaching remission), the relative loss of MM and increased adiposity in RA patients compared with matched HCs was similar to that observed pre-T2T. Additionally, performance of objective function tests was unchanged from that reported by our group for pre-T2T RA patients. Thus T2T, even in responsive RA patients, did not attenuate rheumatoid cachexia or improve objectively assessed function.

Afghanistan's ethnic groups share a Y-chromosomal heritage structured by historical events.

Afghanistan has held a strategic position throughout history. It has been inhabited since the Paleolithic and later became a crossroad for expanding civilizations and empires. Afghanistan's location, history, and diverse ethnic groups present a unique opportunity to explore how nations and ethnic groups emerged, and how major cultural evolutions and technological developments in human history have influenced modern population structures. In this study we have analyzed, for the first time, the four major ethnic groups in present-day Afghanistan: Hazara, Pashtun, Tajik, and Uzbek, using 52 binary markers and 19 short tandem repeats on the non-recombinant segment of the Y-chromosome. A total of 204 Afghan samples were investigated along with more than 8,500 samples from surrounding populations important to Afghanistan's history through migrations and conquests, including Iranians, Greeks, Indians, Middle Easterners, East Europeans, and East Asians. Our results suggest that all current Afghans largely share a heritage derived from a common unstructured ancestral population that could have emerged during the Neolithic revolution and the formation of the first farming communities. Our results also indicate that inter-Afghan differentiation started during the Bronze Age, probably driven by the formation of the first civilizations in the region. Later migrations and invasions into the region have been assimilated differentially among the ethnic groups, increasing inter-population genetic differences, and giving the Afghans a unique genetic diversity in Central Asia.