The association of polygenic risk for schizophrenia, bipolar disorder, and depression with neural connectivity in adolescents and young adults: examining developmental and sex differences

AbstractNeurodevelopmental abnormalities in neural connectivity have been long implicated in the etiology of schizophrenia (SCZ); however, it remains unclear whether these neural connectivity patterns are associated with genetic risk for SCZ in unaffected individuals (i.e., an absence of clinical features of SCZ or a family history of SCZ). We examine whether polygenic risk scores (PRS) for SCZ are associated with functional neural connectivity in adolescents and young adults without SCZ, whether this association is moderated by sex and age, and if similar associations are observed for genetically related neuropsychiatric PRS. One-thousand four-hundred twenty-six offspring from 913 families, unaffected with SCZ, were drawn from the Collaborative Study of the Genetics of Alcoholism (COGA) prospective cohort (median age at first interview = 15.6 (12–26), 51.6% female, 98.1% European American, 41% with a family history of alcohol dependence). Participants were followed longitudinally with resting-state EEG connectivity (i.e., coherence) assessed every two years. Higher SCZ PRS were associated with elevated theta (3–7 Hz) and alpha (7–12 Hz) EEG coherence. Associations differed by sex and age; the most robust associations were observed between PRS and parietal-occipital, central-parietal, and frontal-parietal alpha coherence among males between ages 15–19 (B: 0.15–0.21, p < 10–4). Significant associations among EEG coherence and Bipolar and Depression PRS were observed, but differed from SCZ PRS in terms of sex, age, and topography. Findings reveal that polygenic risk for SCZ is robustly associated with increased functional neural connectivity among young adults without a SCZ diagnosis. Striking differences were observed between men and women throughout development, mapping onto key periods of risk for the onset of psychotic illness and underlining the critical importance of examining sex differences in associations with neuropsychiatric PRS across development.

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Incorporating family history of disease improves polygenic risk scores in diverse populations

10.1101/2021.04.15.439975 ◽

2021 ◽

Author(s):

Margaux L.A. Hujoel ◽

Po-Ru Loh ◽

Benjamin M. Neale ◽

Alkes L. Price

Keyword(s):

Family History ◽

Threshold Model ◽

South Asians ◽

Target Population ◽

Risk Scores ◽

Diverse Populations ◽

Polygenic Risk ◽

History Of Disease ◽

History Of ◽

Large Improvement

AbstractPolygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

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Delineating genetic and familial risk for psychopathology in the ABCD study

10.1101/2020.09.08.20186908 ◽

2020 ◽

Author(s):

Clare E Palmer ◽

Robert John Loughnan ◽

Carolina Makowski ◽

Wesley Thompson ◽

Deanna Barch ◽

...

Keyword(s):

Family History ◽

Psychiatric Disorders ◽

Genetic Risk ◽

Familial Risk ◽

Risk Scores ◽

Typically Developing ◽

Polygenic Risk ◽

Hyperactivity Disorder ◽

History Of ◽

Risk Predictors

Psychiatric disorders place a huge burden on those affected and their families, as well as society. Nearly all psychiatric disorders have a heritable component and lifetime prevalence rates of several disorders are higher among first degree biological relatives of individuals with a diagnosis. Given that many psychiatric disorders have their onset in adolescence, estimating genetic risk during childhood may identify at-risk individuals for early intervention that can reduce this burden. Here we measured genetic risk for psychopathology using both polygenic risk scores (PRS) and family history in a large typically developing sample of 9-10 year old children from the Adolescent Brain and Cognitive Development (ABCD) StudySM and determined associations with a large battery of behavioural phenotypes. By including all genetic risk predictors in the same model, we were able to delineate unique behavioral associations across these measures. Polygenic risk for Attention Deficit Hyperactivity Disorder (ADHD) and depression (DEP) was associated with unique patterns of both externalizing and internalizing behaviors. Family history of conduct problems, depression and anxiety/stress additionally predicted unique behavioral variance across similar measures. These findings provide important insight into the potential predictive utility of PRS and family history in early adolescence and suggest that they may be signaling differential, additive information that could be useful for quantifying risk during development.

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Towards clinical utility of polygenic risk scores

Human Molecular Genetics ◽

10.1093/hmg/ddz187 ◽

2019 ◽

Vol 28 (R2) ◽

pp. R133-R142 ◽

Cited By ~ 56

Author(s):

Samuel A Lambert ◽

Gad Abraham ◽

Michael Inouye

Keyword(s):

Risk Factors ◽

Family History ◽

Risk Prediction ◽

Clinical Utility ◽

Disease Risk ◽

Risk Scores ◽

Polygenic Risk ◽

Common Diseases ◽

History Of Disease ◽

History Of

Abstract Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

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