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We used whole genome resequencing of pooled individuals to develop a high‐density single‐nucleotide polymorphism (SNP) chip for Eucalyptus. Genomes of 240 trees of 12 species were sequenced at 3.5× each, and 46 997 586 raw SNP variants were subject to multivariable filtering metrics toward a multispecies, genome‐wide distributed chip content. Of the 60 904 SNPs on the chip, 59 222 were genotyped and 51 204 were polymorphic across 14 Eucalyptus species, providing a 96% genome‐wide coverage with 1 SNP/12–20 kb, and 47 069 SNPs at ≤ 10 kb from 30 444 of the 33 917 genes in the Eucalyptus genome. Given the EUChip60K multi‐species genotyping flexibility, we show that both the sample size and taxonomic composition of cluster files impact heterozygous call specificity and sensitivity by benchmarking against ‘gold standard’ genotypes derived from deeply sequenced individual tree genomes. Thousands of SNPs were shared across species, likely representing ancient variants arisen before the split of these taxa, hinting to a recent eucalypt radiation. We show that the variable SNP filtering constraints allowed coverage of the entire site frequency spectrum, mitigating SNP ascertainment bias. The EUChip60K represents an outstanding tool with which to address population genomics questions in Eucalyptus and to empower genomic selection, GWAS and the broader study of complex trait variation in eucalypts.

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.

Chip-based high-throughput genotyping has facilitated genome-wide studies of genetic diversity. Many studies have utilized these large data sets to make inferences about the demographic history of human populations using measures of genetic differentiation such as FST or principal component analyses. However, the single nucleotide polymorphism (SNP) chip data suffer from ascertainment biases caused by the SNP discovery process in which a small number of individuals from selected populations are used as discovery panels. In this study, we investigate the effect of the ascertainment bias on inferences regarding genetic differentiation among populations in one of the common genome-wide genotyping platforms. We generate SNP genotyping data for individuals that previously have been subject to partial genome-wide Sanger sequencing and compare inferences based on genotyping data to inferences based on direct sequencing. In addition, we also analyze publicly available genome-wide data. We demonstrate that the ascertainment biases will distort measures of human diversity and possibly change conclusions drawn from these measures in some times unexpected ways. We also show that details of the genotyping calling algorithms can have a surprisingly large effect on population genetic inferences. We not only present a correction of the spectrum for the widely used Affymetrix SNP chips but also show that such corrections are difficult to generalize among studies.

Clinical experience suggests a heightened risk associated with the transition to maintenance dialysis but few national studies have systematically examined early mortality trajectories. Here we calculated weekly mortality rates in the first year of treatment for 498,566 adults initiating maintenance dialysis in the United States (2005–2009). Mortality rates were initially unexpectedly low, peaked at 37.0 per 100 person-years in week 6, and declined steadily to 14.8 by week 51. In both early (weeks 7–12) and later (weeks 13–51) time frames, multivariate mortality associations included older age, female, Caucasian, non-Hispanic ethnicity, end-stage renal disease (ESRD) from hypertension and acute tubular necrosis, ischemic heart disease, estimated glomerular filtration rate of 15ml/min per 1.73m2 or more, shorter duration of nephrologist care, and hemodialysis, especially with a catheter. For early mortality risk, adjusted hazard ratios of 2 or more were seen with age over 65 (5.80 vs. under 40 years), hemodialysis with a catheter (2.73 vs. fistula), and age 40–64 (2.33). For later mortality risk, adjusted hazard ratios of 2 or more were seen with age over 65 (4.32 vs. under 40 years), hemodialysis with a catheter (2.10 vs. fistula), and age 40–64 (2.00). Thus, low initial mortality rates question the accuracy of data collected and are consistent with deaths occurring in the early weeks after starting dialysis not being registered with the United States Renal Data System.

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) in the WGS, and overestimated this parameter for high WGS  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.

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.

Background Population genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays. The resulting bias in the estimation of allele frequency spectra and population genetics parameters like heterozygosity and genetic distances relative to whole genome sequencing (WGS) data is known as SNP ascertainment bias. Full correction for this bias requires detailed knowledge of the array design process, which is often not available in practice. This study suggests an alternative approach to mitigate ascertainment bias of a large set of genotyped individuals by using information of a small set of sequenced individuals via imputation without the need for prior knowledge on the array design. Results The strategy was first tested by simulating additional ascertainment bias with a set of 1566 chickens from 74 populations that were genotyped for the positions of the Affymetrix Axiom™ 580 k Genome-Wide Chicken Array. Imputation accuracy was shown to be consistently higher for populations used for SNP discovery during the simulated array design process. Reference sets of at least one individual per population in the study set led to a strong correction of ascertainment bias for estimates of expected and observed heterozygosity, Wright’s Fixation Index and Nei’s Standard Genetic Distance. In contrast, unbalanced reference sets (overrepresentation of populations compared to the study set) introduced a new bias towards the reference populations. Finally, the array genotypes were imputed to WGS by utilization of reference sets of 74 individuals (one per population) to 98 individuals (additional commercial chickens) and compared with a mixture of individually and pooled sequenced populations. The imputation reduced the slope between heterozygosity estimates of array data and WGS data from 1.94 to 1.26 when using the smaller balanced reference panel and to 1.44 when using the larger but unbalanced reference panel. This generally supported the results from simulation but was less favorable, advocating for a larger reference panel when imputing to WGS. Conclusions The results highlight the potential of using imputation for mitigation of SNP ascertainment bias but also underline the need for unbiased reference sets.

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.

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.

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.