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When introduced species hybridize with native relatives, spread of advantageous invasive genes into native populations (introgression) is a conservation concern. Genome-scale SNP (single nucleotide polymorphism) analysis can be a powerful approach to detect hybridization and identify candidate loci experiencing selection in hybrid zones. However, followup studies are critical to verify and interpret potentially impactful patterns of introgression. In an earlier publication we identified three outlier loci (out of 68 unlinked SNPs) where non-native alleles appeared to have introgressed 90 km into the range of a threatened native salamander, while the other 65 markers showed no evidence of spread further than 12 km. This was consistent with strong selection favoring a few invasive traits, but our inferences necessarily depended on limited reference samples of the native species. Here, we further tested our initial interpretation by interrogating the outlier markers in greater detail. First, we isolated DNA from two museum specimens of native salamanders collected several decades before the introduction. Both had the putatively invasive SNPs, indicating that the SNP alleles were present before the introduction and therefore not diagnostic for nonnative ancestry. Second, we developed a novel genealogical analysis of DNA sequences (rather than SNPs) to infer allelic ancestry, since genealogical analysis of haplotypes minimizes the ancestry assignment errors that can occur with SNPs. When applied to the original loci, this analysis confirmed that the genotypes formerly interpreted as 'superinvasive' are native variants, and non-native alleles remain limited to areas near the original introduction sites. [ABSTRACT FROM AUTHOR]

Background Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. Methods Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. Results Volunteer bias in all associations, as naively estimated in UKB, was substantial—in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. Conclusions Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available. [ABSTRACT FROM AUTHOR]

The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches. [ABSTRACT FROM AUTHOR]

Demonstrating the genetic and environmental bases of adaptive phenotypes is a central goal of biology. Bird colors have a storied tradition at the forefront of conceptual advances in evolutionary biology. With recent increases in our ability to generate and analyze large phenotypic, environmental, and genetic data sets, we are in an excellent position to connect the environment-phenotype and phenotype-genotype links for bird color traits, illuminating potentially more general patterns and processes. I review selected advances in that arena, seeking to identify the most successful research approaches (with their strengths and pitfalls), so that they can be applied to the environment-phenotype-genotype nexus of other traits and taxa. At a macroevolutionary level, mechanistic models are well suited to compare evolutionary hypotheses of trait-environment correlations, and have revealed differences in tempo and mode of color evolution across clades, sexes, and body regions. At a microevolutionary scale, systems with ongoing gene flow along environmental gradients are conducive to addressing the genetics and ecology of phenotypic evolution. I weigh the strengths and weaknesses of two approaches to detecting genomic loci underpinning phenotypic variation in such systems: divergence scans and genome-wide association studies. I also discuss experimental approaches targeted at both the environment-phenotype and the phenotype-genotype links. I conclude by highlighting the importance of synthetic approaches that include environment, phenotype, and genotype within the same framework, and I suggest ideas for developments in that direction. [ABSTRACT FROM AUTHOR]

Abstract Hyperlysinemia is a rare autosomal recessive deficiency of 2‐aminoadipic semialdehyde synthase (AASS) affecting the initial step in lysine degradation. It is thought to be a benign biochemical abnormality, but reports on cases remain scarce. The description of additional cases, in particular, those identified without ascertainment bias, may help counseling of new cases in the future. It may also help to establish the risks associated with pharmacological inhibition of AASS, a potential therapeutic strategy that is under investigation for other inborn errors of lysine degradation. We describe the identification of a hyperlysinemia case identified in the Provincial Neonatal Urine Screening Program in Sherbrooke, Quebec. This case presented with a profile of cystinuria but with a very high increase in urinary lysine. A diagnosis of hyperlysinemia was confirmed through biochemical testing and the identification of biallelic variants in AASS. The p.R146W and p.T371I variants are novel and affect the folding of the lysine‐2‐oxoglutarate domain of AASS. The 11‐month‐old boy is currently doing well without any therapeutic interventions. The identification of this case through newborn urine screening further establishes that hyperlysinemia is a biochemical abnormality with limited clinical consequences and may not require any intervention.

Genetic species identification is often necessary for species flocks, such as rockfishes in the genus Sebastes (Teleostei, Scorpaenidae). Traditional visual identification methods are challenged by the presence of many sympatric rockfish species with morphologically similar juveniles. Here we present a straightforward approach for species identification in rockfishes using 96 nuclear microhaplotype loci that can be efficiently genotyped using high-throughput DNA sequencing. Self-assignment of nearly 1 000 samples from 54 species resulted in > 99% accurate species identification at a 95% confidence threshold. Phylogenetic relationships of Sebastes uncovered with these same loci were highly concordant with relationships previously derived primarily with mitochondrial DNA. We also assessed ascertainment bias and consequent reduced nucleotide diversity and heterozygosity in non-ascertainment species to understand the potential utility of these markers for those species. The data and protocol presented here will be useful for research and management of rockfishes in the Northeastern Pacific Ocean. [ABSTRACT FROM AUTHOR]

Panels of informative biallelic single nucleotide polymorphisms (SNPs) have been proposed to be an economical method to fast-track the population genetic analysis of Plasmodium falciparum in malaria-endemic areas. Whilst used successfully in low-transmission areas where infections are monoclonal and highly related, we present the first study to evaluate the performance of these 24- and 96-SNP molecular barcodes in African countries, characterised by moderate-to-high transmission, where multiclonal infections are prevalent. For SNP barcodes it is generally recommended that the SNPs chosen i) are biallelic, ii) have a minor allele frequency greater than 0.10, and iii) are independently segregating, to minimise bias in the analysis of genetic diversity and population structure. Further, to be standardised and used in many population genetic studies, these barcodes should maintain characteristics i) to iii) across various iv) geographies and v) time points. Using haplotypes generated from the MalariaGEN P. falciparum Community Project version six database, we investigated the ability of these two barcodes to fulfil these criteria in moderate-to-high transmission African populations in 25 sites across 10 countries. Predominantly clinical infections were analysed, with 52.3% found to be multiclonal, generating high proportions of mixed-allele calls (MACs) per isolate thereby impeding haplotype construction. Of the 24- and 96-SNPs, loci were removed if they were not biallelic and had low minor allele frequencies in all study populations, resulting in 20- and 75-SNP barcodes respectively for downstream population genetics analysis. Both SNP barcodes had low expected heterozygosity estimates in these African settings and consequently biased analyses of similarity. Both minor and major allele frequencies were temporally unstable. These SNP barcodes were also shown to identify weak genetic differentiation across large geographic distances based on Mantel Test and DAPC. These results demonstrate that these SNP barcodes are vulnerable to ascertainment bias and as such cannot be used as a standardised approach for malaria surveillance in moderate-to-high transmission areas in Africa, where the greatest genomic diversity of P. falciparum exists at local, regional and country levels. [ABSTRACT FROM AUTHOR]

We propose a likelihood ratio test to assess that sampling has been completed in closed population size estimation studies. More precisely, we assess if the expected number of subjects that have never been sampled is below a user‐specified threshold. The likelihood ratio test statistic has a nonstandard distribution under the null hypothesis. Critical values can be easily approximated and tabulated, and they do not depend on model specification. We illustrate in a simulation study and three real data examples, one of which involves ascertainment bias of amyotrophic lateral sclerosis in Gulf War veterans. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP) data are widely used in research on natural populations. Although they are useful, SNP genotyping data are known to contain bias, normally referred to as ascertainment bias, because they are conditioned by already confirmed variants. This bias is introduced during the genotyping process, including the selection of populations for novel SNP discovery and the number of individuals involved in the discovery panel and selection of SNP markers. It is widely recognized that ascertainment bias can cause inaccurate inferences in population genetics and several methods to address these bias issues have been proposed. However, especially in natural populations, it is not always possible to apply an ideal ascertainment scheme because natural populations tend to have complex structures and histories. In addition, it was not fully assessed if ascertainment bias has the same effect on different types of population structure. Here, we examine the effects of bias produced during the selection of population for SNP discovery and consequent SNP marker selection processes under three demographic models: the island, stepping-stone, and population split models. Results show that site frequency spectra and summary statistics contain biases that depend on the joint effect of population structure and ascertainment schemes. Additionally, population structure inferences are also affected by ascertainment bias. Based on these results, it is recommended to evaluate the validity of the ascertainment strategy prior to the actual typing process because the direction and extent of ascertainment bias vary depending on several factors. [ABSTRACT FROM AUTHOR]

Abstract Background Accurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of genome-wide association studies (GWAS), and extensive computer simulations to identify how genetic disease risks can be misestimated. Results In contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa, and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived. Conclusions Our results imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population.

Case fatality rate (CFR) is used to calculate mortality burden of COVID-19 under different scenarios, thus informing risk-benefit balance of interventions both pharmaceutical and nonpharmaceutical. However, observed CFR is driven by testing: as more low-risk cases are identified, observed CFR will decline. This report quantifies test bias by modeling observed CFR as log-log-linear function of test density (tests per population) in 163 countries. CFR declined almost 20% (e.g. from 5% to 4%) for each doubling of test density (p < 0.0001); this association did not vary by continent (interaction p > 0.10) although at any given test density CFR was higher in Europe or North America than in Asia or Africa. This effect of test density on observed CFR is adequate to hide all but the largest true differences in case survivorship. Published estimates of CFR should specify test density, and comparisons should correct for it such as by applying the provided model.

Background/Aim: In clinical trials, there is potential for bias from unblinded observers that may influence ascertainment of outcomes. This issue arose in the Strategies to Reduce Injuries and Develop Confidence in Elders trial, a cluster randomized trial to test a multicomponent intervention versus enhanced usual care (control) to prevent serious fall injuries, originally defined as a fall injury leading to medical attention. An unblinded nurse falls care manager administered the intervention, while the usual care arm did not involve contact with a falls care manager. Thus, there was an opportunity for falls care managers to refer participants reporting falls to seek medical attention. Since this type of observer bias could not occur in the usual care arm, there was potential for additional falls to be reported in the intervention arm, leading to dilution of the intervention effect and a reduction in study power. We describe the clinical basis for ascertainment bias, the statistical approach used to assess it, and its effect on study power. Methods: The prespecified interim monitoring plan included a decision algorithm for assessing ascertainment bias and adapting (revising) the primary outcome definition, if necessary. The original definition categorized serious fall injuries requiring medical attention into Type 1 (fracture other than thoracic/lumbar vertebral, joint dislocation, cut requiring closure) and Type 2 (head injury, sprain or strain, bruising or swelling, other). The revised definition, proposed by the monitoring plan, excluded Type 2 injuries that did not necessarily require an overnight hospitalization since these would be most subject to bias. These injuries were categorized into those with (Type 2b) and without (Type 2c) medical attention. The remaining Type 2a injuries required medical attention and an overnight hospitalization. We used the ratio of 2b/(2b + 2c) in intervention versus control as a measure of ascertainment bias; ratios > 1 indicated the likelihood of falls care manager bias. We determined the effect of ascertainment bias on study power for the revised (Types 1 and 2a) versus original definition (Types 1, 2a, and 2b). Results: The estimate of ascertainment bias was 1.14 (95% confidence interval: 0.98, 1.30), providing evidence of the likelihood of falls care manager bias. We estimated that this bias diluted the hazard ratio from the hypothesized 0.80 to 0.86 and reduced power to under 80% for the original primary outcome definition. In contrast, adapting the revised definition maintained study power at nearly 90%. Conclusion: There was evidence of ascertainment bias in the Strategies to Reduce Injuries and Develop Confidence in Elders trial. The decision to adapt the primary outcome definition reduced the likelihood of this bias while preserving the intervention effect and study power. [ABSTRACT FROM AUTHOR]

While the gold standard for clinical trials is to blind all parties-participants, researchers, and evaluators-to treatment assignment, this is not always a possibility. When some or all of the above individuals know the treatment assignment, this leaves the study open to the introduction of postrandomization biases. In the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, we were presented with the potential for the unblinded clinicians administering the treatment, as well as the individuals enrolled in the study, to introduce ascertainment bias into some but not all events comprising the primary outcome. In this article, we present ways to estimate the ascertainment bias for a time-to-event outcome, and discuss its impact on the overall power of a trial vs changing of the outcome definition to a more stringent unbiased definition that restricts attention to measurements less subject to potentially differential assessment. We found that for the majority of situations, it is better to revise the definition to a more stringent definition, as was done in STRIDE, even though fewer events may be observed. [ABSTRACT FROM AUTHOR]

Background: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB., Methods: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias., Results: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size., Conclusions: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)

The five discussions of our paper provide several modeling alternatives, extensions, and generalizations that can potentially guide future research in meta-analysis. In this rejoinder, we briefly summarize and comment on some of those points., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Abstract Background Single nucleotide polymorphism (SNP) panels have been widely used to study genomic variations within and between populations. Methods of SNP discovery have been a matter of debate for their potential of introducing ascertainment bias, and genetic diversity results obtained from the SNP genotype data can be misleading. We used a total of 42 chicken populations where both individual genotyped array data and pool whole genome resequencing (WGS) data were available. We compared allele frequency distributions and genetic diversity measures (expected heterozygosity (H e ), fixation index (F ST ) values, genetic distances and principal components analysis (PCA)) between the two data types. With the array data, we applied different filtering options (SNPs polymorphic in samples of two Gallus gallus wild populations, linkage disequilibrium (LD) based pruning and minor allele frequency (MAF) filtering, and combinations thereof) to assess their potential to mitigate the ascertainment bias. Results Rare SNPs were underrepresented in the array data. Array data consistently overestimated H e compared to WGS data, however, with a similar ranking of the breeds, as demonstrated by Spearman’s rank correlations ranging between 0.956 and 0.985. LD based pruning resulted in a reduced overestimation of H e compared to the other filters and slightly improved the relationship with the WGS results. The raw array data and those with polymorphic SNPs in the wild samples underestimated pairwise F ST values between breeds which had low F ST (0.15). LD based pruned data underestimated F ST in a consistent manner. The genetic distance matrix from LD pruned data was more closely related to that of WGS than the other array versions. PCA was rather robust in all array versions, since the population structure on the PCA plot was generally well captured in comparison to the WGS data. Conclusions Among the tested filtering strategies, LD based pruning was found to account for the effects of ascertainment bias in the relatively best way, producing results which are most comparable to those obtained from WGS data and therefore is recommended for practical use.

Background: In randomized trials, the term "double-blind" (and its derivatives, single- and triple-blind, fully blind, and partially blind or masked) has no standard or widely accepted definition. Agreement about which groups are blinded is poor, and authors using these terms often do not identify which groups were blinded, despite specific reporting guidelines to the contrary. Nevertheless, many readers assume-incorrectly-that they know which groups are blinded. Thus, the term is ambiguous at best, misleading at worst, and, in either case, interferes with the accurate reporting, interpretation, and evaluation of randomized trials. The problems with the terms have been thoroughly documented in the literature, and many authors have recommended that they be abandoned.Proposal: We and our co-signers suggest eliminating the use of adjectives that modify "blinding" in randomized trials; a trial would be described as either blinded or unblinded. We also propose that authors report in a standard table which groups or individuals were blinded, what they were blinded to, how blinding was implemented, and whether blinding was maintained. Individuals with dual responsibilities, such as caregiving and data collecting, would also be identified. If blinding was compromised, authors should describe the potential implications of the loss of blinding on interpreting the results.Conclusion: "Double blind" and its derivatives are terms with little to recommend their continued use. Eliminating the use of adjectives that impart a false specificity to the term would reduce misinterpretations, and recommending that authors report who was blinded to what and how in a standard table would require them to be specific about which groups and individuals were blinded. [ABSTRACT FROM AUTHOR]

Objectives: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements.Methods: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected-value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected-value bias and quantified the impact of biased measurements.Results: We analyzed 272 third-trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected-value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12-21%) of the scans, the fetus could be considered as small-for-gestational age or appropriate-for-gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9-16%) of scans, the fetus could be considered as large-for-gestational age or appropriate-for-gestational age if using the largest or smallest possible measurements, respectively.Conclusions: During routine third-trimester growth scans, expected-value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The European bison is a non-model organism; thus, most of its genetic and genomic analyses have been performed using cattle-specific resources, such as BovineSNP50 BeadChip or Illumina Bovine 800 K HD Bead Chip. The problem with non-specific tools is the potential loss of evolutionary diversified information (ascertainment bias) and species-specific markers. Here, we have used a genotyping-by-sequencing (GBS) approach for genotyping 256 samples from the European bison population in Bialowieza Forest (Poland) and performed an analysis using two integrated pipelines of the STACKS software: one is de novo (without reference genome) and the other is a reference pipeline (with reference genome). Moreover, we used a reference pipeline with two different genomes, i.e., Bos taurus and European bison. Genotyping by sequencing (GBS) is a useful tool for SNP genotyping in non-model organisms due to its cost effectiveness. Our results support GBS with a reference pipeline without PCR duplicates as a powerful approach for studying the population structure and genotyping data of non-model organisms. We found more polymorphic markers in the reference pipeline in comparison to the de novo pipeline. The decreased number of SNPs from the de novo pipeline could be due to the extremely low level of heterozygosity in European bison. It has been confirmed that all the de novo/Bos taurus and Bos taurus reference pipeline obtained SNPs were unique and not included in 800 K BovineHD BeadChip.

Abstract Background While the risk of severe complications of Middle East respiratory syndrome (MERS) and its determinants have been explored in previous studies, a systematic analysis of published articles with different designs and populations has yet to be conducted. The present study aimed to systematically review the risk of death associated with MERS as well as risk factors for associated complications. Methods PubMed and Web of Science databases were searched for clinical and epidemiological studies on confirmed cases of MERS. Eligible articles reported clinical outcomes, especially severe complications or death associated with MERS. Risks of admission to intensive care unit (ICU), mechanical ventilation and death were estimated. Subsequently, potential associations between MERS-associated death and age, sex, underlying medical conditions and study design were explored. Results A total of 25 eligible articles were identified. The case fatality risk ranged from 14.5 to 100%, with the pooled estimate at 39.1%. The risks of ICU admission and mechanical ventilation ranged from 44.4 to 100% and from 25.0 to 100%, with pooled estimates at 78.2 and 73.0%, respectively. These risks showed a substantial heterogeneity among the identified studies, and appeared to be the highest in case studies focusing on ICU cases. We identified older age, male sex and underlying medical conditions, including diabetes mellitus, renal disease, respiratory disease, heart disease and hypertension, as clinical predictors of death associated with MERS. In ICU case studies, the expected odds ratios (OR) of death among patients with underlying heart disease or renal disease to patients without such comorbidities were 0.6 (95% Confidence Interval (CI): 0.1, 4.3) and 0.6 (95% CI: 0.0, 2.1), respectively, while the ORs were 3.8 (95% CI: 3.4, 4.2) and 2.4 (95% CI: 2.0, 2.9), respectively, in studies with other types of designs. Conclusions The heterogeneity for the risk of death and severe manifestations was substantially high among the studies, and varying study designs was one of the underlying reasons for this heterogeneity. A statistical estimation of the risk of MERS death and identification of risk factors must be conducted, particularly considering the study design and potential biases associated with case detection and diagnosis.

Case fatality rate (CFR) is used to calculate mortality burden of COVID-19 under different scenarios, thus informing risk-benefit balance of interventions both pharmaceutical and nonpharmaceutical. However, observed CFR is driven by testing: as more low-risk cases are identified, observed CFR will decline. This report quantifies test bias by modeling observed CFR as log-log-linear function of test density (tests per population) in 163 countries. CFR declined almost 20% (e.g. from 5% to 4%) for each doubling of test density (p < 0.0001); this association did not vary by continent (interaction p > 0.10) although at any given test density CFR was higher in Europe or North America than in Asia or Africa. This effect of test density on observed CFR is adequate to hide all but the largest true differences in case survivorship. Published estimates of CFR should specify test density, and comparisons should correct for it such as by applying the provided model. [ABSTRACT FROM AUTHOR]

• Neurological syndromes and conditions can carry high risk of ASD. • Sex differences have been intensely studied in idiopathic autism but not in secondary autism. • Sex differences in secondary autism have not been quantitatively meta-analyzed. • In secondary autism we find slightly more males and males manifest more severe neurological symptoms. • In secondary autism, male overprevalence is partly caused by ascertainment bias. Controversy persists as to whether there exists a bioendogenous and/or socioculturally determined male overprevalence and/or a predominantly female neurological protective effect against occurrence of autism but with greater brain morbidity (intellectual disability, epilepsy) in females who do have autism. Much research on idiopathic ASD has not fully resolved this conundrum. We assembled from case series in the literature 70 samples of male and female patients with specific bioetiologies (genetic, neurological, metabolic), all with complete documentation of sex, comorbid ASD, epilepsy and intellectual disability (total sample = 1264 cases). Male overprevalence in these patients with secondary ASD (sASD) was significant, but much lower than typically reported in idiopathic ASD (iASD). The males with sASD had significantly greater prevalence of intellectual disability than the females with sASD. The neuropathologies with highest risk of sASD presented the highest male overprevalence of sASD. "Ascertainment bias" emerged as a significant modulator of male overprevalence in sASD. In secondary autism, male overprevalence is not as strong as in idiopathic autism, risk of female autism is modestly explained by risk of epilepsy, and male overprevalence is strongly explained by risk of intellectual disability and ascertainment bias. [ABSTRACT FROM AUTHOR]

Bayesian estimates of divergence times based on the molecular clock yield uncertainty of parameter estimates measured by the width of posterior distributions of node ages. For the relaxed molecular clock, previous works have reported that some of the uncertainty inherent to the variation of rates among lineages may be reduced by partitioning data. Here we test this effect for the purely morphological clock, using placental mammals as a case study. We applied the uncorrelated lognormal relaxed clock to morphological data of 40 extant mammalian taxa and 4,533 characters, taken from the largest published matrix of discrete phenotypic characters. The morphologically derived timescale was compared to divergence times inferred from molecular and combined data. We show that partitioning data into anatomical units significantly reduced the uncertainty of divergence time estimates for morphological data. For the first time, we demonstrate that ascertainment bias has an impact on the precision of morphological clock estimates. While analyses including molecular data suggested most divergences between placental orders occurred near the K‐Pg boundary, the partitioned morphological clock recovered older interordinal splits and some younger intraordinal ones, including significantly later dates for the radiation of bats and rodents, which accord to the short‐fuse hypothesis. We provide the first analysis of the impact of data partitioning and accounting for ascertainment bias on divergence time estimates employing the morphological clock. Both treatments of data reduce the uncertainty associated with estimates of node ages. [ABSTRACT FROM AUTHOR]

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species. [ABSTRACT FROM AUTHOR]

Tip-dating, where fossils are included as dated terminal taxa in Bayesian dating inference, is an increasingly popular method. Data for these studies often come from morphological character matrices originally developed for non-dated, and usually parsimony, analyses. In parsimony, only shared derived characters (synapomorphies) provide grouping information, so many character matrices have an ascertainment bias: they omit autapomorphies (unique derived character states), which are considered uninformative. There has been no study of the effect of this ascertainment bias in tipdating, but autapomorphies can be informative in model-based inference. We expected that excluding autapomorphies would shorten the morphological branchlengths of terminal branches, and thus bias downwards the time branchlengths inferred in tipdating. We tested for this effect using a matrix for Carboniferous-Permian eureptiles where all autapomorphies had been deliberately coded. Surprisingly, date estimates are virtually unchanged when autapomorphies are excluded, although we find large changes in morphological rate estimates and small effects on topological and dating confidence. We hypothesized that the puzzling lack of effect on dating was caused by the non-clock nature of the eureptile data. We confirm this explanation by simulating strict clock and non-clock datasets, showing that autapomorphy exclusion biases dating only for the clocklike case. A theoretical solution to ascertainment bias is computing the ascertainment bias correction (Mkparsinf), but we explore this correction in detail, and show that it is computationally impractical for typical datasets with many character states and taxa. Therefore we recommend that palaeontologists collect autapomorphies whenever possible when assembling character matrices. [ABSTRACT FROM AUTHOR]

Premise of the Study: The study of very large and very old clades holds the promise of greater insights into evolution across the tree of life. However, there has been a fair amount of criticism regarding the interpretations and quality of studies to date, with some suggesting that detailed studies carried out on smaller, tractable scales should be preferred over the increasingly grand syntheses of these data. Methods: We provided in detail our trials and tribulations of compiling a large, sparsely sampled matrix from GenBank data and inferring a well‐supported, time‐calibrated phylogeny of Campanulidae. We also used a simulation approach to assess tree quality and to study the value of using very large, comprehensive phylogenies in a comparative context. Key Results: A robust and well‐supported phylogeny can be produced as long as automated procedures are supplemented with some human intervention. In the case of campanulids, the overall topology may be driven not only by particular genes, but also particular sequences for a gene. We also determined that estimates of divergence times should be fairly robust to issues related to clade‐specific heterogeneity. Finally, we demonstrated how relying on results from smaller, younger clades are prone to produce biased interpretations of tropical to temperate evolution across campanulids as a whole. Conclusions: While we were both surprised and encouraged by the robust and fairly well‐resolved, comprehensive phylogeny of campanulids, challenges still remain. Nevertheless, large phylogenies are inherently valuable in a comparative context if only to attenuate the issue of ascertainment bias. [ABSTRACT FROM AUTHOR]

Background: Single nucleotide polymorphism (SNP) panels have been widely used to study genomic variations within and between populations. Methods of SNP discovery have been a matter of debate for their potential of introducing ascertainment bias, and genetic diversity results obtained from the SNP genotype data can be misleading. We used a total of 42 chicken populations where both individual genotyped array data and pool whole genome resequencing (WGS) data were available. We compared allele frequency distributions and genetic diversity measures (expected heterozygosity (He), fixation index (FST) values, genetic distances and principal components analysis (PCA)) between the two data types. With the array data, we applied different filtering options (SNPs polymorphic in samples of two Gallus gallus wild populations, linkage disequilibrium (LD) based pruning and minor allele frequency (MAF) filtering, and combinations thereof) to assess their potential to mitigate the ascertainment bias. Results: Rare SNPs were underrepresented in the array data. Array data consistently overestimated He compared to WGS data, however, with a similar ranking of the breeds, as demonstrated by Spearman's rank correlations ranging between 0.956 and 0.985. LD based pruning resulted in a reduced overestimation of He compared to the other filters and slightly improved the relationship with the WGS results. The raw array data and those with polymorphic SNPs in the wild samples underestimated pairwise FST values between breeds which had low FST (<0.15) in the WGS, and overestimated this parameter for high WGS FST (>0.15). LD based pruned data underestimated FST in a consistent manner. The genetic distance matrix from LD pruned data was more closely related to that of WGS than the other array versions. PCA was rather robust in all array versions, since the population structure on the PCA plot was generally well captured in comparison to the WGS data. Conclusions: Among the tested filtering strategies, LD based pruning was found to account for the effects of ascertainment bias in the relatively best way, producing results which are most comparable to those obtained from WGS data and therefore is recommended for practical use [ABSTRACT FROM AUTHOR]

Introduction Findings for genetic correlates of late-onset Alzheimer's disease (LOAD) in studies that rely solely on clinic visits may differ from those with capacity to follow participants unable to attend clinic visits. Methods We evaluated previously identified LOAD-risk single nucleotide variants in the prospective Adult Changes in Thought study, comparing hazard ratios (HRs) estimated using the full data set of both in-home and clinic visits ( n = 1697) to HRs estimated using only data that were obtained from clinic visits ( n = 1308). Models were adjusted for age, sex, principal components to account for ancestry, and additional health indicators. Results LOAD associations nominally differed for 4 of 21 variants; CR1 and APOE variants were significant after Bonferroni correction. Discussion Estimates of genetic associations may differ for studies limited to clinic-only designs. Home visit capacity should be explored as a possible source of heterogeneity and potential bias in genetic studies. [ABSTRACT FROM AUTHOR]

Background: Genetic anticipation, the earlier onset of disease in successive generations, has been reported in hereditary breast and ovarian cancer syndrome (HBOC), but little is known about its underlying mechanisms. Ascertainment bias has been suggested as a reason in previous studies. Likewise, cohort effect, which may be caused by environmental factors, can be misinterpreted as genetic anticipation.Methods: The authors reviewed the pedigrees of 176 kindreds, segregating those with deleterious mutations in breast cancer genes 1 and 2 (BRCA1/BRCA2) who had at least 2 consecutive generations of the same cancer (breast or ovarian). By using mutation probabilities as analytical weights in weighted random-effect models, generational differences in the age at onset of breast/ovarian cancer were calculated. The analyses were further controlled for ascertainment bias by excluding probands and adjusting for birth-cohort effect in the anticipation models.Results: The mean age at the onset of breast cancer for the probands' generation was 41.9 years, which was 6.8 years and 9.8 years earlier than the parents' and grandparents' generations, respectively. The anticipation effect for breast cancer remained significant after excluding the probands. There was a birth-cohort effect: patients who were born in 1930s and 1940s had breast cancer 5.0 years and 7.6 years earlier than patients who were born before 1920. The difference in breast cancer age of onset across generations was no longer significant after adjusting for birth-cohort effect.Conclusions: The observed anticipation effect was driven mainly by a decrease in age of onset across birth cohorts, underscoring the need for risk-reducing interventions that target changing environmental/lifestyle factors in BRCA1/BRCA2 carriers. Cancer 2016;122:1913-20. © 2016 American Cancer Society. [ABSTRACT FROM AUTHOR]

Purpose: Alcohol consumption in moderation has been associated with incident breast and colorectal cancer. Whether these associations may be overestimated by more intensive screening among moderate consumers is unknown. This study examines the associations of alcohol consumption with cancer screening.Methods: In six iterations (2002-2012) of the Behavioral Risk Factor Surveillance System, a telephone survey of US adults conducted by the Centers for Disease Control and Prevention, participants reported their alcohol use and recent screening for several cancers. We assessed whether alcohol use was associated with breast, cervical, and colorectal cancer screening after sample-weighted adjustment for sociodemographic and healthcare utilization factors.Results: Among 2,191,483 survey respondents, 80.5 % (weighted prevalence) of eligible individuals reported having an up-to-date mammogram, 87.7 % having a Pap test, and 56.8 % having a colonoscopy/sigmoidoscopy. For all breast, cervical, and colorectal cancers, moderate consumers were more likely to report screening (84.7, 91.2, 61.1 %) than non-consumers, even after multivariate adjustment (adjusted prevalence ratios 1.04, 1.04, 1.07; p < 0.001 for all). Among binge consumers, the weighted prevalence was lower than that in non-binge consumers (binge vs non-binge moderate consumers 80.5 vs 85.5 %, 89.9 vs 91.8 %, 52.8 vs 63.3 %) but still higher than non-consumers for breast and cervical cancer screening.Conclusions: In the USA, moderate consumers consistently report a greater likelihood of breast, cervical, and colorectal cancer screening than do non-consumers. Given the likelihood of overdiagnosis, further study of alcohol consumption and cancer should include cancer-specific mortality, which is less sensitive to differences in screening and detection. [ABSTRACT FROM AUTHOR]

Background: Rye, Secale cereale L., has historically been a crop of major importance and is still a key cereal in many parts of Europe. Single populations of cultivated rye have been shown to capture a large proportion of the genetic diversity present in the species, but the distribution of genetic diversity in subspecies and across geographical areas is largely unknown. Here we explore the structure of genetic diversity in landrace rye and relate it to that of wild and feral relatives. Results: A total of 567 SNPs were analysed in 434 individuals from 76 accessions of wild, feral and cultivated rye. Genetic diversity was highest in cultivated rye, slightly lower in feral rye taxa and significantly lower in the wild S. strictum Presl. and S. africanum Stapf. Evaluation of effects from ascertainment bias suggests underestimation of diversity primarily in S. strictum and S. africanum. Levels of ascertainment bias, STRUCTURE and principal component analyses all supported the proposed classification of S. africanum and S. strictum as a separate species from S. cereale. S. afghanicum (Vav.) Roshev, S. ancestrale Zhuk., S. dighoricum(Vav.) Roshev, S. segetale (Zhuk.) Roshev and S. vavilovii Grossh. seemed, in contrast, to share the same gene pool as S. cereale and their genetic clustering was more dependent on geographical origin than taxonomic classification. S. vavilovii was found to be the most likely wild ancestor of cultivated rye. Among cultivated rye landraces from Europe, Asia and North Africa five geographically discrete genetic clusters were identified. These had only limited overlap with major agro-climatic zones. Slash-and-burn rye from the Finnmark area in Scandinavia formed a distinct cluster with little similarity to other landrace ryes. Regional studies of Northern and South-West Europe demonstrate different genetic distribution patterns as a result of varying cultivation intensity. Conclusions: With the exception of S. strictum and S. africanum different rye taxa share the majority of the genetic variation. Due to the vast sharing of genetic diversity within the S. cereale clade, ascertainment bias seems to be a lesser problem in rye than in predominantly selfing species. By exploiting within accession diversity geographic structure can be shown on a much finer scale than previously reported. [ABSTRACT FROM AUTHOR]

Previous genetic studies of eastern coyotes ( Canis latrans) are based on one of two strategies: sampling many individuals using one or very few molecular markers, or sampling very few individuals using many genomic markers. Thus, a regional analysis of genetic diversity and population structure in eastern coyotes using many samples and several molecular markers is lacking. I evaluated genetic diversity and population structure in 385 northeastern coyotes using 16 common single nucleotide polymorphisms (SNPs). A region-wide analysis of population structure revealed three primary genetic populations, but these do not correspond to the same three subdivisions inferred in a previous analysis of mitochondrial DNA sequences. More focused geographic analyses of population structure indicated that ample genetic structure occurs in coyotes from an intermediate contact zone where two range expansion fronts meet. These results demonstrate that genotyping several highly heterozygous SNPs in a large, geographically dense sample is an effective way to detect cryptic population genetic structure. The importance of SNPs in studies of population and wildlife genomics is rapidly increasing; this study adds to the growing body of recent literature that demonstrates the utility of SNPs ascertained from a model organism for evolutionary inference in closely related species.

Abstract The large single nucleotide polymorphism (SNP) typing projects have provided an invaluable data resource for human population geneticists. Almost all of the available SNP loci, however, have been identified through a SNP discovery protocol that will influence the allelic distributions in the sampled loci. Standard methods for population genetic analysis based on the available SNP data will, therefore, be biased. This paper discusses the effect of this ascertainment bias on allelic distributions and on methods for quantifying linkage disequilibrium and estimating demographic parameters. Several recently developed methods for correcting for the ascertainment bias will also be discussed.