Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example

Generalized genetic liability to substance use disorders

Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

Genetic and non-genetic predictors of risk for opioid dependence

BACKGROUND

Elucidation of the interaction of biological and psychosocial/environmental factors on opioid dependence (OD) risk can inform our understanding of the etiology of OD. We examined the role of psychosocial/environmental factors in moderating polygenic risk for opioid use disorder (OUD).

METHODS

Data from 1958 European ancestry adults who participated in the Yale-Penn 3 study were analyzed. Polygenic risk scores (PRS) were based on a large-scale multi-trait analysis of genome-wide association studies (MTAG) of OUD.

RESULTS

A total of 420 (21.1%) individuals had a lifetime diagnosis of OD. OUD PRS were positively associated with OD (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.21-1.66). Household income and education were the strongest correlates of OD. Among individuals with higher OUD PRS, those with higher education level had lower odds of OD (OR 0.92, 95% CI 0.85-0.98); and those with posttraumatic stress disorder (PTSD) were more likely to have OD relative to those without PTSD (OR 1.56, 95% CI 1.04-2.35).

CONCLUSIONS

Results suggest an interplay between genetics and psychosocial environment in contributing to OD risk. While PRS alone do not yet have useful clinical predictive utility, psychosocial factors may help enhance prediction. These findings could inform more targeted clinical and policy interventions to help address this public health crisis.

Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans

Importance

Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated.

Objective

To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk.

Design

This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022.

Setting

Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States.

Participants

Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis.

Exposures

Number of risk alleles present across 15 candidate genetic variants.

Main Outcome and Measures

Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models.

Results

Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample.

Conclusions and Relevance

Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

The Potential and Challenges of Genomics Informed Precision Care for Substance Use Disorders

Substance use disorders (SUDs) are complex brain disorders with heritability rooted in the interplay of multiple genetic factors, alongside significant environmental influences. Gaining insights into the genetic mechanisms that heighten SUD risk can guide precision care, specifically in the development of targeted tools for prevention, early intervention, and the discovery of therapeutic targets. Nurses are ideally placed to advance genomics-informed precision care for individuals with SUDs. To fulfill this role, they must be adequately prepared to assess the value and utility of current genomics knowledge, its limitations, and ways to incorporate this understanding into clinical practice, education, research, and health care policy. [Journal of Psychosocial Nursing and Mental Health Services, 62(3), 11-14.].

Invited Expert Opinion- Bioinformatic and Limitation Directives to Help Adopt Genetic Addiction Risk Screening and Identify Preaddictive Reward Dysregulation: Required Analytic Evidence to Induce Dopamine Homeostatsis

Addiction, albeit some disbelievers like Mark Lewis [1], is a chronic, relapsing brain disease, resulting in unwanted loss of control over both substance and non- substance behavioral addictions leading to serious adverse consequences [2]. Addiction scientists and clinicians face an incredible challenge in combatting the current opioid and alcohol use disorder (AUD) pandemic throughout the world. Provisional data from the Centers for Disease Control and Prevention (CDC) shows that from July 2021-2022, over 100,000 individuals living in the United States (US) died from a drug overdose, and 77,237 of those deaths were related to opioid use [3]. This number is expected to rise, and according to the US Surgeon General it is highly conceivable that by 2025 approximately 165,000 Americans will die from an opioid overdose. Alcohol abuse, according to data from the World Health Organization (WHO), results in 3 million deaths worldwide every year, which represents 5.3% of all deaths globally [4].

Genetic Addiction Risk Severity Assessment Identifies Polymorphic Reward Genes as Antecedents to Reward Deficiency Syndrome (RDS) Hypodopaminergia's Effect on Addictive and Non-Addictive Behaviors in a Nuclear Family

This case series presents the novel genetic addiction risk score (GARS), which shows a high prevalence of polymorphic risk alleles of reward genes in a nuclear family with multiple reward deficiency syndrome (RDS) behavioral issues expressing a hypodopaminergic antecedent. The family consists of a mother, father, son, and daughter. The mother experienced issues with focus, memory, anger, and amotivational syndrome. The father experienced weight issues and depression. The son experienced heavy drinking, along with some drug abuse and anxiety. The daughter experienced depression, lethargy, brain fog, focus issues, and anxiety, among others. A major clinical outcome of the results presented to the family members helped reduce personal guilt and augment potential hope for future healing. Our laboratory's prior research established that carriers of four or more alleles measured by GARS (DRD1-DRD4, DAT1, MOR, GABABR3, COMT, MAOAA, and 5HTLPR) are predictive of the addiction severity index (ASI) for drug abuse, and carriers of seven or more alleles are predictive of severe alcoholism. This generational case series shows the impact that genetic information has on reducing stigma and guilt in a nuclear family struggling with RDS behaviors. The futuristic plan is to introduce an appropriate DNA-guided "pro-dopamine regulator" into the recovery and enhancement of life.

Statistical Validation of Risk Alleles in Genetic Addiction Risk Severity (GARS) Test: Early Identification of Risk for Alcohol Use Disorder (AUD) in 74,566 Case–Control Subjects

Since 1990, when our laboratory published the association of the DRD2 Taq A1 allele and severe alcoholism in JAMA, there has been an explosion of genetic candidate association studies, including GWAS. To develop an accurate test to help identify those at risk for at least Alcohol Use Disorder (AUD), Blum’s group developed the Genetic Addiction Risk Severity (GARS) test, consisting of ten genes and eleven associated risk alleles. In order to statistically validate the selection of these risk alleles measured by GARS, we applied strict analysis to studies that investigated the association of each polymorphism with AUD or AUD-related conditions published from 1990 until 2021. This analysis calculated the Hardy–Weinberg Equilibrium of each polymorphism in cases and controls. If available, the Pearson’s χ2 test or Fisher’s exact test was applied to comparisons of the gender, genotype, and allele distribution. The statistical analyses found the OR, 95% CI for OR, and a post-risk for 8% estimation of the population’s alcoholism prevalence revealed a significant detection. The OR results showed significance for DRD2, DRD3, DRD4, DAT1, COMT, OPRM1, and 5HTT at 5%. While most of the research related to GARS is derived from our laboratory, we are encouraging more independent research to confirm our findings.