Systematic comparison of family history and polygenic risk across 24 common diseases

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Genetic associations for two biological age measures point to distinct aging phenotypes

Biological age measures outperform chronological age in predicting various aging outcomes, yet little is known regarding genetic predisposition. We performed genome‐wide association scans of two age‐adjusted biological age measures (PhenoAgeAcceleration and BioAgeAcceleration), estimated from clinical biochemistry markers (Levine et al., 2018; Levine, 2013) in European‐descent participants from UK Biobank. The strongest signals were found in the APOE gene, tagged by the two major protein‐coding SNPs, PhenoAgeAccel—rs429358 (APOE e4 determinant) (p = 1.50 × 10⁻⁷²); BioAgeAccel—rs7412 (APOE e2 determinant) (p = 3.16 × 10⁻⁶⁰). Interestingly, we observed inverse APOE e2 and e4 associations and unique pathway enrichments when comparing the two biological age measures. Genes associated with BioAgeAccel were enriched in lipid related pathways, while genes associated with PhenoAgeAccel showed enrichment for immune system, cell function, and carbohydrate homeostasis pathways, suggesting the two measures capture different aging domains. Our study reaffirms that aging patterns are heterogeneous across individuals, and the manner in which a person ages may be partly attributed to genetic predisposition.     

Polygenic score for neuroticism is related to sleep difficulties

Neuroticism, a broad trait measure of the tendency to experience negative emotions and vulnerability to stress, is consistently related to poor sleep quality. Less is known about potential pleiotropy in the genetic risk for high neuroticism and poor sleep. Therefore, the present study examined whether polygenic score (PGS) for neuroticism is related to sleep quality in two large samples of adults. In addition, depressive symptoms, anxiety and phenotypical neuroticism were tested as mediators in both samples. Participants were 8316 individuals aged from 50 to 101 years (mean age = 68.29, SD = 9.83) from the Health and Retirement Study, and 4973 individuals aged from 63 to 67 years (mean age = 64.30, SD = 0.68) from the Wisconsin Longitudinal Study. Participants from both samples were genotyped and answered questions on sleep quality. A higher PGS for neuroticism was related to lower sleep quality concurrently and over time in both samples. Anxiety, depressive symptoms and neuroticism mediated these relationships in the two samples. Although effect sizes were small, the present study provides replicable evidence that individuals with a higher genetic predisposition to experience negative emotions and distress are at risk of sleep difficulties.     

Relationships between substance abuse/dependence and psychiatric disorders based on polygenic scores

Genetic susceptibility to substance use disorders (SUDs) is partially shared between substances. Heritability of any substance dependence, estimated as 54%, is partly explained by additive effects of common variants. Comorbidity between SUDs and other psychiatric disorders is frequent. The present study aims to analyze the additive role of common variants in this comorbidity using polygenic scores (PGSs) based on genome‐wide association study discovery samples of schizophrenia (SCZ), bipolar disorder, attention‐deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder and anxiety disorders, available from large consortia. PGSs were calculated for 534 patients meeting DSM‐IV criteria for dependence of a substance and abuse/dependence of another substance between alcohol, tobacco, cannabis, cocaine, opiates, hypnotics, stimulants, hallucinogens and solvents; and 587 blood donors from the same population, Iberians from Galicia, as controls. Significance of the PGS and percentage of variance explained were calculated by logistic regression. Using discovery samples of similar size, significant associations with SUDs were detected for SCZ PGS. SCZ PGS explained more variance in SUDs than in most psychiatric disorders. Cross‐disorder PGS based on five psychiatric disorders was significant after adjustment for the effect of SCZ PGS. SCZ PGS was significantly higher in women than in men abusing alcohol. Our findings indicate that SUDs share genetic susceptibility with SCZ to a greater extent than with other psychiatric disorders, including externalizing disorders such as attention‐deficit/hyperactivity disorder. Women have lower probability to develop substance abuse/dependence than men at similar PGS probably because of a higher social pressure against excessive drug use in women.     

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program

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Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.     

LipidSeq: a next-generation clinical resequencing panel for monogenic dyslipidemias

We report the design of a targeted resequencing panel for monogenic dyslipidemias, LipidSeq, for the purpose of replacing Sanger sequencing in the clinical detection of dyslipidemia-causing variants. We also evaluate the performance of the LipidSeq approach versus Sanger sequencing in 84 patients with a range of phenotypes including extreme blood lipid concentrations as well as additional dyslipidemias and related metabolic disorders. The panel performs well, with high concordance (95.2%) in samples with known mutations based on Sanger sequencing and a high detection rate (57.9%) of mutations likely to be causative for disease in samples not previously sequenced. Clinical implementation of LipidSeq has the potential to aid in the molecular diagnosis of patients with monogenic dyslipidemias with a high degree of speed and accuracy and at lower cost than either Sanger sequencing or whole exome sequencing. Furthermore, LipidSeq will help to provide a more focused picture of monogenic and polygenic contributors that underlie dyslipidemia while excluding the discovery of incidental pathogenic clinically actionable variants in nonmetabolism-related genes, such as oncogenes, that would otherwise be identified by a whole exome approach, thus minimizing potential ethical issues.     

Leveraging related health phenotypes for polygenic prediction of impulsive choice,impulsive action,and impulsive personality traits in 1534 European ancestry community adults

Impulsivity refers to a number of conceptually related phenotypes reflecting self-regulatory capacity that are considered promising endophenotypes for mental and physical health. Measures of impulsivity can be broadly grouped into three domains, namely, impulsive choice, impulsive action, and impulsive personality traits. In a community-based sample of ancestral Europeans (n = 1534), we conducted genome-wide association studies (GWASs) of impulsive choice (delay discounting), impulsive action (behavioral inhibition), and impulsive personality traits (UPPS-P), and evaluated 11 polygenic risk scores (PRSs) of phenotypes previously linked to self-regulation. Although there were no individual genome-wide significant hits, the neuroticism PRS was positively associated with negative urgency (adjusted R² = 1.61%, p = 3.6 × 10⁻⁷) and the educational attainment PRS was inversely associated with delay discounting (adjusted R² = 1.68%, p = 2.2 × 10⁻⁷). There was also evidence implicating PRSs of attention-deficit/hyperactivity disorder, externalizing, risk-taking, smoking cessation, smoking initiation, and body mass index with one or more impulsivity phenotypes (adjusted R²s: 0.35%–1.07%; FDR adjusted ps = 0.05–0.0006). These significant associations between PRSs and impulsivity phenotypes are consistent with established genetic correlations. The combined PRS explained 0.91%–2.46% of the phenotypic variance for individual impulsivity measures, corresponding to 8.7%–32.5% of their reported single-nucleotide polymorphism (SNP)-based heritability, suggesting a non-negligible portion of the SNP-based heritability can be recovered by PRSs. These results support the predictive validity and utility of PRSs, even derived from related phenotypes, to inform the genetics of impulsivity phenotypes.     

Association between polygenic liability for schizophrenia and substance involvement: A nationwide population‐based study in Taiwan

Schizophrenia and substance involvement frequently co‐occur in individuals, and a bidirectional relationship between the two has been proposed; shared underlying genetic factors could be an alternative explanation. This study investigated the genetic overlap between schizophrenia and substance involvement, including tobacco, alcohol and betel nut use. The study subjects were recruited from the Taiwan Biobank, and genome‐wide genotyping data was available for 18 327 participants without schizophrenia. We calculated the Psychiatric Genomics Consortium‐derived polygenic risk score (PRS) for schizophrenia in each participant. The significance of the schizophrenia PRS associated with substance involvement was evaluated using a regression model with adjustments for gender, age and population stratification components. The modified effect of gender or birth decade was also explored. The schizophrenia PRS was positively associated with lifetime tobacco smoking in women (OR in per SD increase in PRS = 1.12 with 95% CI 1.04‐1.20, P = .002), but not in men (OR = 0.99 with 95% CI 0.95‐1.04, P = .74), and the gender‐PRS interaction reached significance (P = .006). The OR between PRS and lifetime tobacco smoking increased with the birth decade (P of birth decade‐PRS interaction = .0002). In women, OR increased from 0.97 (P = .85) for subjects with a birth decade before 1950 to 1.21 (P = .04) for subjects with a birth decade after 1980; in men, the corresponding OR increased from 0.88 (P = .04) to 1.13 (P = .11). There was no association between schizophrenia PRS and alcohol/betel nut use phenotypes. This study provides evidence for the genetic overlap between schizophrenia and tobacco use in women, and this overlap was stronger in the younger population.     

Fast and accurate Bayesian polygenic risk modeling with variational inference

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Genetic trade‐offs between complex diseases and longevity

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle‐aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long‐lived individuals. We further stratified PRSs into cell‐type groups and significance‐level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p‐values (p ≤ 1x10^‐5) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10^‐5 < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade‐offs between complex diseases and longevity.