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Survival from in-hospital cardiac arrest is approximately 18%, but for patients who require advanced airway management survival is lower. Those who do survive are often left with significant disability. Traditionally, resuscitation of cardiac arrest patients has included tracheal intubation, however insertion of a supraglottic airway has gained popularity as an alternative approach to advanced airway management. Evidence from out-of-hospital cardiac arrest suggests no significant differences in mortality or morbidity between these two approaches, but there is no randomised evidence for airway management during in-hospital cardiac arrest.The aim of the AIRWAYS-3 randomised trial, described in this protocol paper, is to determine the clinical and cost effectiveness of a supraglottic airway versus tracheal intubation during in-hospital cardiac arrest. Patients will be allocated randomly to receive either a supraglottic airway or tracheal intubation as the initial advanced airway management. We will also estimate the relative cost-effectiveness of these two approaches. The primary outcome is functional status, measured using the modified Rankin Scale at hospital discharge or 30 days post-randomisation, whichever occurs first.AIRWAYS-3 presents ethical challenges regarding patient consent and data collection. These include the enrolment of unconscious patients without prior consent in a way that avoids methodological bias. Other complexities include the requirement to randomise patients efficiently during a time-critical cardiac arrest. Many of these challenges are encountered in other emergency care research; we discuss our approaches to addressing them.Trial registration: ISRCTN17720457. Prospectively registered on 29/07/2022.

Many rare high-impact variants have been associated with disease, but the origins and functional impact are not always explored. Here, the authors trace the ancestry of a rare high impact atrial fibrillation allele in KCNQ1, and use iPSC-derived cardiomyocytes to characterize the effect of the allele.

Large whole-exome sequencing studies have suggested that the genetic architecture of syndromic congenital heart disease (CHD) is different from sporadic forms. Here, Watkins et al. estimate the relative contribution of damaging recessive and de novo genotypes to CHD in 2391 trios and find them to be associated with different gene functions.

Crohn’s disease is a complex genetic trait characterized by chronic relapsing intestinal inflammation. Genome wide association studies (GWAS) have identified more than 170 loci associated with the disease, accounting for ∼14% of the disease variance. We hypothesized that rare genetic variation in GWAS positional candidates also contribute to disease pathogenesis. We performed targeted, massively-parallel sequencing of 101 genes in 205 children with Crohn’s disease, including 179 parent-child trios and 200 controls, both of European ancestry. We used the gene burden test implemented in VAAST and estimated effect sizes using logistic regression and meta-analyses. We identified three genes with nominally significant p-values: NOD2, RTKN2, and MGAT3. Only NOD2 was significant after correcting for multiple comparisons. We identified eight novel rare variants in NOD2 that are likely disease-associated. Incorporation of rare variation and compound heterozygosity nominally increased the proportion of variance explained from 0.074 to 0.089. We estimated the population attributable risk and total heritability of variation in NOD2 to be 32.9% and 3.4%, respectively, with 3.7% and 0.25% accounted for by rare putatively functional variants. Sequencing probands (as opposed to genotyping) to identify rare variants and incorporating phase by sequencing parents can recover a portion of the missing heritability of Crohn’s disease.

Abstract Background Polymorphic human Alu elements are excellent tools for assessing population structure, and new retrotransposition events can contribute to disease. Next-generation sequencing has greatly increased the potential to discover Alu elements in human populations, and various sequencing and bioinformatics methods have been designed to tackle the problem of detecting these highly repetitive elements. However, current techniques for Alu discovery may miss rare, polymorphic Alu elements. Combining multiple discovery approaches may provide a better profile of the polymorphic Alu mobilome. AluYb8/9 elements have been a focus of our recent studies as they are young subfamilies (~2.3 million years old) that contribute ~30% of recent polymorphic Alu retrotransposition events. Here, we update our ME-Scan methods for detecting Alu elements and apply these methods to discover new insertions in a large set of individuals with diverse ancestral backgrounds. Results We identified 5,288 putative Alu insertion events, including several hundred novel AluYb8/9 elements from 213 individuals from 18 diverse human populations. Hundreds of these loci were specific to continental populations, and 23 non-reference population-specific loci were validated by PCR. We provide high-quality sequence information for 68 rare AluYb8/9 elements, of which 11 have hallmarks of an active source element. Our subfamily distribution of rare AluYb8/9 elements is consistent with previous datasets, and may be representative of rare loci. We also find that while ME-Scan and low-coverage, whole-genome sequencing (WGS) detect different Alu elements in 41 1000 Genomes individuals, the two methods yield similar population structure results. Conclusion Current in-silico methods for Alu discovery may miss rare, polymorphic Alu elements. Therefore, using multiple techniques can provide a more accurate profile of Alu elements in individuals and populations. We improved our false-negative rate as an indicator of sample quality for future ME-Scan experiments. In conclusion, we demonstrate that ME-Scan is a good supplement for next-generation sequencing methods and is well-suited for population-level analyses.

Abstract Background The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined. Results South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples. Conclusions Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies.

There is strong evidence for a genetic contribution to non-syndromic congenital heart defects (CHDs). However, exome- and genome-wide studies conducted at the variant and gene-level have identified few genome-wide significant CHD-related genes. Gene-set analyses are a useful complement to such studies and candidate gene-set analyses of rare variants have provided insight into the genetics of CHDs. However, similar analyses have not been conducted using data on common genetic variants. Consequently, we conducted common variant analyses of 15 CHD candidate gene-sets, using data from two common types of CHDs: conotruncal heart defects (1431 cases) and left ventricular outflow tract defects (509 cases). After Bonferroni correction for evaluation of multiple gene-sets, the cytoskeletal gene-set was significantly associated with conotruncal heart defects (βS = 0.09; 95% confidence interval (CI) 0.03–0.15). This association was stronger when analyses were restricted to the sub-set of cytoskeletal genes that have been observed to harbor rare damaging genotypes in at least two CHD cases (βS = 0.32, 95% CI 0.08–0.56). These findings add to the evidence linking cytoskeletal genes to CHDs and suggest that, for cytoskeletal genes, common variation may contribute to the risk of CHDs.

The genetics involved in Ewing sarcoma susceptibility and prognosis are poorly understood. EWS/FLI and related EWS/ETS chimeras upregulate numerous gene targets via promoter-based GGAA-microsatellite response elements. These microsatellites are highly polymorphic in humans, and preliminary evidence suggests EWS/FLI-mediated gene expression is highly dependent on the number of GGAA motifs within the microsatellite.Here we sought to examine the polymorphic spectrum of a GGAA-microsatellite within the NR0B1 promoter (a critical EWS/FLI target) in primary Ewing sarcoma tumors, and characterize how this polymorphism influences gene expression and clinical outcomes.A complex, bimodal pattern of EWS/FLI-mediated gene expression was observed across a wide range of GGAA motifs, with maximal expression observed in constructs containing 20-26 GGAA motifs. Relative to white European and African controls, the NR0B1 GGAA-microsatellite in tumor cells demonstrated a strong bias for haplotypes containing 21-25 GGAA motifs suggesting a relationship between microsatellite function and disease susceptibility. This selection bias was not a product of microsatellite instability in tumor samples, nor was there a correlation between NR0B1 GGAA-microsatellite polymorphisms and survival outcomes.These data suggest that GGAA-microsatellite polymorphisms observed in human populations modulate EWS/FLI-mediated gene expression and may influence disease susceptibility in Ewing sarcoma.

Deedu (DU) Mongolians, who migrated from the Mongolian steppes to the Qinghai-Tibetan Plateau approximately 500 years ago, are challenged by environmental conditions similar to native Tibetan highlanders. Identification of adaptive genetic factors in this population could provide insight into coordinated physiological responses to this environment. Here we examine genomic and phenotypic variation in this unique population and present the first complete analysis of a Mongolian whole-genome sequence. High-density SNP array data demonstrate that DU Mongolians share genetic ancestry with other Mongolian as well as Tibetan populations, specifically in genomic regions related with adaptation to high altitude. Several selection candidate genes identified in DU Mongolians are shared with other Asian groups (e.g., EDAR), neighboring Tibetan populations (including high-altitude candidates EPAS1, PKLR, and CYP2E1), as well as genes previously hypothesized to be associated with metabolic adaptation (e.g., PPARG). Hemoglobin concentration, a trait associated with high-altitude adaptation in Tibetans, is at an intermediate level in DU Mongolians compared to Tibetans and Han Chinese at comparable altitude. Whole-genome sequence from a DU Mongolian (Tianjiao1) shows that about 2% of the genomic variants, including more than 300 protein-coding changes, are specific to this individual. Our analyses of DU Mongolians and the first Mongolian genome provide valuable insight into genetic adaptation to extreme environments.

Humans reached present-day Island Southeast Asia (ISEA) in one of the first major human migrations out of Africa. Population movements in the millennia following this initial settlement are thought to have greatly influenced the genetic makeup of current inhabitants, yet the extent attributed to different events is not clear. Recent studies suggest that south-to-north gene flow largely influenced present-day patterns of genetic variation in Southeast Asian populations and that late Pleistocene and early Holocene migrations from Southeast Asia are responsible for a substantial proportion of ISEA ancestry. Archaeological and linguistic evidence suggests that the ancestors of present-day inhabitants came mainly from north-to-south migrations from Taiwan and throughout ISEA approximately 4,000 years ago. We report a large-scale genetic analysis of human variation in the Iban population from the Malaysian state of Sarawak in northwestern Borneo, located in the center of ISEA. Genome-wide single-nucleotide polymorphism (SNP) markers analyzed here suggest that the Iban exhibit greatest genetic similarity to Indonesian and mainland Southeast Asian populations. The most common non-recombining Y (NRY) and mitochondrial (mt) DNA haplogroups present in the Iban are associated with populations of Southeast Asia. We conclude that migrations from Southeast Asia made a large contribution to Iban ancestry, although evidence of potential gene flow from Taiwan is also seen in uniparentally inherited marker data.

Abstract Background Short tandem repeats (STRs) compose approximately 3% of the genome, and mutations at STR loci have been linked to dozens of human diseases including amyotrophic lateral sclerosis, Friedreich ataxia, Huntington disease, and fragile X syndrome. Improving our understanding of these mutations would increase our knowledge of the mutational dynamics of the genome and may uncover additional loci that contribute to disease. To estimate the genome-wide pattern of mutations at STR loci, we analyze blood-derived whole-genome sequencing data for 544 individuals from 29 three-generation CEPH pedigrees. These pedigrees contain both sets of grandparents, the parents, and an average of 9 grandchildren per family. Results We use HipSTR to identify de novo STR mutations in the 2nd generation of these pedigrees and require transmission to the third generation for validation. Analyzing approximately 1.6 million STR loci, we estimate the empirical de novo STR mutation rate to be 5.24 × 10−5 mutations per locus per generation. Perfect repeats mutate about 2 × more often than imperfect repeats. De novo STRs are significantly enriched in Alu elements. Conclusions Approximately 30% of new STR mutations occur within Alu elements, which compose only 11% of the genome, but only 10% are found in LINE-1 insertions, which compose 17% of the genome. Phasing these mutations to the parent of origin shows that parental transmission biases vary among families. We estimate the average number of de novo genome-wide STR mutations per individual to be approximately 85, which is similar to the average number of observed de novo single nucleotide variants.