Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model

Background The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model. Methods We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization’s cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05. Result The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4–59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14–1.39), wasting (OR = 1.35, 95% BCI (1.15–1.57), maternal anemia (OR = 1.61, 95% BCI (1.44–1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08–1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62–0.85). Conclusion Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.

A fast and robust strategy to remove variant level artifacts in Alzheimer's Disease Sequencing Project data

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer's disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer's Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. In order to achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing kits. This approach may lead to variable variant quality across sequencing centers and/or kits. Here, we performed exome-wide and genome-wide association analyses on AD risk using the latest ADSP WES and WGS data releases. We observed that many variants displayed large variation in allele frequencies across sequencing centers/kits and contributed to spurious association signals with AD risk. We also observed that sequencing kit/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented novel filters that aim to capture and remove these center/kit-specific artifactual variants. We conclude by deriving a novel, fast, and robust approach to filter variants that represent sequencing center- or kit-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs.

Metacommunity dynamics and the spurious detection of species associations in co-occurrence analyses: patch disturbance matters

Many methods attempt to detect species associations from co-occurrence patterns. Such associations are then typically used to infer inter-specific interactions. However, correlation is not equivalent to interaction. Habitat heterogeneity and out-of-equilibrium colonization histories are acknowledged to cause species associations even when inter-specific interactions are absent. Here we show how classical metacommunity dynamics, within a homogeneous habitat at equilibrium, can also lead to statistical associations. This occurs even when species do not interact. All that is required is patch disturbance (i.e. simultaneous extinction of several species in a patch) a common phenomenon in a wide range of real systems. We compare direct tests of pairwise independence, matrix permutation approaches and joint species distribution modelling. We use mathematical analysis and example simulations to show that patch disturbance leads all these methods to produce characteristic signatures of spurious association from "null" co-occurrence matrices. Including patch age (i.e. the time since the last patch disturbance event) as a covariate is necessary to resolve this artefact. However, this would require data that very often are not available in practice for these types of analyses. We contend that patch disturbance is a key (but hitherto overlooked) factor which must be accounted for when analysing species co-occurrence.

Speech Acts and Performative Utterances

Speech Act Theory is the application to spoken and written language of the philosophy of action developed by John L. Austin. Austin was particularly interested in conventionalized actions, which have a special significance thanks to their social or institutional context. Although he emphasized that such actions could also be carried out through non-verbal means, Austin is mostly remembered for his analysis of the ways in which they can be carried out through the utterance of words—hence the term “Speech Act Theory,” and the title under which his lecture series on the topic was posthumously published (i.e., How to Do Things with Words). He described utterances that perform such actions as “performative utterances.” But he also effectively argued that all utterances are performative—or rather, that all utterances have a performative or “illocutionary” aspect. Austin’s analysis of speech as action provides scholars with a way of looking at verbal behavior that relates spoken and written utterances to the circumstances of their production and deployment without reducing their meanings to authorial intentions conceived as mental states. As such, it has intrinsic appeal to scholars of literature, who have since the 1970s often distanced themselves both from psychological and from purely formal conceptions of literature. However, engagements with Speech Act Theory by literary and cultural theorists have often been superficial (for example, in the commonplace but spurious association of Austin’s account of performative utterances with the unrelated idea that gender is performative). Indeed, the fundamental concepts of Speech Act Theory have usually been misunderstood and misrepresented within literary studies because its core concerns are quite alien to that discipline’s central preoccupation: that is, the critical interpretation of literary texts.

A pseudolikelihood approach for assessing genetic association in case–control studies with unmeasured population structure

The case–control study design is one of the main tools for detecting associations between genetic markers and diseases. It is well known that population substructure can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we propose a novel statistical method to estimate the association in case–control studies with unmeasured population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer the population substructure; second, the association between the disease and the test marker adjusting for the population substructure is modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to the existing methods, on bias, coverage probability and computational time, is assessed through simulations. The method is applied to an end-stage renal disease study in African Americans population.

Association of EGLN1 gene with high aerobic capacity of Peruvian Quechua at high altitude

Highland native Andeans have resided at altitude for millennia. They display high aerobic capacity (VO2max) at altitude, which may be a reflection of genetic adaptation to hypoxia. Previous genomewide (GW) scans for natural selection have nominated Egl-9 homolog 1 gene (EGLN1) as a candidate gene. The encoded protein, EGLN1/PHD2, is an O2 sensor that controls levels of the Hypoxia Inducible Factor-α (HIF-α), which regulates the cellular response to hypoxia. From GW association and analysis of covariance performed on a total sample of 429 Peruvian Quechua and 94 US lowland referents, we identified 5 EGLN1 SNPs associated with higher VO2max (L⋅min−1 and mL⋅min−1⋅kg−1) in hypoxia (rs1769793, rs2064766, rs2437150, rs2491403, rs479200). For 4 of these SNPs, Quechua had the highest frequency of the advantageous (high VO2max) allele compared with 25 diverse lowland comparison populations from the 1000 Genomes Project. Genotype effects were substantial, with high versus low VO2max genotype categories differing by ∼11% (e.g., for rs1769793 SNP genotype TT = 34.2 mL⋅min−1⋅kg−1 vs. CC = 30.5 mL⋅min−1⋅kg−1). To guard against spurious association, we controlled for population stratification. Findings were replicated for EGLN1 SNP rs1769793 in an independent Andean sample collected in 2002. These findings contextualize previous reports of natural selection at EGLN1 in Andeans, and support the hypothesis that natural selection has increased the frequency of an EGLN1 causal variant that enhances O2 delivery or use during exercise at altitude in Peruvian Quechua.