Estimating Effects and Making Predictions from Genome-Wide Marker Data

In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called ``winner's curse.'' We argue that the classical definition of unbiasedness is not useful in this context and propose to use a different definition of unbiasedness that is a property of the estimator we advocate. We suggest an integrated approach to the estimation of the SNP effects and to the prediction of trait values, treating SNP effects as random instead of fixed effects. Statistical methods traditionally used in the prediction of trait values in the genetics of livestock, which predates the availability of SNP data, can be applied to analysis of GWAS, giving better estimates of the SNP effects and predictions of phenotypic and genetic values in individuals.Comment: Published in at http://dx.doi.org/10.1214/09-STS306 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multi-locus models of genetic risk of disease

Background: Evidence for genetic contribution to complex diseases is described by recurrence risks to relatives of diseased individuals. Genome-wide association studies allow a description of the genetics of the same diseases in terms of risk loci, their effects and allele frequencies. To reconcile the two descriptions requires a model of how risks from individual loci combine to determine an individual's overall risk

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power

Prediction of individual genetic risk to disease from genome-wide association studies

Empirical studies suggest that the effect sizes of individual causal risk alleles underlying complex genetic diseases are small, with most genotype relative risks in the range of 1.1-2.0. Although the increased risk of disease for a carrier is small for any single locus, knowledge of multiple-risk alleles throughout the genome could allow the identification of individuals that are at high risk. In this study, we investigate the number and effect size of risk loci that underlie complex disease constrained by the disease parameters of prevalence and heritability. Then we quantify the value of prediction of genetic risk to disease using a range of realistic combinations of the number, size, and distribution of risk effects that underlie complex diseases. We propose an approach to assess the genetic risk of a disease in healthy individuals, based on dense genome-wide SNP panels. We test this approach using simulation. When the number of loci contributing to the disease is >50, a large case-control study is needed to identify a set of risk loci for use in predicting the disease risk of healthy people not included in the case-control study. For diseases controlled by 1000 loci of mean relative risk of only 1.04, a case-control study with 10,000 cases and controls can lead to selection of ∼75 loci that explain >50% of the genetic variance. The 5% of people with the highest predicted risk are three to seven times more likely to suffer the disease than the population average, depending on heritability and disease prevalence. Whether an individual with known genetic risk develops the disease depends on known and unknown environmental factors

Large-scale genomics unveils the genetic architecture of psychiatric disorders

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment

GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression.

Genetic factors are recognized to contribute to peptic ulcer disease (PUD) and other gastrointestinal diseases, such as gastro-oesophageal reflux disease (GORD), irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). Here, genome-wide association study (GWAS) analyses based on 456,327 UK Biobank (UKB) individuals identify 8 independent and significant loci for PUD at, or near, genes MUC1, MUC6, FUT2, PSCA, ABO, CDX2, GAST and CCKBR. There are previously established roles in susceptibility to Helicobacter pylori infection, response to counteract infection-related damage, gastric acid secretion or gastrointestinal motility for these genes. Only two associations have been previously reported for duodenal ulcer, here replicated trans-ancestrally. The results highlight the role of host genetic susceptibility to infection. Post-GWAS analyses for PUD, GORD, IBS and IBD add insights into relationships between these gastrointestinal diseases and their relationships with depression, a commonly comorbid disorder

Research Review: Polygenic methods and their application to psychiatric traits

Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years. Methods and scope: We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs; a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability. Findings: Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. Sample sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs. Conclusions: Increasing the sample size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of functional analyses that might eventually lead to new prevention and treatment options and to improved biological validity of diagnostic constructs. Polygenic analyses will contribute further to our understanding of complex genetic traits as sample sizes increase and as sample resources become richer in phenotypic descriptors, both in terms of clinical symptoms and of nongenetic risk factors

Timing of Antidepressant Discontinuation During Pregnancy and Postpartum Psychiatric Outcomes in Denmark and Norway

Importance: Approximately half of women discontinue antidepressant use during pregnancy, yet this could lead to relapse in the postpartum period. Objective: To investigate the associations between longitudinal antidepressant fill trajectories during pregnancy and postpartum psychiatric outcomes. Design and setting, and participants: Cohort study using nationwide registers in Denmark and Norway. Participants included 41,475 liveborn singleton pregnancies in Denmark (1997–2016) and 16,459 in Norway (2009–2018) for women who filled at least one antidepressant prescription within six months before pregnancy. Exposures: Antidepressant prescription fills were obtained from the prescription registers. Antidepressant treatment during pregnancy was modeled using longitudinal k-means. Main outcomes and measures: Initiating psycholeptics, psychiatric emergency, or records of self-harm within one year postpartum. Hazard ratio (HR) of each psychiatric outcome was estimated using Cox regression models. Inverse probability of treatment weighting was used to control for confounding. Country-specific HRs were pooled using random-effects meta-analytic models. Results: Of 57,934 pregnancies (mean maternal age range across countries: 30.7– ?? years), [XL1] our trajectories were identified: early discontinuers (about 30% of the population), late discontinuers (previously stable users) (20–25%), late discontinuers (short-term users) (15–20%), and continuers (25–30%). Early discontinuers and late discontinuers (short-term users) had a lower probability of initiating psycholeptics and having postpartum psychiatric emergencies compared to continuers. We found a moderately increased probability of initiation of psycholeptics among late discontinuers (previously stable users) compared to continuers (HR=1.13, 95% CI: 1.03–1.24). This increase in late discontinuers (previously stable users) was more pronounced among women with previous affective disorders (HR=1.28, 95% CI: 1.12–1.47). No association between antidepressant fill trajectories and postpartum self-harm risk was found. Conclusions and Relevance: Using pooled data from Denmark and Norway, we found a moderately elevated probability of initiation of psycholeptics in late discontinuers (previously stable users) compared to continuers. [XL1]Mean (SD) age of the study population was 30.7(5.3) year

Concepts, estimation and interpretation of SNP-based heritability

Narrow-sense heritability (h(2)) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h(2) previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (h(SNPh)(2)). In this Perspective, we discuss recently developed methods to estimate h(SNPh)(2) for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of h(SNPh)(2), definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results

The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936

Background: The DNA methylation-based 'epigenetic clock' correlates strongly with chronological age, but it is currently unclear what drives individual differences. We examine cross-sectional and longitudinal associations between the epigenetic clock and four mortality-linked markers of physical and mental fitness: lung function, walking speed, grip strength and cognitive ability. Methods: DNA methylation-based age acceleration (residuals of the epigenetic clock estimate regressed on chronological age) were estimated in the Lothian Birth Cohort 1936 at ages 70 (n=920), 73 (n=299) and 76 (n=273) years. General cognitive ability, walking speed, lung function and grip strength were measured concurrently. Cross-sectional correlations between age acceleration and the fitness variables were calculated. Longitudinal change in the epigenetic clock estimates and the fitness variables were assessed via linear mixed models and latent growth curves. Epigenetic age acceleration at age 70 was used as a predictor of longitudinal change in fitness. Epigenome-wide association studies (EWASs) were conducted on the four fitness measures. Results: Cross-sectional correlations were significant between greater age acceleration and poorer performance on the lung function, cognition and grip strength measures (r range: -0.07 to -0.05, P range: 9.7 x 10 to 0.024). All of the fitness variables declined over time but age acceleration did not correlate with subsequent change over 6 years. There were no EWAS hits for the fitness traits. Conclusions: Markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration). However, age acceleration does not associate with decline in these measures, at least over a relatively short follow-up