scholarly journals Integrative haplotype estimation with sub-linear complexity

2018 ◽  
Author(s):  
Olivier Delaneau ◽  
Jean-Francois Zagury ◽  
Matthew R Robinson ◽  
Jonathan Marchini ◽  
Emmanouil Dermitzakis
Keyword(s):  
Sample Size ◽  
Gold Standard ◽  
Linear Complexity ◽  
Estimation Methods ◽  
Linear Scaling ◽  
Uk Biobank ◽  
Haplotype Estimation ◽  
High Coverage ◽  
Human Genomes ◽  
The Uk

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 new method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear scaling 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 SHAPET4 in an open source format on https://odelaneau.github.io/shapeit4/ 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.

scholarly journals Accurate, scalable and integrative haplotype estimation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Olivier Delaneau ◽  
Jean-François Zagury ◽  
Matthew R. Robinson ◽  
Jonathan L. Marchini ◽  
Emmanouil T. Dermitzakis
Keyword(s):  
Sample Size ◽  
Open Source ◽  
Gold Standard ◽  
Estimation Methods ◽  
Uk Biobank ◽  
Haplotype Estimation ◽  
High Coverage ◽  
Human Genomes ◽  
The Uk

AbstractThe 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.

scholarly journals Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

2021 ◽  
Author(s):  
Nils Kappelmann ◽  
Darina Czamara ◽  
Nicolas Rost ◽  
Sylvain Moser ◽  
Vanessa Schmoll ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Network Analysis ◽  
Inflammatory Markers ◽  
Quality Criteria ◽  
Estimation Methods ◽  
Uk Biobank ◽  
Metabolic Markers ◽  
Model Search ◽  
Tnf Α ◽  
The Uk

ABSTRACTBackgroundAbout every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.MethodsThis study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n=110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n=1,058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n=1,143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three quality criteria were defined to appraise consistency of results across estimation methods, network bootstrapping, and samples.ResultsNetwork analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our quality criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two quality criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.ConclusionsOur findings align with previous studies suggesting that systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

scholarly journals Understanding cognitive impairment in mood disorders: mediation analyses in the UK Biobank cohort

2019 ◽  
Vol 215 (5) ◽  
pp. 683-690 ◽  
Author(s):  
Breda Cullen ◽  
Daniel J. Smith ◽  
Ian J. Deary ◽  
Jill P. Pell ◽  
Katherine M. Keyes ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Cognitive Impairment ◽  
Major Depression ◽  
Mood Disorders ◽  
Psychotropic Medication ◽  
Estimation Methods ◽  
Cardiometabolic Disease ◽  
Visuospatial Memory ◽  
Uk Biobank ◽  
The Uk

BackgroundCognitive impairment is strongly linked with persistent disability in people with mood disorders, but the factors that explain cognitive impairment in this population are unclear.AimsTo estimate the total effect of (a) bipolar disorder and (b) major depression on cognitive function, and the magnitude of the effect that is explained by potentially modifiable intermediate factors.MethodCross-sectional study using baseline data from the UK Biobank cohort. Participants were categorised as having bipolar disorder (n = 2709), major depression (n = 50 975) or no mood disorder (n = 102 931 and n = 105 284). The outcomes were computerised tests of reasoning, reaction time and memory. The potential mediators were cardiometabolic disease and psychotropic medication. Analyses were informed by graphical methods and controlled for confounding using regression, propensity score-based methods and G-computation.ResultsGroup differences of small magnitude were found on a visuospatial memory test. Z-score differences for the bipolar disorder group were in the range −0.23 to −0.17 (95% CI −0.39 to −0.03) across different estimation methods, and for the major depression group they were approximately −0.07 (95% CI −0.10 to −0.03). One-quarter of the effect was mediated via psychotropic medication in the bipolar disorder group (−0.05; 95% CI −0.09 to −0.01). No evidence was found for mediation via cardiometabolic disease.ConclusionsIn a large community-based sample in middle to early old age, bipolar disorder and depression were associated with lower visuospatial memory performance, in part potentially due to psychotropic medication use. Mood disorders and their treatments will have increasing importance for population cognitive health as the proportion of older adults continues to grow.Declaration of interestI.J.D. is a UK Biobank participant. J.P.P. is a member of the UK Biobank Steering Committee.

scholarly journals Understanding Cognitive Impairment in Mood Disorders: Mediation Analyses in the UK Biobank Cohort

2019 ◽  
Author(s):  
Breda Cullen ◽  
Daniel J. Smith ◽  
Ian J. Deary ◽  
Jill P. Pell ◽  
Katherine M. Keyes ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Major Depression ◽  
Mood Disorders ◽  
Psychotropic Medication ◽  
Estimation Methods ◽  
Cardiometabolic Disease ◽  
Visuospatial Memory ◽  
Uk Biobank ◽  
Small Magnitude ◽  
The Uk

AbstractBackgroundCognitive impairment is strongly linked with persistent disability in people with mood disorders, but the factors that explain cognitive impairment in this population are unclear.AimsWe aimed to estimate the total effect of (i) bipolar disorder (BD) and (ii) major depression on cognitive function, and the magnitude of the effect that was explained by potentially modifiable intermediate factors.MethodCross-sectional study using baseline data from the UK Biobank cohort. Participants were categorised as BD (N=2,709), major depression (N=50,975), or no mood disorder (N=102,931 to 105,284). The outcomes were computerised tests of reasoning, reaction time and memory. The potential mediators were cardiometabolic disease and psychotropic medication. Analyses were informed by graphical methods, and controlled for confounding using regression, propensity score-based methods, and G-computation.ResultsGroup differences of small magnitude were found on a visuospatial memory test. Z-score differences for BD were in the range −0.23 to −0.17 (95% CI range −0.39 to −0.03) across different estimation methods, and approximately −0.07 (95% CI −0.10 to −0.03) for major depression. One-quarter of the effect was mediated via psychotropic medication in the BD group (−0.05; 95% CI −0.09 to −0.01). No evidence was found for mediation via cardiometabolic disease.ConclusionsIn a large community-based sample in middle to early old age, BD and depression were associated with lower visuospatial memory performance, in part potentially due to psychotropic medication use. Mood disorders and their treatments will have increasing importance for population cognitive health as the proportion of older adults continues to grow.

scholarly journals Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Oliver S. Burren ◽  
Guillermo Reales ◽  
Limy Wong ◽  
John Bowes ◽  
James C. Lee ◽  
...  
Keyword(s):  
Sample Size ◽  
Genetic Risk ◽  
Association Studies ◽  
Evidence Base ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Immune Mediated ◽  
The Uk ◽  
Shared Risk

Abstract Background Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. Methods Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension “basis” that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. Results We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. Conclusions Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

scholarly journals Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on Hemoglobin A1c

2020 ◽  
Author(s):  
Kenneth E. Westerman ◽  
Jenkai Miao ◽  
Daniel I. Chasman ◽  
Jose C. Florez ◽  
Han Chen ◽  
...  
Keyword(s):  
Sample Size ◽  
Dietary Patterns ◽  
Disease Risk ◽  
Interaction Analysis ◽  
European Ancestry ◽  
Specific Gene ◽  
Large Sample Size ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk

ABSTRACTDiet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards developing precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large sample size of the UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ∼340,000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (p < 5×10−8): two involved dietary patterns (meat pattern with rs147678157 and a fruit &vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534, and milk type [dairy vs. other] with 4:131148078_TAGAA_T). All of these were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked any genetic main effect that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) were identified as interacting with carbohydrate-containing food groups. Some of these interactions showed nominal replication in non-European ancestry UKB subsets, as well as association using alternative measures of glycemia (fasting glucose and follow-up HbA1c measurements). Our results highlight relevant GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

Low birthweight is associated with poorer limb muscle mass and grip strength in middle age: findings from the UK Biobank Imaging Enhancement

2019 ◽  
Author(s):  
Elizabeth Curtis ◽  
Justin Liu ◽  
Kate Ward ◽  
Karen Jameson ◽  
Zahra Raisi-Estabragh ◽  
...  
Keyword(s):  
Grip Strength ◽  
Muscle Mass ◽  
Low Birthweight ◽  
Middle Age ◽  
Limb Muscle ◽  
Uk Biobank ◽  
The Uk

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

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