The UK Biobank resource with deep phenotyping and genomic data

Making the Most of Clumping and Thresholding for Polygenic Scores

Polygenic prediction has the potential to contribute to precision medicine. Clumping and thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, several p value thresholds are tested to maximize predictive ability of the derived polygenic scores. Along with this p value threshold, we propose to tune three other hyper-parameters for C+T. We implement an efficient way to derive thousands of different C+T scores corresponding to a grid over four hyper-parameters. For example, it takes a few hours to derive 123K different C+T scores for 300K individuals and 1M variants using 16 physical cores. We find that optimizing over these four hyper-parameters improves the predictive performance of C+T in both simulations and real data applications as compared to tuning only the p value threshold. A particularly large increase can be noted when predicting depression status, from an AUC of 0.557 (95% CI: [0.544-0.569]) when tuning only the p value threshold to an AUC of 0.592 (95% CI: [0.580-0.604]) when tuning all four hyper-parameters we propose for C+T. We further propose stacked clumping and thresholding (SCT), a polygenic score that results from stacking all derived C+T scores. Instead of choosing one set of hyper-parameters that maximizes prediction in some training set, SCT learns an optimal linear combination of all C+T scores by using an efficient penalized regression. We apply SCT to eight different case-control diseases in the UK biobank data and find that SCT substantially improves prediction accuracy with an average AUC increase of 0.035 over standard C+T.

Genetic variants in circTUBB interacting with smoking can enhance colorectal cancer risk

Circular RNAs (circRNAs) are an intriguing class of regulatory RNAs involved in the tumorigenesis of many cancers, including colorectal cancer. Mechanistically, circRNAs sponge microRNAs (miRNAs) with specific miRNA response elements (MREs) and compete for regulatory target genes downstream. However, the genetic effects of MREs on colorectal cancer susceptibility remain unclear. The MREs of colorectal cancer-associated circRNAs (CRC-circRNAs) and corresponding single nucleotide polymorphisms (SNPs) were identified by the transcriptome of cancer cells and the 1000 Genomes Project. The association between candidate SNPs and colorectal cancer risk was evaluated in a Chinese population (1150 cases and 1342 controls) and two European populations (9023 cases and 386,896 controls) using logistic regression analysis. Among the 197 candidate SNPs within MREs of 186 CRC-circRNAs, rs25497 in circTUBB was significantly associated with colorectal cancer risk in a Chinese population after false discovery rate (FDR) correction [odds ratio (OR) = 1.78, 95% confidence interval (CI) = 1.44-2.21, P = 1.42 × 10^-7, P_FDR = 2.80 × 10^-5] and even reached significance in a genome-wide association study (GWAS) under the dominant model (P = 1.28 × 10^-8). Similar results were found in the European populations (OR_meta = 1.30, 95% CI = 1.10-1.53). Both stratification and interaction analyses revealed that rs25497 interacting with smoking affected the colorectal cancer risk (P_interaction = 1.48 × 10^-2). Here, we first comprehensively identified genetic variants in MREs of CRC-circRNAs and evaluated their effects on colorectal cancer risk in both Chinese and European populations. These findings provide basis for a comprehensive understanding of colorectal cancer etiology.

Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics

BACKGROUND

Associations between type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) were discovered in observational studies in both European and East Asian populations. However, whether such associations are causal remains largely unknown.

METHODS

We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of T2D with the risk of ALS in both European and East Asian populations. Our analysis was implemented using summary statistics obtained from large-scale genome-wide association studies with ~660,000 individuals for T2D and ~81,000 individuals for ALS in the European population, and ~191,000 individuals for T2D and ~4100 individuals for ALS in the East Asian population. The causal relationship between T2D and ALS in both populations was estimated using the inverse-variance-weighted methods and was further validated through extensive complementary and sensitivity analyses.

RESULTS

Using multiple instruments that were strongly associated with T2D, a negative association between T2D and ALS was identified in the European population with the odds ratio (OR) estimated to be 0.93 (95% CI 0.88-0.99, p = 0.023), while a positive association between T2D and ALS was observed in the East Asian population with OR = 1.28 (95% CI 0.99-1.62, p = 0.058). These results were robust against instrument selection, various modeling misspecifications, and estimation biases, with the Egger regression and MR-PRESSO ruling out the possibility of horizontal pleiotropic effects of instruments. However, no causal association was found between T2D-related exposures (including glycemic traits) and ALS in the European population.

CONCLUSION

Our results provide new evidence supporting the causal neuroprotective role of T2D on ALS in the European population and provide empirically suggestive evidence of increasing risk of T2D on ALS in the East Asian population. Our results have an important implication on ALS pathology, paving ways for developing therapeutic strategies across multiple populations.

LIN28B affects gene expression at the hypothalamic-pituitary axis and serum testosterone levels

Genome-wide association studies (GWAS) have recurrently associated sequence variation nearby LIN28B with pubertal timing, growth and disease. However, the biology linking LIN28B with these traits is still poorly understood. With our study, we sought to elucidate the mechanisms behind the LIN28B associations, with a special focus on studying LIN28B function at the hypothalamic-pituitary (HP) axis that is ultimately responsible for pubertal onset. Using CRISPR-Cas9 technology, we first generated lin28b knockout (KO) zebrafish. Compared to controls, the lin28b KO fish showed both accelerated growth tempo, reduced adult size and increased expression of mitochondrial genes during larval development. Importantly, data from the knockout zebrafish models and adult humans imply that LIN28B expression has potential to affect gene expression in the HP axis. Specifically, our results suggest that LIN28B expression correlates positively with the expression of ESR1 in the hypothalamus and POMC in the pituitary. Moreover, we show how the pubertal timing advancing allele (T) for rs7759938 at the LIN28B locus associates with higher testosterone levels in the UK Biobank data. Overall, we provide novel evidence that LIN28B contributes to the regulation of sex hormone pathways, which might help explain why the gene associates with several distinct traits.

Genetic correlates of social stratification in Great Britain

Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Advances in asthma and allergic disease genetics: Is bigger always better?

This review focuses on genome-wide association studies (GWASs) of asthma and allergic diseases published between January 1, 2018, and June 30, 2019. During this time period, there were 38 GWASs reported in 19 articles, including the largest performed to date for many of these conditions. Overall, we learned that childhood-onset asthma is associated with the most independent loci compared with other defined groups of asthma and allergic disease cases; adult-onset asthma and moderate-to-severe asthma are associated with fewer genes, which are largely a subset of those associated with childhood-onset asthma. There is significant genetic overlap between asthma and allergic diseases, particularly with respect to childhood-onset asthma, which involves genes that reflect the importance of barrier function biology, and to HLA region genes, which are the most frequently associated genes overall in both groups of diseases. Although the largest GWASs in African American and Latino/Hispanic populations were reported during this period, they are still significantly underpowered compared with studies reported in populations of European ancestry, highlighting the need for larger studies, particularly in patients with childhood-onset asthma and allergic diseases, in these important populations that carry the greatest burden of disease.

Telomere length and aging-related outcomes in humans: A Mendelian randomization study in 261,000 older participants

Inherited genetic variation influencing leukocyte telomere length provides a natural experiment for testing associations with health outcomes, more robust to confounding and reverse causation than observational studies. We tested associations between genetically determined telomere length and aging‐related health outcomes in a large European ancestry older cohort. Data were from n = 379,758 UK Biobank participants aged 40–70, followed up for mean of 7.5 years (n = 261,837 participants aged 60 and older by end of follow‐up). Thirteen variants strongly associated with longer telomere length in peripheral white blood cells were analyzed using Mendelian randomization methods with Egger plots to assess pleiotropy. Variants in TERC, TERT, NAF1, OBFC1, and RTEL1 were included, and estimates were per 250 base pairs increase in telomere length, approximately equivalent to the average change over a decade in the general white population. We highlighted associations with false discovery rate‐adjusted p‐values smaller than .05. Genetically determined longer telomere length was associated with lowered risk of coronary heart disease (CHD; OR = 0.95, 95% CI: 0.92–0.98) but raised risk of cancer (OR = 1.11, 95% CI: 1.06–1.16). Little evidence for associations were found with parental lifespan, centenarian status of parents, cognitive function, grip strength, sarcopenia, or falls. The results for those aged 60 and older were similar in younger or all participants. Genetically determined telomere length was associated with increased risk of cancer and reduced risk of CHD but little change in other age‐related health outcomes. Telomere lengthening may offer little gain in later‐life health status and face increasing cancer risks.

Depression increases the genetic susceptibility to high body mass index: Evidence from UK Biobank

BACKGROUND

This study aimed to explore the association between depression and body mass index (BMI), and to investigate whether genetic susceptibility to high BMI is different among individuals with or without depression.

METHODS

We used data on 251,125 individuals of white British ancestry from the UK Biobank. We conducted Mendelian randomization (MR) analysis to test for a causal association between depression and BMI using a major depressive disorder (MDD)-related genetic risk score (GRSMDD ) as an instrument for depression. We also examined whether depression modifies genetic susceptibility to high BMI, by investigating the interaction between depression and the BMI-related GRSBMI .

RESULTS

We found observational and genetic evidence for an association between depression and BMI (MR beta: 0.09, 95% confidence interval [CI] 0.04-0.13). Further, the contribution of genetic risk to high BMI was higher among individuals with depression compared to controls. Carrying 10 additional BMI increasing alleles was associated with 0.24 standard deviation (SD; 95%CI 0.23-0.25) higher BMI among depressed individuals compared to 0.20 SD (95%CI 0.19-0.21) higher in controls, which corresponds to 3.4 kg and 2.8 kg extra weight for an individual of average height. Amongst the individual loci, the evidence for interaction was most notable for a variant near MC4R, a gene known to affect both appetite regulation and the hypothalamic-pituitary adrenal axis (pinteraction  = 5.7 × 10-5 ).

CONCLUSION

Genetic predisposition to high BMI was higher among depressed than to nondepressed individuals. This study provides support for a possible role of MC4R in the link between depression and obesity.

The Genetic Links to Anxiety and Depression (GLAD) Study: Online recruitment into the largest recontactable study of depression and anxiety

BACKGROUND

Anxiety and depression are common, debilitating and costly. These disorders are influenced by multiple risk factors, from genes to psychological vulnerabilities and environmental stressors, but research is hampered by a lack of sufficiently large comprehensive studies. We are recruiting 40,000 individuals with lifetime depression or anxiety and broad assessment of risks to facilitate future research.

METHODS

The Genetic Links to Anxiety and Depression (GLAD) Study (www.gladstudy.org.uk) recruits individuals with depression or anxiety into the NIHR Mental Health BioResource. Participants invited to join the study (via media campaigns) provide demographic, environmental and genetic data, and consent for medical record linkage and recontact.

RESULTS

Online recruitment was effective; 42,531 participants consented and 27,776 completed the questionnaire by end of July 2019. Participants' questionnaire data identified very high rates of recurrent depression, severe anxiety, and comorbidity. Participants reported high rates of treatment receipt. The age profile of the sample is biased toward young adults, with higher recruitment of females and the more educated, especially at younger ages.

DISCUSSION

This paper describes the study methodology and descriptive data for GLAD, which represents a large, recontactable resource that will enable future research into risks, outcomes, and treatment for anxiety and depression.

Long-Chain ω-3 Levels Are Associated With Increased Alcohol Sensitivity in a Population-Based Sample of Adolescents

BACKGROUND

The levels of the ω-3 long-chain polyunsaturated fatty acids (ω-3 LC-PUFAs), including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been associated with alcohol sensitivity in vertebrate and invertebrate model systems, but prior studies have not examined this association in human samples despite evidence of associations between ω-3 LC-PUFA levels and alcohol-related phenotypes. Both alcohol sensitivity and ω-3 LC-PUFA levels are impacted by genetic factors, and these influences may contribute to observed associations between phenotypes. Given the potential for using EPA and DHA supplementation in adjuvant care for alcohol misuse and other outcomes, it is important to clarify how ω-3 LC-PUFA levels relate to alcohol sensitivity.

METHODS

Analyses were conducted using data from the Avon Longitudinal Study of Parents and Children. Plasma ω-3 LC-PUFA levels were measured at ages 15.5 and 17.5. Participants reported on their initial alcohol sensitivity using the early drinking Self-Rating of the Effects of Alcohol (SRE-5) scale, for which more drinks needed for effects indicates lower levels of response per drink, at ages 15.5, 16.5, and 17.5. Polygenic liability for alcohol consumption, alcohol problems, EPA levels, and DHA levels was derived using summary statistics from large, publicly available datasets. Linear regressions were used to examine the cross-sectional and longitudinal associations between ω-3 LC-PUFA levels and SRE scores.

RESULTS

Age 15.5 ω-3 LC-PUFA levels were negatively associated with contemporaneous SRE scores and with age 17.5 SRE scores. One modest association (p = 0.02) between polygenic liability and SRE scores was observed, between alcohol problems-based polygenic risk scores (PRS) and age 16.5 SRE scores. Tests of moderation by genetic liability were not warranted.

CONCLUSIONS

Plasma ω-3 LC-PUFA levels may be related to initial sensitivity to alcohol during adolescence. These data indicate that diet-related factors have the potential to impact humans' earliest responses to alcohol exposure.

Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of pairwise IBD proportions by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD sharing ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim.

Investigating Causality Between Blood Metabolites and Emotional and Behavioral Responses to Traumatic Stress: a Mendelian Randomization Study

To investigate the causal relationship between blood metabolites and traits related to trauma-response, we combined genome-wide and metabolome-wide datasets generated from large-scale cohorts. Five trauma-response traits ascertained in the UK Biobank (52,816 < N < 117,900 individuals) were considered: (i) "Avoided activities/situations because of previous stressful experience" (Avoidance); (ii) "Felt distant from other people" (Distant); (iii) "Felt irritable/had angry outbursts" (Irritable); (iv) "Felt very upset when reminded of stressful experience" (Upset); (v) "Repeated disturbing thoughts of stressful experience". These were investigated with respect to 52 blood metabolites tested in a previous genome-wide-association study (N = 24,925 European-ancestry individuals). Linkage disequilibrium score regression, polygenic risk scoring (PRS), and Mendelian randomization were applied to the datasets. We observed that 14 metabolites were genetically correlated with trauma-response traits (p < 0.05). High-resolution PRS of 4 metabolites (citrate; glycoprotein acetyls; concentration of large very-low-density lipoproteins (VLDL) particles (LVLDLP); total cholesterol in medium particles of VLDL (MVLDLC)) were associated with trauma-response traits (false discovery rate Q < 10%). These genetic associations were partially due to causal relationships (Citrate→Upset β = - 0.058, p = 9.1 × 10^-4; Glycoproteins→Avoidance β = 0.008, p = 0.003; LVLDLP→Distant β = 0.008, p = 0.022; MVLDLC→Avoidance β = 0.019, p = 3 × 10^-4). No reverse associations were observed. In conclusion, our study supports causal relationships between certain blood metabolites and emotional and behavioral responses to traumatic experiences.

Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment

Previous studies have suggested that altered asymmetry of the planum temporale (PT) is associated with neurodevelopmental disorders, including dyslexia, schizophrenia, and autism. Shared genetic factors have been suggested to link PT asymmetry to these disorders. In a dataset of unrelated subjects from the general population (UK Biobank, N = 18,057), we found that PT volume asymmetry had a significant heritability of roughly 14%. In genome-wide association analysis, two loci were significantly associated with PT asymmetry, including a coding polymorphism within the gene ITIH5 that is predicted to affect the protein's function and to be deleterious (rs41298373, p = 2.01 × 10-15), and a locus that affects the expression of the genes BOK and DTYMK (rs7420166, p = 7.54 × 10-10). DTYMK showed left-right asymmetry of mRNA expression in post mortem PT tissue. Cortex-wide mapping of these SNP effects revealed influences on asymmetry that went somewhat beyond the PT. Using publicly available genome-wide association statistics from large-scale studies, we saw no significant genetic correlations of PT asymmetry with autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, educational attainment or intelligence. Of the top two individual loci associated with PT asymmetry, rs41298373 showed a tentative association with intelligence (unadjusted p = .025), while the locus at BOK/DTYMK showed tentative association with educational attainment (unadjusted Ps < .05). These findings provide novel insights into the genetic contributions to human brain asymmetry, but do not support a substantial polygenic association of PT asymmetry with cognitive variation and mental disorders, as far as can be discerned with current sample sizes.

Accurate, scalable and integrative haplotype estimation

The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

Screening Human Embryos for Polygenic Traits Has Limited Utility

The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.

Brain age prediction using deep learning uncovers associated sequence variants

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=12378$$\end{document}N=12378, replication set: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=4456$$\end{document}N=4456) yielded two sequence variants, rs1452628-T (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =-0.08$$\end{document}β=−0.08, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=1.15\times{10}^{-9}$$\end{document}P=1.15×10−9) and rs2435204-G (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0.102$$\end{document}β=0.102, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=9.73\times 1{0}^{-12}$$\end{document}P=9.73×10−12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).

Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.

Tygerberg Research Ubuntu-Inspired Community Engagement Model: Integrating Community Engagement into Genomic Biobanking

Introduction: Community engagement (CE) is an ethical imperative in research, but the knowledge base for what constitutes effective and ethically sound CE is limited. Ubuntu, as a component of responsive communitarianism where communal welfare is valued together with individual autonomy, is useful in furthering our understanding of effective CE and how it could best be achieved. Similarly, a relative solidarity model serves as a compromise between extreme individualism and extreme communalism and is more appropriate in a heterogenous African context. Approaching CE from an Ubuntu philosophical perspective in southern Africa is particularly important in genomic biobanking, given the implications for individuals, families, and communities.

Discussion: CE is often implemented in a tokenistic manner as an ancillary component of research. Understanding consent information is challenging where genomic biobanking is concerned due to scientific complexity. We started a process of CE around genomic biobanking and conducted empirical research in an attempt to develop a model to promote effective and ethically sound CE, using relative solidarity to create a nuanced application of Ubuntu. The TRUCE model is an eight-step model that uses social mapping to identify potential communities, establishes the scope of CE, and requires that communities are approached early. Co-creation strategies for CE are encouraged and co-ownership of knowledge production is emphasized. Recruiting and engaging communities at each stage of research is necessary. Evaluation and adaptation of CE strategies are included. Discussion and dissemination of results after the research is completed are encouraged.

Conclusions: There is a significant gap between the theory of CE and its authentic application to research in Africa. This Ubuntu-inspired model facilitates bridging that gap and is particularly suited to genomic biobanking. The CE model enhances and complements the consent process and should be integrated into research as a funding and regulatory requirement where applicable.

Deciphering the Biological Mechanisms Underlying the Genome-Wide Associations between Computerized Device Use and Psychiatric Disorders

Computerized device use (CDU) is societally ubiquitous but its effects on mental health are unknown. We performed genetic correlation, Mendelian randomization, and latent causal variable analyses to identify shared genetic mechanisms between psychiatric disorders (Psychiatric Genomics Consortium; 14,477 < N < 150,064) and CDU (UK Biobank; N = 361,194 individuals). Using linkage disequilibrium score regression, we detected strong genetic correlations between "weekly usage of mobile phone in last 3 months" (PhoneUse) vs. attention deficit hyperactivity disorder (ADHD; rg = 0.425, p = 4.59 × 10-29) and "plays computer games" (CompGaming) vs. schizophrenia (SCZ; rg = -0.271, p = 7.16 × 10-26). Focusing on these correlations, we used two sample MRs to detect the causal relationships between trait pairs by treating single nucleotide polymorphisms as non-modifiable risk factors underlying both phenotypes. Significant bidirectional associations were detected (PhoneUse→ADHD β = 0.132, p = 1.89 × 10-4 and ADHD→PhoneUse β = 0.084, p = 2.86 × 10-10; CompGaming→SCZ β = -0.02, p = 6.46 × 10-25 and CompGaming→SCZ β = -0.194, p = 0.005) and the latent causal variable analyses did not support a causal relationship independent of the genetic correlations between these traits. This suggests that molecular pathways contribute to the genetic overlap between these traits. Dopamine transport enrichment (Gene Ontology:0015872, pSCZvsCompGaming = 2.74 × 10-10) and DRD2 association (pSCZ = 7.94 × 10-8; pCompGaming = 3.98 × 10-25) were detected in SCZ and CompGaming and support their negative correlative relationship. FOXP2 was significantly associated with ADHD (p = 9.32 × 10-7) and PhoneUse (p = 9.00 × 10-11) with effect directions concordant with their positive genetic correlation. Our study demonstrates that epidemiological associations between psychiatric disorders and CDUs are due, in part, to the molecular mechanisms shared between them rather than a causal relationship. Our findings imply that biological mechanisms underlying CDU contribute to the psychiatric phenotype manifestation.

Genetic evidence for assortative mating on alcohol consumption in the UK Biobank

Alcohol use is correlated within spouse-pairs, but it is difficult to disentangle effects of alcohol consumption on mate-selection from social factors or the shared spousal environment. We hypothesised that genetic variants related to alcohol consumption may, via their effect on alcohol behaviour, influence mate selection. Here, we find strong evidence that an individual's self-reported alcohol consumption and their genotype at rs1229984, a missense variant in ADH1B, are associated with their partner's self-reported alcohol use. Applying Mendelian randomization, we estimate that a unit increase in an individual's weekly alcohol consumption increases partner's alcohol consumption by 0.26 units (95% C.I. 0.15, 0.38; P = 8.20 × 10-6). Furthermore, we find evidence of spousal genotypic concordance for rs1229984, suggesting that spousal concordance for alcohol consumption existed prior to cohabitation. Although the SNP is strongly associated with ancestry, our results suggest some concordance independent of population stratification. Our findings suggest that alcohol behaviour directly influences mate selection.

Big Data in Medicine, the Present and Hopefully the Future

The emergence of data coming from different venues, as several “omic” approaches, is providing already compelling evidence that the smart use of this information could provide invaluable information to prevent, diagnose and treat human diseases. However, the most daunting challenges remain ahead, as the explosive accumulation of data from additional perspectives, including social graphs, biosensors, and imaging, promise to deliver crucial information that could be exploited for the improvement of the entire human race, both in developed, and developing countries, optimizing health expenses and reaching also the less fortunate sections of the societies. And yet, formidable challenges remain, that pertain for the most part to the collection of the data, their organization, and most relevantly their integration. Here we provide few, pointed examples to the present relevance of these big data approaches in human health as well potential road maps toward the implementation of broader data collections and analyses.

Deep Feature Selection and Causal Analysis of Alzheimer's Disease

Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer’s disease (AD). However, it is also well known that DCNNs are “black boxes” owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the development of AD and causal paths from genetic variants to AD via image mediation. The proposed algorithm is applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with diffusion tensor imaging (DTI) in 151 subjects (51 AD and 100 non-AD) who were measured at four time points of baseline, 6 months, 12 months, and 24 months. The algorithm identified brain regions underlying AD consisting of the temporal lobes (including the hippocampus) and the ventricular system.

Genetic variants of calcium and vitamin D metabolism in kidney stone disease

Kidney stone disease (nephrolithiasis) is a major clinical and economic health burden with a heritability of ~45–60%. We present genome-wide association studies in British and Japanese populations and a trans-ethnic meta-analysis that include 12,123 cases and 417,378 controls, and identify 20 nephrolithiasis-associated loci, seven of which are previously unreported. A CYP24A1 locus is predicted to affect vitamin D metabolism and five loci, DGKD, DGKH, WDR72, GPIC1, and BCR, are predicted to influence calcium-sensing receptor (CaSR) signaling. In a validation cohort of only nephrolithiasis patients, the CYP24A1-associated locus correlates with serum calcium concentration and a number of nephrolithiasis episodes while the DGKD-associated locus correlates with urinary calcium excretion. In vitro, DGKD knockdown impairs CaSR-signal transduction, an effect rectified with the calcimimetic cinacalcet. Our findings indicate that studies of genotype-guided precision-medicine approaches, including withholding vitamin D supplementation and targeting vitamin D activation or CaSR-signaling pathways in patients with recurrent kidney stones, are warranted.

Kidney stones form in the presence of overabundance of crystal-forming substances such as Ca²⁺ and oxalate. Here, the authors report genome-wide association analyses for kidney stone disease, report seven previously unknown loci and find that some of these loci also associate with Ca²⁺ concentration and excretion.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these "Mass Cytometry Imaging" (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.

Computational tools for geroscience

The rapid progress of the past three decades has led the geroscience field near a point where human interventions in aging are plausible. Advances across scientific areas, such as high throughput "-omics" approaches, have led to an exponentially increasing quantity of data available for biogerontologists. To best translate the lifespan and healthspan extending interventions discovered by basic scientists into preventative medicine, it is imperative that the current data are comprehensively utilized to generate testable hypotheses about translational interventions. Building a translational pipeline for geroscience will require both systematic efforts to identify interventions that extend healthspan across taxa and diagnostics that can identify patients who may benefit from interventions prior to the onset of an age-related morbidity. Databases and computational tools that organize and analyze both the wealth of information available on basic biogerontology research and clinical data on aging populations will be critical in developing such a pipeline. Here, we review the current landscape of databases and computational resources available for translational aging research. We discuss key platforms and tools available for aging research, with a focus on how each tool can be used in concert with hypothesis driven experiments to move closer to human interventions in aging.

Long-Term Cardiovascular Risk in Women With Hypertension During Pregnancy

BACKGROUND

History of a hypertensive disorder of pregnancy (HDP) among women may be useful to refine atherosclerotic cardiovascular disease risk assessments. However, future risk of diverse cardiovascular conditions in asymptomatic middle-aged women with prior HDP remains unknown.

OBJECTIVES

The purpose of this study was to examine the long-term incidence of diverse cardiovascular conditions among middle-aged women with and without prior HDP.

METHODS

Women in the prospective, observational UK Biobank age 40 to 69 years who reported ≥1 live birth were included. Noninvasive arterial stiffness measurement was performed in a subset of women. Cox models were fitted to associate HDP with incident cardiovascular diseases. Causal mediation analyses estimated the contribution of conventional risk factors to observed associations.

RESULTS

Of 220,024 women included, 2,808 (1.3%) had prior HDP. The mean age at baseline was 57.4 ± 7.8 years, and women were followed for median 7 years (interquartile range: 6.3 to 7.7 years). Women with HDP had elevated arterial stiffness indexes and greater prevalence of chronic hypertension compared with women without HDP. Overall, 7.0 versus 5.3 age-adjusted incident cardiovascular conditions occurred per 1,000 women-years for women with versus without prior HDP, respectively (p = 0.001). In analysis of time-to-first incident cardiovascular diagnosis, prior HDP was associated with a hazard ratio (HR) of 1.3 (95% CI: 1.04 to 1.60; p = 0.02). HDP was associated with greater incidence of CAD (HR: 1.8; 95% CI: 1.3 to 2.6; p < 0.001), heart failure (HR: 1.7; 95% CI: 1.04 to 2.60; p = 0.03), aortic stenosis (HR: 2.9; 95% CI: 1.5 to 5.4; p < 0.001), and mitral regurgitation (HR: 5.0; 95% CI: 1.5 to 17.1; p = 0.01). In causal mediation analyses, chronic hypertension explained 64% of HDP's association with CAD and 49% of HDP's association with heart failure.

CONCLUSIONS

Hypertensive disorders of pregnancy are associated with accelerated cardiovascular aging and more diverse cardiovascular conditions than previously appreciated, including valvular heart disease. Cardiovascular risk after HDP is largely but incompletely mediated by development of chronic hypertension.

Improved polygenic prediction by Bayesian multiple regression on summary statistics

Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R² by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.

Characterization of Prevalence and Health Consequences of Uniparental Disomy in Four Million Individuals from the General Population

Meiotic nondisjunction and resulting aneuploidy can lead to severe health consequences in humans. Aneuploidy rescue can restore euploidy but may result in uniparental disomy (UPD), the inheritance of both homologs of a chromosome from one parent with no representative copy from the other. Current understanding of UPD is limited to ∼3,300 case subjects for which UPD was associated with clinical presentation due to imprinting disorders or recessive diseases. Thus, the prevalence of UPD and its phenotypic consequences in the general population are unknown. We searched for instances of UPD across 4,400,363 consented research participants from the personal genetics company 23andMe, Inc., and 431,094 UK Biobank participants. Using computationally detected DNA segments identical-by-descent (IBD) and runs of homozygosity (ROH), we identified 675 instances of UPD across both databases. We estimate that UPD is twice as common as previously thought, and we present a machine-learning framework to detect UPD using ROH. While we find a nominally significant association between UPD of chromosome 22 and autism risk, we do not find significant associations between UPD and deleterious traits in the 23andMe database.

Monogenic and Polygenic Contributions to Atrial Fibrillation Risk: Results From a National Biobank

RATIONALE

Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF.

OBJECTIVE

We sought to determine the contribution of rare and common genetic variation to AF risk in the general population.

METHODS

The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk.

RESULTS

The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P=2.50×10^-8). The association with AF was more significant (odds ratio, 6.15, P=3.26×10^-14) when restricting to LOF variants located in exons highly expressed in cardiac tissue (TTN_LOF). Overall, 0.44% of individuals carried TTN_LOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTN_LOF variants only accounted for 0.2%.

CONCLUSIONS

Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTN_LOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.

Evolutionarily conserved regulation of sleep by epidermal growth factor receptor signaling

The genetic bases for most human sleep disorders and for variation in human sleep quantity and quality are largely unknown. Using the zebrafish, a diurnal vertebrate, to investigate the genetic regulation of sleep, we found that epidermal growth factor receptor (EGFR) signaling is necessary and sufficient for normal sleep levels and is required for the normal homeostatic response to sleep deprivation. We observed that EGFR signaling promotes sleep via mitogen-activated protein kinase/extracellular signal-regulated kinase and RFamide neuropeptide signaling and that it regulates RFamide neuropeptide expression and neuronal activity. Consistent with these findings, analysis of a large cohort of human genetic data from participants of European ancestry revealed that common variants in genes within the EGFR signaling pathway are associated with variation in human sleep quantity and quality. These results indicate that EGFR signaling and its downstream pathways play a central and ancient role in regulating sleep and provide new therapeutic targets for sleep disorders.

Health effects associated with serum calcium concentrations: evidence from MR-PheWAS analysis in UK Biobank

We conducted a phenome-wide Mendelian randomization analysis (MR-PheWAS) to survey health effects associated with high normal serum calcium. We found causal evidence for conditions related to renal function, bone and joint health, and cardiovascular risk. These conditions collectively suggest that tissue calcification may be a key mechanism through which serum calcium influences health.

INTRODUCTION

Calcium is essential for the normal functioning of the cardiovascular system, muscles, and nerves. In this MR-PheWAS study, we sought to capture the totality of health effects associated with high normal serum calcium.

METHODS

We used data from up to 337,535 UK Biobank participants, and tested for associations between calcium genetic score (calcium-GS) and 925 disease outcomes, with follow-up analyses using complementary MR methods.

RESULTS

Calcium-GS was robustly associated with serum calcium concentration (F statistics = 349). After multiple testing correction (P < 1.62E-4), we saw genetic evidence for an association between high serum calcium and urinary calculus (OR per 1 mg/dl 3.5, 95%CI 1.3-9.2), renal colic (9.1, 95%CI 2.5-33.5), and allergy/adverse effect of penicillin (2.2, 95%CI 1.5-3.3). Secondary analyses with independent replication from consortia meta-analyses suggested further effects on myocardial infarction and osteoarthrosis.

CONCLUSION

We found causal evidence for effects of high normal serum calcium with conditions related to renal function, bone and joint health, and cardiovascular risk, which may collectively reflect influences on tissue calcification and immune function.

Genetic predisposition to mosaic Y chromosome loss in blood

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.

Scaling computational genomics to millions of individuals with GPUs

Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5–10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease

Venous thromboembolism is a significant cause of mortality1, yet its genetic determinants are incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank, with testing of approximately 13 million DNA sequence variants for association with venous thromboembolism (26,066 cases and 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 venous thromboembolism cases and 167,295 controls. We identified 22 previously unknown loci, bringing the total number of venous thromboembolism-associated loci to 33, and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for venous thromboembolism that identifies 5% of the population at an equivalent incident venous thromboembolism risk to carriers of the established factor V Leiden p.R506Q and prothrombin G20210A mutations. Our data provide mechanistic insights into the genetic epidemiology of venous thromboembolism and suggest a greater overlap among venous and arterial cardiovascular disease than previously thought.

Genetic correlations between pain phenotypes and depression and neuroticism

Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. In this study, we estimated the genetic correlations of eight pain phenotypes (defined by the UK Biobank, n = 151,922–226,683) with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. We also used the LDSC software to calculate the genetic correlations among pain phenotypes. All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h² = 0.31, standard error = 0.072). Many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Our results suggest that pain, neuroticism and depression share partially overlapping genetic risk factors.

What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

As more and more health systems have converted to the use of electronic health records, the amount of searchable and analyzable data is exploding. This includes not just provider or laboratory created data but also data collected by instruments, personal devices, and patients themselves, among others. This has led to more attention being paid to the analysis of these data to answer previously unaddressed questions. This is especially important given the number of therapies previously found to be beneficial in clinical trials that are currently being re-scrutinized. Because there are orders of magnitude more information contained in these data sets, a fundamentally different approach needs to be taken to their processing and analysis and the generation of knowledge. Health care and medicine are drivers of this phenomenon and will ultimately be the main beneficiaries. Concurrently, many different types of questions can now be asked using these data sets. Research groups have become increasingly active in mining large data sets, including nationwide health care databases, to learn about associations of medication use and various unrelated diseases such as cancer. Given the recent increase in research activity in this area, its promise to radically change clinical research, and the relative lack of widespread knowledge about its potential and advances, we surveyed the available literature to understand the strengths and limitations of these new tools. We also outline new databases and techniques that are available to researchers worldwide, with special focus on work pertaining to the broad and rapid monitoring of drug safety and secondary effects.

Integrating transcriptome-wide association study and mRNA expression profiling identified candidate genes and pathways associated with osteomyelitis

Objective: Osteomyelitis (OM) is an acute or chronic inflammatory process, characterized by severe inflammation and progressive bone destruction. Limited efforts have been made to explore the genetic basis of OM.Method: The genome-wide association study data set of OM was obtained from the UK Biobank. A transcriptome-wide association study (TWAS) of OM was conducted by the FUSION tool using the gene expression reference weights of lymphocytes and blood. The OM-associated genes identified by TWAS were subjected to gene ontology (GO) enrichment analysis to explore OM-related gene sets. The TWAS results of OM were finally compared with a genome-wide mRNA expression profiling of OM to detect common genes and gene sets.Results: TWAS of OM detected 86 genes for lymphocytes and 387 genes for blood. Comparing the genes identified by TWAS and mRNA expression profiling detected eight common genes for OM, including VWF (p_TWAS = 0.0030, p_mRNA = 3.44 × 10^-9), CCDC50 (p_TWAS = 0.0130, p_mRNA = 0.0003), and TPD52 (p_TWAS = 0.0180, p_mRNA = 1 × 10^-6). GO analysis of the genes identified by TWAS detected multiple OM-associated GO terms, e.g. peroxisomal matrix (p_TWAS = 0.0082), extracellular exosome (p_TWAS = 0.0248), and monooxygenase activity (p_TWAS = 0.0040). Further comparing the GO results of TWAS and mRNA expression profiling detected one common GO term, named extracellular exosome (p_TWAS = 0.0248, p_mRNA = 0.0027).Conclusion: This integrative study of TWAS and mRNA expression profiling detected multiple candidate genes and GO terms for OM. Our results provide novel clues for understanding the pathogenesis of OM.

Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis

Uterine leiomyomata (UL) are the most common neoplasms of the female reproductive tract and primary cause for hysterectomy, leading to considerable morbidity and high economic burden. Here we conduct a GWAS meta-analysis in 35,474 cases and 267,505 female controls of European ancestry, identifying eight novel genome-wide significant (P < 5 × 10^-8) loci, in addition to confirming 21 previously reported loci, including multiple independent signals at 10 loci. Phenotypic stratification of UL by heavy menstrual bleeding in 3409 cases and 199,171 female controls reveals genome-wide significant associations at three of the 29 UL loci: 5p15.33 (TERT), 5q35.2 (FGFR4) and 11q22.3 (ATM). Four loci identified in the meta-analysis are also associated with endometriosis risk; an epidemiological meta-analysis across 402,868 women suggests at least a doubling of risk for UL diagnosis among those with a history of endometriosis. These findings increase our understanding of genetic contribution and biology underlying UL development, and suggest overlapping genetic origins with endometriosis.

External Replication of Urinary Bladder Cancer Prognostic Polymorphisms in the UK Biobank

Introduction: Multiple studies have reported genetic associations with prognostic outcomes of urinary bladder cancer. However, the lack of replication of these associations prohibits establishing further evidence-based research directions. Moreover, there is a lack of independent bladder cancer patient samples that contain prognostic measures, making genetic replication analyses even more challenging.

Materials and Methods: We have identified 1,534 eligible patients and used data on Hospital Episode Statistics in the UK Biobank to model variables of otherwise non-collected events on bladder cancer recurrence and progression. Data on survival was extracted from the Death Registry. We have used SNPTEST software to replicate previously reported genetic associations with bladder cancer recurrence (N = 69), progression (N = 23), survival (N = 53), and age at the time of diagnosis (N = 20).

Results: Using our algorithm, we have identified 618 recurrence and 58 UBC progression events. In total, there were 209 deaths (106 UBC-specific). In replication analyses, eight SNPs have reached nominal statistical significance (p < 0.05). Rs2042329 (CWC27) for UBC recurrence; rs804256, rs4639, and rs804276 (in/close to NEIL2) for NMIBC recurrence; rs2293347 (EGFR) for UBC OS; rs3756712 (PDCD6) for NMIBC OS; rs2344673 (RGS5) for MIBC OS, and rs2297518 (NOS2) for UBC progression. However, none have remained significant after adjustments for multiple comparisons.

Discussion: External replication in genetic epidemiology is an essential step to identify credible findings. In our study, we identify potential genetic targets of higher interest for UBC prognosis. In addition, we propose an algorithm for identifying UBC recurrence and progression using routinely-collected data on patient interventions.

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Understanding and Enhancing Sepsis Survivorship. Priorities for Research and Practice

An estimated 14.1 million patients survive sepsis each year. Many survivors experience poor long-term outcomes, including new or worsened neuropsychological impairment; physical disability; and vulnerability to further health deterioration, including recurrent infection, cardiovascular events, and acute renal failure. However, clinical trials and guidelines have focused on shorter-term survival, so there are few data on promoting longer-term recovery. To address this unmet need, the International Sepsis Forum convened a colloquium in February 2018 titled "Understanding and Enhancing Sepsis Survivorship." The goals were to identify gaps and limitations of current research and shorter- and longer-term priorities for understanding and enhancing sepsis survivorship. Twenty-six experts from eight countries participated. The top short-term priorities identified by nominal group technique culminating in formal voting were to better leverage existing databases for research, develop and disseminate educational resources on postsepsis morbidity, and partner with sepsis survivors to define and achieve research priorities. The top longer-term priorities were to study mechanisms of long-term morbidity through large cohort studies with deep phenotyping, build a harmonized global sepsis registry to facilitate enrollment in cohorts and trials, and complete detailed longitudinal follow-up to characterize the diversity of recovery experiences. This perspective reviews colloquium discussions, the identified priorities, and current initiatives to address them.

A strategy for building and using a human reference pangenome

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

Diversity In Precision Medicine And Pharmacogenetics: Methodological And Conceptual Considerations For Broadening Participation

Genome-wide association studies (GWAS) have revealed important links between genetic markers across the human genome and phenotypic traits, including risk factors for disease. Studies have shown that GWAS continue to be overwhelmingly conducted on people of primarily European descent, despite the fact that the vast majority of human genomic variation is present in non-European populations such as those in Africa. To enhance our understanding of diversity in the pharmacogenomics and precision medicine literature, this review provides a window into the representation of biogeographical populations that have been studied for pharmacogenetic traits, such as enzyme metabolism and adverse drug response. Using the Medical Subject Headings (MeSH) ontology search terms in PubMed, studies were identified that are either population-based, or include a description of the study population on the basis of biological or environmental diversity. The results of this scoping review indicate that the majority of relevant papers (>95% of studies tagged in PubMed with MeSH terms “precision medicine” or “pharmacogenetics”, N=23,701) are not annotated with the “population group” MeSH term, suggesting that the majority of studies in this literature are not population-based, or the authors chose not to describe the study population. Among those studies related to pharmacogenetics or precision medicine that are specific to human population groups (N=1006) and were included in the analysis after filtering and screening on eligibility criteria (N=192), the majority of single-population studies included individuals of African, Asian, and European origins, or genetic ancestry. Combining studies of single and multiple populations, 33% involve participants of Asian origin or ancestry; 30% European; 24% African; 10% Hispanic or Latino; and < 3% American Indian or Alaska Native. These data provide a baseline for future comparison, indicating which biogeographic groups have informed the pharmacogenomic knowledgebase specific to diverse human populations. Challenges and potential solutions to improve diversity in the field and in genetics research more broadly are discussed.

A strategy for building and using a human reference pangenome

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

Rare-variant collapsing analyses for complex traits: guidelines and applications

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.