The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

SummaryPopulation health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

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Genome-wide association study identifies 44 independent genomic loci for self-reported adult hearing difficulty in the UK Biobank cohort

10.1101/549071 ◽

2019 ◽

Author(s):

Helena RR. Wells ◽

Maxim B. Freidin ◽

Fatin N. Zainul Abidin ◽

Antony Payton ◽

Piers Dawes ◽

...

Keyword(s):

Hearing Loss ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Age Related ◽

Genome Wide ◽

Hearing Difficulty ◽

The Uk

Age-related hearing impairment (ARHI) is the most common sensory impairment in the aging population; a third of individuals are affected by disabling hearing loss by the age of 651. ARHI is a multifactorial condition caused by both genetic and environmental factors, with estimates of heritability between 35% and 55%2–4. The genetic risk factors and underlying biological pathology of ARHI are largely unknown, meaning that targets for new therapies remain elusive. We performed genome-wide association studies (GWAS) for two self-reported hearing phenotypes, hearing difficulty (HDiff) and hearing aid use (HAid), using over 250,000 UK Biobank5 volunteers aged between 40-69 years. We identified 44 independent genome-wide significant loci (P<5E-08), 33 of which have not previously been associated with any form of hearing loss. Gene sets from these loci are enriched in auditory processes such as synaptic activities, nervous system processes, inner ear morphology and cognition. Immunohistochemistry for protein localisation in adult mouse cochlea indicate metabolic, sensory and neuronal functions for NID2, CLRN2 and ARHGEF28 identified in the GWAS. These results provide new insight into the genetic landscape underlying susceptibility to ARHI.

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Estimating the effective sample size in association studies of quantitative traits

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab057 ◽

2021 ◽

Author(s):

Andrey Ziyatdinov ◽

Jihye Kim ◽

Dmitry Prokopenko ◽

Florian Privé ◽

Fabien Laporte ◽

...

Keyword(s):

Statistical Power ◽

Quantitative Traits ◽

Mixed Model ◽

Association Studies ◽

Effective Sample Size ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Environment Interaction ◽

Gene Environment ◽

The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

Scientific Reports ◽

10.1038/s41598-021-86871-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chao-Yu Guo ◽

Reng-Hong Wang ◽

Hsin-Chou Yang

Keyword(s):

Complex Traits ◽

Association Studies ◽

Association Test ◽

Whole Genome Sequence ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Sequence Kernel Association Test ◽

Gene Environment ◽

Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

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Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

10.1101/2021.12.24.21268381 ◽

2021 ◽

Author(s):

Abhishek Nag ◽

Lawrence Middleton ◽

Ryan S Dhindsa ◽

Dimitrios Vitsios ◽

Eleanor M Wigmore ◽

...

Keyword(s):

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Protein Coding ◽

The Uk ◽

Metabolic Biomarkers ◽

Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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Lifetime depression and age-related changes in body composition, cardiovascular function, grip strength and lung function: sex-specific analyses in the UK Biobank

BJPsych Open ◽

10.1192/bjo.2021.166 ◽

2021 ◽

Vol 7 (S1) ◽

pp. S44-S44

Author(s):

Julian Mutz ◽

Cathryn M Lewis

Keyword(s):

Body Composition ◽

Lung Function ◽

Cardiovascular Function ◽

Healthy Controls ◽

Physiological Measures ◽

Uk Biobank ◽

Mineral Density ◽

Age Related ◽

The Uk ◽

Age Related Changes

AimsIndividuals with mental disorders, on average, die prematurely, have higher levels of physical comorbidities and may experience accelerated ageing. In individuals with lifetime depression and healthy controls, we examined associations between age and multiple physiological measures.MethodThe UK Biobank study recruited >500,000 participants, aged 37–73 years, between 2006–2010. Generalised additive models were used to examine associations between age and grip strength, cardiovascular function, body composition, lung function and bone mineral density. Analyses were conducted separately in males and females with depression compared to healthy controls.ResultAnalytical samples included up to 342,393 adults (mean age = 55.87 years; 52.61% females). We found statistically significant differences between individuals with depression and healthy controls for most physiological measures, with standardised mean differences between -0.145 and 0.156. There was some evidence that age-related changes in body composition, cardiovascular function, lung function and heel bone mineral density followed different trajectories in individuals with depression. These differences did not uniformly narrow or widen with age. For example, BMI in female cases was 1.1 kg/m2 higher at age 40 and this difference narrowed to 0.4 kg/m2 at age 70. In males, systolic blood pressure was 1 mmHg lower in cases at age 45 and this difference widened to 2.5 mmHg at age 65.ConclusionIndividuals with depression differed from healthy controls across a broad range of physiological measures. Differences in ageing trajectories differed by sex and were not uniform across physiological measures, with evidence of both age-related narrowing and widening of case-control differences.

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An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Nature Genetics ◽

10.1038/s41588-018-0107-y ◽

2018 ◽

Vol 50 (5) ◽

pp. 727-736 ◽

Cited By ~ 108

Author(s):

Donna M. Werling ◽

Harrison Brand ◽

Joon-Yong An ◽

Matthew R. Stone ◽

Lingxue Zhu ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Genome Sequence ◽

Association Studies ◽

Analytical Framework ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Whole Genome Sequence ◽

Whole Genome

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Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2021-319508 ◽

2021 ◽

pp. bjophthalmol-2021-319508

Author(s):

Xianwen Shang ◽

Zhuoting Zhu ◽

Yu Huang ◽

Xueli Zhang ◽

Wei Wang ◽

...

Keyword(s):

Heart Disease ◽

Age Related Macular Degeneration ◽

Hospital Inpatient ◽

Uk Biobank ◽

Age Related ◽

Incident Dementia ◽

Increased Risk ◽

The Uk ◽

Systemic Condition

AimsTo examine independent and interactive associations of ophthalmic and systemic conditions with incident dementia.MethodsOur analysis included 12 364 adults aged 55–73 years from the UK Biobank cohort. Participants were assessed between 2006 and 2010 at baseline and were followed up until the early of 2021. Incident dementia was ascertained using hospital inpatient, death records and self-reported data.ResultsOver 1 263 513 person-years of follow-up, 2304 cases of incident dementia were documented. The multivariable-adjusted HRs (95% CI) for dementia associated with age-related macular degeneration (AMD), cataract, diabetes-related eye disease (DRED) and glaucoma at baseline were 1.26 (1.05 to 1.52), 1.11 (1.00 to 1.24), 1.61 (1.30 to 2.00) and (1.07 (0.92 to 1.25), respectively. Diabetes, heart disease, stroke and depression at baseline were all associated with an increased risk of dementia. Of the combination of AMD and a systemic condition, AMD-diabetes was associated with the highest risk for incident dementia (HR (95% CI): 2.73 (1.79 to 4.17)). Individuals with cataract and a systemic condition were 1.19–2.29 times more likely to develop dementia compared with those without cataract and systemic conditions. The corresponding number for DRED and a systemic condition was 1.50–3.24. Diabetes, hypertension, heart disease, depression and stroke newly identified during follow-up mediated the association between cataract and incident dementia as well as the association between DRED and incident dementia.ConclusionsAMD, cataract and DRED but not glaucoma are associated with an increased risk of dementia. Individuals with both ophthalmic and systemic conditions are at higher risk of dementia compared with those with an ophthalmic or systemic condition only.

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Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort

Translational Psychiatry ◽

10.1038/s41398-020-00940-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Steluta Grama ◽

Isabella Willcocks ◽

John J. Hubert ◽

Antonio F. Pardiñas ◽

Sophie E. Legge ◽

...

Keyword(s):

Multiple Testing ◽

Specific Gene ◽

Whole Genome ◽

Uk Biobank ◽

Abnormal Behaviour ◽

Brain Volumes ◽

Polygenic Risk ◽

Subcortical Brain ◽

Gene Sets ◽

The Uk

Abstract Research has shown differences in subcortical brain volumes between participants with schizophrenia and healthy controls. However, none of these differences have been found to associate with schizophrenia polygenic risk. Here, in a large sample (n = 14,701) of unaffected participants from the UK Biobank, we test whether schizophrenia polygenic risk scores (PRS) limited to specific gene-sets predict subcortical brain volumes. We compare associations with schizophrenia PRS at the whole genome level (‘genomic’, including all SNPs associated with the disorder at a p-value threshold < 0.05) with ‘genic’ PRS (based on SNPs in the vicinity of known genes), ‘intergenic’ PRS (based on the remaining SNPs), and genic PRS limited to SNPs within 7 gene-sets previously found to be enriched for genetic association with schizophrenia (‘abnormal behaviour,’ ‘abnormal long-term potentiation,’ ‘abnormal nervous system electrophysiology,’ ‘FMRP targets,’ ‘5HT2C channels,’ ‘CaV2 channels’ and ‘loss-of-function intolerant genes’). We observe a negative association between the ‘abnormal behaviour’ gene-set PRS and volume of the right thalamus that survived correction for multiple testing (ß = −0.031, pFDR = 0.005) and was robust to different schizophrenia PRS p-value thresholds. In contrast, the only association with genomic PRS surviving correction for multiple testing was for right pallidum, which was observed using a schizophrenia PRS p-value threshold < 0.01 (ß = −0.032, p = 0.0003, pFDR = 0.02), but not when using other PRS P-value thresholds. We conclude that schizophrenia PRS limited to functional gene sets may provide a better means of capturing differences in subcortical brain volume than whole genome PRS approaches.

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LPA and APOE are associated with statin selection in the UK Biobank

10.1101/2020.08.28.272765 ◽

2020 ◽

Author(s):

Adam Lavertu ◽

Gregory McInnes ◽

Yosuke Tanigawa ◽

Russ B Altman ◽

Manuel A. Rivas

Keyword(s):

Statin Therapy ◽

High Intensity ◽

Drug Response ◽

Association Studies ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Treatment Decisions ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

The Uk

AbstractGenetics plays a key role in drug response, affecting efficacy and toxicity. Pharmacogenomics aims to understand how genetic variation influences drug response and develop clinical guidelines to aid clinicians in personalized treatment decisions informed by genetics. Although pharmacogenomics has not been broadly adopted into clinical practice, genetics influences treatment decisions regardless. Physicians adjust patient care based on observed response to medication, which may occur as a result of genetic variants harbored by the patient. Here we seek to understand the genetics of drug selection in statin therapy, a class of drugs widely used for high cholesterol treatment. Genetics are known to play an important role in statin efficacy and toxicity, leading to significant changes in patient outcome. We performed genome-wide association studies (GWAS) on statin selection among 59,198 participants in the UK Biobank and found that variants known to influence statin efficacy are significantly associated with statin selection. Specifically, we find that carriers of variants in APOE and LPA that are known to decrease efficacy of treatment are more likely to be on atorvastatin, a stronger statin. Additionally, carriers of the APOE and LPA variants are more likely to be on a higher intensity dose (a dose that reduces low-density lipoprotein cholesterol by greater than 40%) of atorvastatin than non-carriers (APOE: p(high intensity) = 0.16, OR = 1.7, P = 1.64 × 10−4, LPA: p(high intensity) = 0.17, OR = 1.4, P = 1.14 × 10−2). These findings represent the largest genetic association study of statin selection and statin dose association to date and provide evidence for the role of LPA and APOE in statin response, furthering the possibility of personalized statin therapy.

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