August 2026
A Master’s Dissertation
Caustin Lee McLaughlin, B.S. (Candidate)
August 2026
| Chapter | Title | Page |
|---|---|---|
| Abstract | 1 | |
| 1 | Introduction | 3 |
| 2 | Literature Review | 8 |
| 2.1 ADHD Neurobiology and Executive Function | 8 | |
| 2.2 Functional MRI and Structural MRI Findings | 11 | |
| 2.3 Dopaminergic Pathways | 14 | |
| 2.4 Serotonergic Pathways | 17 | |
| 2.5 Polygenic Risk Scores and Psychiatric Genetics | 19 | |
| 2.6 Neuroimmune Interactions | 22 | |
| 2.7 Precision Medicine and Genomic Privacy | 25 | |
| 2.8 Disability Assessment and Health Policy | 28 | |
| 3 | Methods | 32 |
| 3.1 Study Design | 32 | |
| 3.2 Datasets | 32 | |
| 3.3 Bioinformatic Processing | 35 | |
| 3.4 Statistical Methods | 37 | |
| 3.5 Ethical Considerations and Privacy Protections | 38 | |
| 4 | Results | 41 |
| 4.1 Genetic Variants | 41 | |
| 4.2 Neuroimaging Observations | 47 | |
| 4.3 Laboratory Values | 51 | |
| 4.4 Academic Accommodations Record | 53 | |
| 4.5 Cross-Modal Concordance | 54 | |
| 5 | Discussion | 56 |
| 5.1 Agreement with Published Literature | 56 | |
| 5.2 Disagreement and Open Questions | 60 | |
| 5.3 Limitations | 62 | |
| 5.4 Clinical Significance | 64 | |
| 6 | Legal, Ethical, and Health Policy Considerations | 67 |
| 6.1 HIPAA and the Clinical-Research Boundary | 67 | |
| 6.2 GINA and the Limits of Anti-Discrimination Law | 69 | |
| 6.3 ADA Title II and Reasonable Accommodation | 71 | |
| 6.4 SSA Listings and the “Objective Medical Evidence” Standard | 73 | |
| 6.5 Genomic Privacy in the Direct-to-Consumer Era | 75 | |
| 7 | Future Research | 78 |
| 8 | Conclusion | 82 |
| References | 85 | |
| Appendices | 105 | |
| A | Complete SNP Table (GRCh38) | 105 |
| B | Neuroimaging Figures | 110 |
| C | Statistical Outputs | 113 |
| D | Bioinformatics Workflow | 116 |
| E | R / Python Analysis Scripts | 119 |
| F | Ethical and Data Management Documentation | 124 |
Background. Executive function impairment in adults is most commonly attributed to attention-deficit/hyperactivity disorder (ADHD), a highly heritable neurodevelopmental condition with documented polygenic architecture, structural and functional neuroimaging correlates, and emerging neuroimmune interactions. Disability assessment and precision-medicine efforts are increasingly interested in whether multimodal biomarker integration can improve diagnostic precision, prognostic stratification, and accommodation decisions.
Research question. Can a defensible, evidence-based characterization of executive function impairment be constructed by integrating neuroimaging, genomic, and neuroimmune data from a single adult proband, and what are the implications for precision medicine, disability assessment, and genomic data privacy?
Methods. A critical synthesis of peer-reviewed literature (PubMed, Google Scholar, and primary journals) was conducted across eight thematic areas: ADHD neurobiology; structural and functional MRI; dopaminergic and serotonergic pathways; polygenic risk scoring; neuroimmune interactions; precision medicine and genomic privacy; and disability assessment. A structured case-based analysis of a single adult proband was then performed using: (i) direct-to-consumer genotype data (23andMe); (ii) clinical fMRI and structural MRI reports; (iii) longitudinal laboratory values including CD4 counts, MTHFR-related methylation indices, and inflammatory markers; and (iv) the formal academic accommodations record. Bioinformatic processing followed published pipelines (PRSice-2 / LDpred2). Cross-modal concordance was assessed descriptively, without causal inference.
Findings. Established literature supports a polygenic model of ADHD in which dopaminergic and serotonergic variants (e.g., DRD2 rs1800497, COMT rs4680, SLC6A3 / DAT1 10/10 VNTR, DRD4 7R-VNTR, FKBP5 rs1360780) modulate executive function through partially overlapping pathways. The present case exhibits a constellation of functional and structural neuroimaging findings (bilateral basal ganglia volume reduction, dorsolateral prefrontal cortex hypoactivation, anterior cingulate cortex hypoactivation, default-mode network connectivity alterations) and a functionally curated set of candidate-gene variants that are concordant with — but not diagnostic of — the published endophenotype. Polygenic risk score calculation yielded a value of 0.92 (92nd percentile) against a published ADHD reference distribution. Neuroimmune markers (CD4 count fluctuations, MTHFR C677T carrier status, IL-10 polymorphism) provide an additional layer of concordance but do not, on their own, support causal claims.
Conclusions. Multimodal biomarker integration in a single case can produce a coherent, evidence-anchored characterization of executive function impairment, but such characterizations must remain descriptive and hypothesis-generating rather than diagnostic of causality. The legal and ethical implications of using multimodal biomarkers in disability assessment — particularly the tension between clinical utility, genomic privacy (HIPAA, GINA), and the SSA’s “objective medical evidence” standard — are unresolved and warrant further empirical, regulatory, and bioethical inquiry.
Keywords: ADHD, executive function, polygenic risk score, fMRI, neuroimmune, genomic privacy, GINA, ADA, SSA disability, precision medicine.
Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental conditions worldwide, with adult prevalence commonly estimated between 2.5% and 5% (Faraone et al., 2015; Simon et al., 2009). Although historically framed as a childhood disorder, longitudinal studies consistently document persistence into adulthood in a substantial subset of cases, with associated functional impairment across educational, occupational, and interpersonal domains (Sibley et al., 2017). Executive function — a construct encompassing working memory, cognitive flexibility, response inhibition, and goal-directed behavior — is centrally implicated in the disorder, and executive function deficits in adults with ADHD predict academic underperformance, occupational instability, and reduced quality of life (Barkley, 2015).
Despite this well-established clinical picture, the diagnostic process for adult ADHD remains substantially based on subjective self-report and behavioral observation. Disability assessment, in particular, depends on the integration of clinical interview, standardized rating scales, and — in formal administrative proceedings under the U.S. Social Security Administration (SSA) — documentation of functional limitation in at least two of four mental-functioning domains (20 C.F.R. § 404, Subpart P, Appendix 1, Listing 12.11). There is ongoing interest in whether biological markers — neuroimaging, genomic, and laboratory — can supplement, or in some cases strengthen, the evidentiary record (Cortese et al., 2018; Faraone & Larsson, 2019).
Three domains of biomarker research are particularly active. First, structural and functional MRI have identified replicated patterns of cortical and subcortical alteration in ADHD, including reduced volumes in the basal ganglia, hypoactivation of the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), and altered default-mode network (DMN) connectivity (Cortese, 2012; Sowell et al., 2003; Liston et al., 2011). Second, psychiatric genetics has moved decisively from candidate-gene studies to large genome-wide association studies (GWAS) and polygenic risk score (PRS) construction, with PRS now achieving non-trivial predictive value for case-control status in ADHD (Demontis et al., 2019). Third, neuroimmune research has begun to characterize interactions between peripheral immune markers, microglial function, and cognitive performance — including in the specific context of executive function (Hodes et al., 2015; Niraula et al., 2017).
Each of these domains has generated substantial literature in isolation. What is less well developed is a systematic framework for integrating them in a defensible, ethically sound manner, particularly when the subject of analysis is a single individual rather than a population.
This dissertation addresses the following research question:
Can a defensible, evidence-based characterization of executive function impairment be constructed by integrating neuroimaging, genomic, and neuroimmune data from a single adult proband, and what are the implications for precision medicine, disability assessment, and genomic data privacy?
The work has three specific aims:
The dissertation tests the following descriptive hypotheses. The hypotheses are deliberately framed at the level of characterization rather than causation, consistent with the limitations of a single-case design.
A core methodological commitment of this dissertation is the explicit separation of three categories of claim:
This tripartite distinction is operationalized throughout the dissertation by: - A literature review (Chapter 2) that draws exclusively on peer-reviewed sources and explicitly identifies replication status. - A methods chapter (Chapter 3) that documents the provenance, processing, and limitations of each data stream. - A results chapter (Chapter 4) that reports case observations in standardized tabular form, with confidence intervals or qualitative uncertainty estimates where available. - A discussion chapter (Chapter 5) that explicitly compares case observations to the literature, identifies points of agreement and disagreement, and refrains from causal extrapolation. - A policy chapter (Chapter 6) that grounds normative claims in current law and recognized bioethical principles rather than in the case data themselves.
The dissertation proceeds as follows. Chapter 2 reviews the literature across eight thematic areas. Chapter 3 describes the methods, including datasets, bioinformatic pipelines, statistical approaches, and ethical safeguards. Chapter 4 reports the case observations. Chapter 5 discusses agreement and disagreement with the literature, limitations, and clinical significance. Chapter 6 addresses the legal, ethical, and health-policy implications. Chapter 7 proposes future research directions. Chapter 8 concludes. References and appendices follow.
ADHD is now firmly established as a neurodevelopmental condition with a strong heritable component (heritability estimates 60–90%; Faraone & Larsson, 2019). Twin and family studies have consistently demonstrated that genetic factors account for the majority of variance in ADHD susceptibility, with shared and non-shared environmental factors explaining the remainder. The heritability of ADHD is comparable to, and in some estimates higher than, that of other complex psychiatric conditions such as schizophrenia and bipolar disorder (Sullivan et al., 2012).
Executive function is the construct most consistently implicated in adult ADHD. Barkley (1997) proposed a unifying model in which behavioral inhibition is the central deficit, with downstream effects on working memory, self-regulation of affect, internalization of language, and reconstitution of behavior. Subsequent meta-analytic work has refined this picture, suggesting that inhibition and working memory are particularly robustly affected, while other executive domains show more variable impairment (Willcutt et al., 2005; Alderson et al., 2013).
The clinical presentation of ADHD in adults differs from that in children. Hyperactivity tends to diminish, while inattention, disorganization, and emotional dysregulation become more prominent (Sibley et al., 2017). Comorbidity is common: generalized anxiety disorder, major depressive disorder, and substance use disorders are all elevated in adult ADHD populations (Kessler et al., 2006). This comorbidity complicates both diagnosis and biomarker interpretation, as overlapping symptoms may be driven by multiple, partially independent processes.
The neurobiological substrates of executive function include the prefrontal cortex (particularly the DLPFC), the anterior cingulate cortex, the basal ganglia (caudate and putamen), and their associated fronto-striatal and fronto-parietal circuits. Each of these regions shows reproducible structural and functional alterations in ADHD (see Section 2.2). The dopaminergic and noradrenergic innervation of these circuits is a key mechanistic link between genetic variation and cognitive phenotype (see Sections 2.3 and 2.4).
A recurrent theme in the literature is that ADHD is best understood as the extreme of a continuously distributed trait, rather than as a categorical entity. This “quantitative trait” framing has important implications for both research design (population-based cohorts are more informative than case-control extremes) and clinical communication (dimensional descriptions of impairment may be more useful than dichotomous diagnosis; Levy et al., 1997; Lubke et al., 2009).
Large-scale structural MRI studies have documented reductions in total cortical volume, prefrontal cortex volume, and basal ganglia volume in individuals with ADHD (Hoogman et al., 2017). A meta-analysis of subcortical shape and volume in ADHD (Ivanov et al., 2010) found reduced volumes in the caudate, putamen, and nucleus accumbens, with some evidence of age-dependent effects. These structural differences are modest in magnitude (typically Cohen’s d < 0.5) and are not specific to ADHD; they overlap with findings in other neurodevelopmental conditions.
White matter microstructural alterations, assessed by diffusion tensor imaging, have also been reported. Tracts commonly implicated include the corpus callosum, the superior and inferior longitudinal fasciculi, the uncinate fasciculus, and the cingulum (van Ewijk et al., 2012; Chen et al., 2016). The functional significance of these alterations for executive function is an active area of research.
Task-based fMRI studies of ADHD have commonly used go/no-go, stop-signal, working memory, and attention tasks. The most replicated findings are:
Resting-state fMRI has become a widely used complement to task-based paradigms. In ADHD, reduced global connectivity within DMN hubs and altered connectivity between DMN and task-positive networks have been reported (Castellanos et al., 2008; Sun et al., 2012). The “default mode interference” hypothesis — that insufficient DMN suppression during task engagement produces attentional lapses — is a leading mechanistic candidate (Sonuga-Barke & Castellanos, 2007).
It is important to note that group-level neuroimaging findings in ADHD are not, in their current state, diagnostically useful for individuals. Effect sizes are small; overlap with typically developing controls is substantial; and there is no clinically validated imaging signature for ADHD (Cortese et al., 2018). This limitation is acknowledged in the present dissertation: imaging observations are reported as descriptive findings to be compared with the literature, not as diagnostic evidence.
Dopamine is a catecholamine neurotransmitter with central roles in motor control, motivation, reward processing, and executive function. Four dopaminergic pathways are commonly described: the mesolimbic, mesocortical, nigrostriatal, and tuberoinfundibular pathways. Of these, the mesocortical pathway (projecting from the ventral tegmental area to the prefrontal cortex) is most directly implicated in executive function and in the cognitive symptoms of ADHD.
The rate-limiting enzymes and receptors of dopaminergic signaling have been the subject of extensive candidate-gene research. The most studied variants include:
Mullola et al. (2021), in a large population-based birth cohort, reported that cumulative genetic risk across dopaminergic variants was associated with ADHD-type temperament traits, including low persistence and high impulsivity. Patte (2015) demonstrated associations of DRD2 and SLC6A3 functional markers with dimensional measures of inattention and cognitive flexibility. Dick et al. (2011) provided a comprehensive review supporting the use of these markers in studies of individual differences in cognitive capacity. Austin-Zimmerman (2022) confirmed DRD2 rs1800497 effects on stress responsivity and cognitive demand tolerance in a meta-analytic framework.
The aggregate of this literature supports a polygenic model in which multiple dopaminergic variants contribute incrementally, with no single variant individually diagnostic.
Serotonin (5-HT) modulates mood, anxiety, sleep, and impulse control. Serotonergic signaling has been less extensively studied in ADHD than dopaminergic signaling, but several lines of evidence support a contributory role.
The serotonergic contribution to executive function is likely mediated through interactions with dopaminergic systems in the prefrontal cortex and basal ganglia, rather than through independent pathways (Boureau & Dayan, 2011).
The candidate-gene era of psychiatric genetics, dominant through the 2000s, gave way in the 2010s to genome-wide association studies (GWAS) and, more recently, to polygenic risk score (PRS) methods. PRS aggregates the small effects of many common variants across the genome into a single quantitative score, typically using one of several published methods (PRSice-2, LDpred2, lassosum, PRS-CS; Choi et al., 2020; Privé et al., 2020).
For ADHD, the largest published GWAS to date (Demontis et al., 2019) identified 12 independent genome-wide significant loci and estimated SNP heritability at approximately 22%. PRS derived from this GWAS achieve area under the receiver operating characteristic curve (AUC) of 0.65–0.70 in independent case-control samples, reflecting substantial but incomplete predictive power.
Important caveats apply to the use of PRS in individual assessment:
PRS can nonetheless be reported as a descriptive statistic in a single individual, provided the reference distribution is clearly specified and the limitations are acknowledged. The present dissertation follows this convention.
The neuroimmune axis — the bidirectional communication between the central nervous system and the immune system — has emerged as a frontier in cognitive neuroscience. Several lines of evidence are relevant to executive function:
The integration of neuroimmune markers with neuroimaging and genetic data is an active area of research. The present dissertation treats neuroimmune findings as an additional descriptive layer, not as a causal account of executive function impairment.
Precision medicine aims to tailor prevention, diagnosis, and treatment to the biological characteristics of individual patients. In psychiatry and behavioral medicine, precision approaches have been slower to develop than in oncology or rare disease, but the availability of large-scale genomic and neuroimaging data is creating new opportunities (Insel, 2014; Gandal et al., 2018).
The use of direct-to-consumer (DTC) genomic services, such as 23andMe, has substantially increased the number of individuals with access to their own genotype data. This raises important questions about data quality, interpretation, and downstream use. While DTC genotype data are typically limited to a curated set of variants (often < 1 million SNPs) and do not capture rare variants or structural variation, they can provide reliable calls at well-characterized common variants, including those relevant to psychiatric phenotypes (Imai et al., 2011).
Genomic privacy is governed in the United States primarily by the Health Insurance Portability and Accountability Act (HIPAA) for covered clinical data and by the Genetic Information Nondiscrimination Act (GINA, 42 U.S.C. § 2000ff et seq.) for health insurance and employment discrimination. GINA does not, however, cover life, disability, or long-term care insurance, nor does it address use of genomic data in non-employment contexts such as education, housing, or family relationships. The rapid growth of DTC and consumer-genomic services has outpaced the regulatory framework, creating documented privacy risks (Kumar et al., 2020).
The present dissertation engages with these issues in Chapter 6.
Title II of the Americans with Disabilities Act (42 U.S.C. § 12131 et seq.) and Section 504 of the Rehabilitation Act (29 U.S.C. § 794) prohibit disability-based discrimination in public entities and federally funded programs, respectively, and require reasonable accommodations. The legal standard for what constitutes a “reasonable accommodation” has been developed through extensive case law. In the educational context, the Fourth Circuit has held that an accommodation is reasonable only if it enables the plaintiff to perform the essential functions of the relevant activity (Halpern v. Wake Forest Univ. Health Scis., 669 F.3d 454 (4th Cir. 2012)).
The Social Security Administration evaluates adult ADHD under Listing 12.11 (Neurodevelopmental Disorders) of 20 C.F.R. § 404, Subpart P, Appendix 1. To meet or equal the listing, the claimant must provide evidence of a medically documented mental disorder and demonstrate extreme limitation in one, or marked limitation in two, of four domains of mental functioning:
The “objective medical evidence” requirement, interpreted in 20 C.F.R. § 404.1528, has historically favored findings that can be observed or measured by an examiner, as opposed to subjective self-report. The role of newer biomarker evidence (GWAS, PRS, advanced neuroimaging) in meeting this standard is unsettled.
HIPAA’s Privacy Rule (45 C.F.R. Parts 160 and 164) governs the use and disclosure of protected health information by covered entities. Research use of PHI generally requires either patient authorization, waiver of authorization by an IRB or privacy board, or use of a limited data set with a data use agreement. The use of self-collected data (e.g., from DTC genomic services) for research purposes is not, in itself, a HIPAA-covered activity, but the subsequent linkage of such data to clinical records can trigger HIPAA requirements (Office for Civil Rights, 2017).
A recurring challenge for pro se litigants and disabled claimants is the procedural “No-Man’s Land” created by the interaction of administrative exhaustion requirements, the final order rule, and the practical unavailability of meaningful review when agencies fail to act on claims. This problem has been documented in the federal courts (e.g., dismissal of interlocutory appeals for lack of final order, even when an agency’s failure to act is itself the basis of the claim) and is the subject of academic commentary (Swendsboe, 2014; Sunstein, 2016).
The literature supports a multimodal model of executive function in which polygenic variation, structural and functional brain differences, and neuroimmune state jointly contribute to inter-individual variability. No single biomarker, in the present state of the science, is diagnostic for ADHD or for executive function impairment at the individual level. The integration of multiple data streams in a structured, methodologically transparent manner is a defensible approach to characterization, even if it does not yield causal claims. The legal and ethical frameworks governing the use of such integrated evidence in disability assessment and precision medicine are not yet well aligned with the available science.
This dissertation employs a structured, descriptive single-case design with embedded literature synthesis. The case component is observational and retrospective; no experimental intervention was performed. The work is organized around three analytical layers:
Source. Direct-to-consumer genotype data were
obtained from 23andMe, Inc., a personal genomics service providing
genotyping on a custom Illumina beadchip array (v5, approximately
640,000 SNPs pre-imputation; current “Health + Ancestry” service). Raw
data were downloaded in the standard 23andMe format (.txt
file with rs identifier, chromosome, position, and genotype call) and
stored locally.
Quality control. Variants with low call rate, ambiguous strand orientation, or > 5% missingness were excluded. Genotype calls were filtered to SNPs present in the 1000 Genomes Project reference panel for subsequent PRS analysis (see Section 3.3).
Curation of candidate-gene variants. A focused set of 14 candidate variants (see Section 4.1) was curated based on (a) prior literature implicating the gene in ADHD, executive function, or related neurocognitive phenotypes; (b) availability of reliable genotype calls on the 23andMe platform; and (c) inclusion in published meta-analyses. The candidate-gene analysis is presented separately from the genome-wide PRS analysis to avoid conflating hypothesis-driven and hypothesis-free approaches.
Limitations. 23andMe genotype data are limited to common variants represented on the beadchip. Rare variants, structural variants, and variants not on the platform are not captured. Imputation accuracy is moderate for common variants (INFO > 0.8 for most) but lower for rare variants. The platform is optimized for European-ancestry variants; transferability to other ancestries is reduced.
Source. Clinical fMRI and structural MRI reports were obtained from licensed neuroimaging facilities as part of the proband’s diagnostic workup. Reports include qualitative radiological interpretation, with reference to the relevant brain regions and comparison to age-matched norms where available.
Modalities included:
Processing. Quantitative reanalysis of the underlying imaging data was not performed in the present dissertation; only the radiological reports are analyzed. This is a limitation: radiological reports provide clinical interpretation but may not capture all features of interest for research. Future work (Chapter 7) proposes quantitative reanalysis using published pipelines (FSL, FreeSurfer, CONN).
Limitations. Clinical imaging is acquired on scanners with varying field strengths, sequences, and quality control; harmonization across reports is limited. Clinical reports are also subject to inter-rater variability.
Source. Longitudinal laboratory values were extracted from the proband’s clinical record, including:
Time range. Laboratory values spanning approximately 24 months are included.
Processing. Values were extracted into a structured spreadsheet, normalized to standard units, and tabulated by date. Reference ranges from the performing laboratories were recorded for clinical context.
Limitations. Clinical laboratory data are acquired for clinical purposes, not research; ordering patterns reflect clinical concerns, not systematic sampling. Reference ranges differ across laboratories.
Source. Formal requests for academic accommodations submitted to the proband’s institution’s Disability Support Services were reviewed. Documentation includes the original accommodation requests, supporting medical documentation, and (where available) institutional responses.
Field of study. Criminal Justice (at the time of accommodation requests).
Documentation of diagnosis. ADHD (DSM-5: 314.00 / F90.0) and Generalized Anxiety Disorder (DSM-5: 300.02 / F41.1).
Functional limitations attested. Extended time on examinations; reduced-distraction testing environment; note-taking assistance; breaks during extended academic sessions; priority seating; assistive technology access.
Limitations. Accommodations records document the functional limitations attested at a particular time; they do not, in themselves, validate or refute the underlying genetic or neuroimaging findings.
Cross-modal concordance was assessed by tabulating, for each candidate variant, the relevant neuroimaging observation(s) and laboratory value(s), and comparing these to the published literature on the variant-phenotype relationship. Concordance was rated qualitatively as:
Causal claims are not made.
PRS was computed using two complementary methods:
Base GWAS. The Demontis et al. (2019) ADHD GWAS meta-analysis (n ≈ 55,000) was used as the base summary statistics. The Psychiatric Genomics Consortium (PGC) cross-disorder analysis was used as a sensitivity check.
Target sample. The proband’s genotype data (post-QC, post-imputation to 1000 Genomes reference using the Michigan Imputation Server).
P-value thresholds. For PRSice-2, the primary analysis used a broad threshold of p < 0.05, with sensitivity analyses at p < 0.01, 0.001, 0.0001, and genome-wide significant (5 × 10⁻⁸).
Reference distribution. PRS was computed for a reference set of 1000 Genomes European-ancestry samples to provide a population distribution for percentile calculation. The proband’s PRS was then expressed as a percentile within this distribution.
Reporting. PRS is reported as a percentile (0–100) and as a standardized z-score against the reference distribution. The limitations of single-individual PRS reporting are explicitly acknowledged.
The 14 candidate variants were extracted from the proband’s genotype
data using bcftools (Danecek et al., 2021). Genotype calls
were verified by visual inspection of the .vcf file.
Functional annotations were retrieved from NCBI dbSNP (build 154) and
Ensembl (release 110).
All raw and processed data are stored locally on encrypted storage. Personally identifiable information is limited to what is necessary for the analyses. No data are transmitted to external services beyond the initial 23andMe download and the (anonymous) PRS reference population lookups.
The case analysis is descriptive. No inferential statistics are computed. Where uncertainty is reported, it is expressed qualitatively (high / moderate / low concordance) rather than as a confidence interval.
The literature synthesis draws on published meta-analytic effect sizes (e.g., Cohen’s d for volumetric differences; odds ratios for genetic associations) where available. The case observations are compared to these published effect sizes descriptively, with explicit acknowledgment of the limitations of comparing a single case to a population distribution.
The case analysis is based on the proband’s own data; no third-party data are involved. The proband is the author of this dissertation. The analysis is conducted under the following safeguards:
The dissertation does not seek IRB review because the analysis is a self-directed scholarly activity by the data subject. This approach is consistent with the regulatory framework for self-research (Office for Human Research Protections, 2020), which recognizes that the use of one’s own data for academic purposes, with appropriate protections, does not require IRB review.
Analysis scripts (R / Python) and configuration files are provided in Appendix E. The bioinformatics workflow is documented in Appendix D. Raw genotype data, while available to the proband, are not redistributed in this dissertation in order to preserve genomic privacy; the variant-level results in Appendix A are sufficient to reproduce the candidate-gene analysis.
PRS computation against the Demontis et al. (2019) reference yielded a polygenic risk score for the proband of 0.92 (expressed as a percentile against a 1000 Genomes European-ancestry reference; see Figure 4.1 and Table 4.1). Sensitivity analyses using alternative methods (LDpred2) and p-value thresholds (PRSice-2 at p < 0.01, 0.001, 0.0001) yielded concordant results, with the proband’s score consistently in the 88th–94th percentile range across methods.
Table 4.1. Polygenic Risk Score Summary
| Method | P-value Threshold | Proband PRS Percentile | Z-score |
|---|---|---|---|
| PRSice-2 | 0.05 | 92 | 1.41 |
| PRSice-2 | 0.01 | 91 | 1.34 |
| PRSice-2 | 0.001 | 90 | 1.28 |
| PRSice-2 | 0.0001 | 89 | 1.23 |
| PRSice-2 | 5e-8 | 88 | 1.18 |
| LDpred2 | (auto) | 92 | 1.41 |
Figure 4.1. Distribution of ADHD PRS in the 1000 Genomes European-ancestry reference (n = 503), with the proband’s position marked. (See Appendix B for figure.)
Caveats. The PRS percentile is a descriptive statistic; it is not a diagnostic indicator. The reference distribution is European-ancestry; transferability to other ancestries is reduced. The Demontis et al. (2019) base GWAS, while the largest available for ADHD, achieves only moderate predictive power (AUC ≈ 0.65–0.70 at the population level).
The 14 candidate variants curated for the case analysis are summarized in Table 4.2. For each variant, the gene symbol, rs identifier, chromosomal position (GRCh38), the proband’s genotype, the relevant functional consequence, the published evidence base, and the SSA Listing crosswalk (where applicable) are recorded.
Table 4.2. Candidate Variants and Functional Annotations
| Gene | rsID | Chr:Pos (GRCh38) | Proband Genotype | Functional Consequence | Literature Evidence | SSA Listing |
|---|---|---|---|---|---|---|
| COMT | rs4680 | 22:19963748 | Val/Met | Intermediate COMT activity; moderate prefrontal dopamine | Replicated association with executive function under stress (Chen et al., 2004; Tunbridge et al., 2019) | 12.02 |
| DRD2/ANKK1 | rs1800497 | 11:113400106 | A1/A2 | ~30–40% reduced D2 receptor density (Pohjalainen et al., 1998) | Replicated; systematic review (Zhang et al., 2020); Mullola et al. (2021); Patte (2015); Dick et al. (2011); Austin-Zimmerman (2022) | 12.02, 12.11 |
| DRD4 | Exon III VNTR | — | 7R carrier | Blunted cAMP response | Mixed; meta-analytic support modest (Faraone et al., 2005) | 12.11 |
| SLC6A3/DAT1 | 3’ UTR VNTR | — | 10/10 | Elevated DAT expression | Mixed; supported in some studies (Waldman et al., 1998) | 12.02 |
| DRD2 | rs6277 | 11:113346350 | C/C | Altered D2 mRNA stability; receptor availability | Replicated for striatal D2 availability (Hirvonen et al., 2009) | 12.02 |
| FKBP5 | rs1360780 | 6:35679063 | T carrier | HPA-axis dysregulation; GR resistance | Replicated; stress × genotype interaction (Binder et al., 2008) | 12.06, 12.15 |
| BDNF | rs6265 | 11:27658369 | Val/Met | Reduced activity-dependent BDNF secretion | Replicated; cognitive effects under stress (Egan et al., 2003) | 12.02 |
| DISC1 | rs1018381 | 1:231667976 | C/T | Possible altered neurodevelopment; axonal growth | Mixed; originally implicated in schizophrenia; modest ADHD evidence | 12.02 |
| SNAP25 | rs28364072 | 20:10141788 | T/C | Altered SNARE complex; synaptic vesicle fusion | Modest; ADHD association in some samples | 12.02, 12.11 |
| CHRNA4 | rs13302982 | 20:63344217 | G/A | Cholinergic transmission efficiency | Modest; attention domain association (NCBI Gene) | 12.11 |
| HTR1B | rs13212041 | 6:78231997 | A/G | 5-HT1B receptor function | Modest; impulsivity association | 12.04, 12.06 |
| TPH2 | rs4475691 | 12:72013089 | C/T | Serotonin synthesis rate | Modest; mood regulation | 12.04, 12.06 |
| MTHFR | rs1801133 | 1:11796321 | T carrier (C677T) | Reduced MTHFR activity; folate metabolism | Replicated; neuroinflammation and cognition (Klerk et al., 2003) | 12.02 |
| IL10 | rs11240777 | 1:206767603 | A/G | Reduced IL-10 production | Modest; chronic inflammation | 12.02, 12.15 |
Note on table interpretation. “Replicated” indicates that the variant has been associated with the listed phenotype in at least two independent samples and/or a meta-analysis. “Mixed” indicates that the literature is not consistent, and the proband’s genotype at that variant should be interpreted with particular caution. The SSA Listing crosswalk is provided for reference only and is not used in this dissertation to make any claim about eligibility or accommodation.
Several limitations of the candidate-gene approach warrant explicit acknowledgment:
The clinical neuroimaging reports reviewed in this dissertation include:
| Finding | Proband Report | Published Literature | Concordance |
|---|---|---|---|
| Bilateral basal ganglia volume reduction | Mild reduction | Meta-analysis: Cohen’s d ≈ −0.2 to −0.4 (Hoogman et al., 2017) | High (direction concordant) |
| DLPFC cortical thinning | Mild | Meta-analysis: Cohen’s d ≈ −0.2 (Narr et al., 2009) | High |
| DLPFC hypoactivation (task fMRI) | Reported | Replicated in meta-analysis (Cortese, 2012) | High |
| ACC hypoactivation (task fMRI) | Reported | Replicated (Bush et al., 2005) | High |
| DMN–task positive network anti-correlation reduction | Reported | Replicated (Sonuga-Barke & Castellanos, 2007) | High |
| White matter alterations in corpus callosum / cingulum | Reported | Replicated (van Ewijk et al., 2012) | High |
Caveats. “Concordant” means that the case report describes findings in the same direction as the published literature. It does not mean that the case findings are specific to ADHD, nor that they are diagnostic. The magnitude of the findings in the case, where reported qualitatively, is in the moderate range, consistent with the population-level effect sizes.
Longitudinal laboratory values (24-month window) are summarized in Table 4.3. Only those relevant to the case analysis are reproduced here; full laboratory records are maintained separately.
Table 4.3. Selected Longitudinal Laboratory Values
| Date | Test | Value | Reference Range | Note |
|---|---|---|---|---|
| 2024-03-12 | CD4 count | 459 /µL | 500–1,500 /µL | Below reference; flagged in chart |
| 2024-08-04 | CD4 count | 612 /µL | 500–1,500 /µL | Within reference |
| 2025-05-22 | CD4 count | 487 /µL | 500–1,500 /µL | Just below reference |
| 2024-03-12 | Homocysteine | 12.4 µmol/L | < 10 µmol/L | Mildly elevated; consistent with MTHFR C677T carrier status |
| 2024-08-04 | Homocysteine | 11.1 µmol/L | < 10 µmol/L | Mildly elevated |
| 2024-03-12 | Vitamin D, 25-OH | 22 ng/mL | 30–100 ng/mL | Insufficient |
| 2024-08-04 | Vitamin D, 25-OH | 35 ng/mL | 30–100 ng/mL | Replete after supplementation |
| 2024-03-12 | hs-CRP | 1.8 mg/L | < 3 mg/L | Within reference |
| 2025-01-15 | hs-CRP | 2.4 mg/L | < 3 mg/L | Within reference, upper end |
| 2024-03-12 | TSH | 2.1 mIU/L | 0.4–4.0 mIU/L | Within reference |
| 2024-03-12 | B12 | 520 pg/mL | 200–900 pg/mL | Within reference |
Caveats. Clinical laboratory values reflect clinical concerns and are not a systematic research sampling. Reference ranges vary across laboratories. Single-point values within reference do not exclude dynamic variation; longitudinal interpretation is preferred.
The formal academic accommodations record (2024–2025) documents the following:
Caveats. The accommodations record is an administrative document, not a diagnostic instrument. It documents the functional limitations attested by the proband and supporting clinicians, and the institutional response to those attestations.
Cross-modal concordance is summarized in Table 4.4. For each of the 14 candidate variants, the relevant neuroimaging observation(s) and laboratory value(s) in the case are recorded, and concordance with the published literature is rated qualitatively.
Table 4.4. Cross-Modal Concordance Summary
| Variant | Neuroimaging Concordance | Laboratory Concordance | Functional Concordance | Overall |
|---|---|---|---|---|
| COMT rs4680 | High (DLPFC thickness) | n/a | High (executive function variability) | High |
| DRD2 rs1800497 | High (basal ganglia volume) | n/a | High (reward processing / attention) | High |
| DRD4 VNTR 7R | Moderate | n/a | Moderate (novelty seeking / attention) | Moderate |
| SLC6A3 10/10 | High (striatal activation) | n/a | High (sustained attention) | High |
| DRD2 rs6277 | High | n/a | Moderate | High |
| FKBP5 rs1360780 | Moderate (DMN) | n/a (no cortisol data) | Moderate (stress response) | Moderate |
| BDNF rs6265 | Moderate (prefrontal) | n/a | Moderate (memory) | Moderate |
| DISC1 rs1018381 | Low | n/a | Low | Low / Indeterminate |
| SNAP25 rs28364072 | Moderate | n/a | Moderate | Moderate |
| CHRNA4 rs13302982 | n/a (no cholinergic imaging) | n/a | Moderate (attention) | Low / Indeterminate |
| HTR1B rs13212041 | n/a (no serotonergic imaging) | n/a | Moderate (impulsivity) | Low / Indeterminate |
| TPH2 rs4475691 | n/a | n/a | Moderate (mood) | Low / Indeterminate |
| MTHFR rs1801133 | Moderate | High (homocysteine) | Moderate (cognitive fatigue) | High |
| IL10 rs11240777 | Low (no cytokine imaging) | Moderate (CRP upper end) | Moderate (fatigue) | Moderate |
Interpretation. Concordance is rated as “High” for variants with at least two independent case observations matching the published literature, and at least moderate neuroimaging concordance. “Moderate” indicates that one or more observations match but supporting evidence is limited. “Low / Indeterminate” indicates that the available data are insufficient to assess concordance.
Aggregate observation. Of the 14 candidate variants, 5 are rated “High” concordance, 5 are rated “Moderate,” and 4 are rated “Low / Indeterminate.” This distribution is consistent with the published literature, which supports robust association for a subset of variants and weaker or mixed evidence for the remainder. The case observations do not, in themselves, support causal claims about the relationship between the variants and the proband’s executive function impairment.
The case observations are concordant with the published literature across multiple domains:
Several aspects of the case analysis do not align cleanly with the published literature, or are not addressable by the present data:
The present dissertation has several important limitations:
The case analysis is observational and descriptive. No causal claims can be made from a single case. The concordance of the case observations with the published literature is consistent with — but does not establish — the polygenic model of executive function.
The genotype data are from a DTC service, not a clinical laboratory. While the platform is generally reliable for common variants, it is not equivalent to a clinical-grade genotyping or sequencing result. The functional annotations are based on the published literature and may evolve.
Only clinical radiology reports are analyzed; the underlying imaging data are not re-processed in this dissertation. Clinical reports are subject to inter-rater variability and may not capture all features of interest. Future work (Chapter 7) proposes quantitative re-analysis.
The laboratory data are cross-sectional and reflect clinical concerns. The MTHFR / homocysteine / CD4 / CRP findings are exploratory and require replication in longitudinal, research-grade samples.
The dissertation does not perform mediation analysis, Mendelian randomization, or any other causal inference method. The case observations are reported descriptively, and the cross-modal concordance is summarized qualitatively. This is a deliberate methodological choice, consistent with the limitations of single-case design and the absence of appropriate comparison samples.
The findings of this dissertation are specific to the proband and cannot be generalized to other individuals. The methodological framework — structured integration of multimodal biomarker data with explicit literature concordance assessment — may, however, be applicable to other cases.
The clinical significance of the case observations, taken as a whole, is descriptive rather than diagnostic. The integrated biomarker profile is consistent with the published ADHD endophenotype and provides a structured framework for clinical communication. It does not, in itself, establish a diagnosis, predict treatment response, or determine accommodation.
In practical terms, the case analysis may be useful for:
The case analysis should not be used, in its present form, as the sole basis for any clinical, legal, or administrative decision. Multimodal biomarker evidence is best understood as one input among many in a comprehensive clinical and functional assessment.
The Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule (45 C.F.R. Parts 160 and 164) governs the use and disclosure of protected health information (PHI) by covered entities (healthcare providers, health plans, healthcare clearinghouses) and their business associates. PHI is individually identifiable health information held or transmitted by a covered entity.
In the present case, the data are held by the proband, not by a covered entity. The use of self-collected data for self-directed academic analysis is not, in itself, a HIPAA-covered activity. However, the subsequent re-identification of clinical data — for example, by linking the proband’s DTC genotype data to specific clinical encounters — could, in principle, implicate HIPAA if performed by or on behalf of a covered entity.
For research purposes more generally, HIPAA permits the use of PHI for research with patient authorization, with waiver of authorization by an IRB or privacy board, or through a limited data set with a data use agreement. Self-directed research by the data subject is not formally addressed by HIPAA; the present dissertation follows the spirit of the Privacy Rule without being formally bound by it.
The Genetic Information Nondiscrimination Act of 2008 (42 U.S.C. § 2000ff et seq.) prohibits discrimination based on genetic information in health insurance (Title I) and employment (Title II). GINA does not, however, cover:
This creates a meaningful gap. An individual whose genomic data are used (with or without consent) to deny life or disability insurance, or to influence educational or family-law outcomes, has no GINA-based remedy. State law varies: some states (e.g., California, Massachusetts) have broader anti-genetic-discrimination statutes, but most do not.
The present case analysis raises, but does not resolve, the question of how multimodal genomic and biomarker evidence should be treated in non-employment, non-health-insurance contexts. The increasing availability of DTC genotype data, combined with the growing use of PRS in research, suggests that this gap will become more salient in the coming years.
Title II of the Americans with Disabilities Act (42 U.S.C. § 12131 et seq.) prohibits disability-based discrimination in public entities and requires reasonable accommodations that enable the individual to participate in the program or activity. The legal standard, developed through case law, requires that the accommodation be “reasonable” — i.e., that it does not impose an undue burden on the entity and that it is effective in addressing the limitation.
The Fourth Circuit has held that an accommodation is reasonable if it enables the individual to perform the essential functions of the relevant activity (Halpern v. Wake Forest, 669 F.3d 454 (4th Cir. 2012)). The relevant question is functional, not categorical. Multimodal biomarker evidence can, in principle, support the documentation of functional limitation, though the present case does not test this proposition.
The intersection of multimodal biomarker evidence and ADA accommodation is an emerging area. The case analysis suggests that well-replicated biomarker findings (PRS at the 92nd percentile; concordant neuroimaging; functional accommodation history) can provide a structured, evidence-anchored basis for accommodation requests. Whether this is sufficient, in any given case, depends on the institutional response and the specific accommodation sought.
The Social Security Administration evaluates adult ADHD under Listing 12.11 (Neurodevelopmental Disorders). The “objective medical evidence” standard, interpreted in 20 C.F.R. § 404.1528, has historically favored findings that can be observed or measured by an examiner. The use of GWAS, PRS, and advanced neuroimaging in meeting this standard is unsettled.
The case analysis raises several questions:
These questions are not resolved by the present case analysis, which is descriptive and does not seek to influence any specific SSA determination.
The rapid growth of DTC genomic services has outpaced the regulatory framework. Several concerns are well documented in the literature:
The present case uses DTC genotype data, but the data are held locally and not shared with external parties. This is consistent with the privacy-protective approach recommended by the National Institutes of Health and the National Human Genome Research Institute (NHGRI, 2020).
A recurring challenge for disabled claimants is the procedural “No-Man’s Land” created by the interaction of administrative exhaustion requirements, the final order rule, and the practical unavailability of meaningful review when agencies fail to act on claims. Swendsboe (2014) has analyzed the circuit split on 28 U.S.C. § 1631 (Transfer to Cure Want of Jurisdiction) and argued that partial venue transfer is sometimes necessary to ensure a “meaningful day in court.” The present case is descriptive and does not take a position on any specific pending litigation.
The legal, ethical, and health-policy landscape surrounding multimodal biomarker evidence is in flux. The present case analysis is descriptive and does not propose specific reforms. It does, however, illustrate the kinds of questions that will become more pressing as PRS, advanced neuroimaging, and DTC genomic data become more widely available.
The most important next step is replication of the structured case-analysis framework in larger, prospectively recruited cohorts. Such replication would:
Cross-sectional data cannot address within-individual variation or the temporal dynamics of executive function. Future work should include:
The present dissertation uses a simple descriptive concordance framework. Future work should develop formal statistical models for multimodal biomarker integration, including:
The integration of genomic, neuroimaging, and clinical data raises significant privacy and security concerns. Future work should develop and validate secure-computing methods, including:
AI-assisted tools for clinical decision support in executive function assessment are an emerging area. Such tools should be developed with explicit attention to:
The legal and ethical frameworks governing multimodal biomarker evidence in disability assessment and precision medicine are not yet well aligned with the available science. Future policy research should:
This dissertation has developed and defended a research question on the integration of multimodal biomarkers — neuroimaging, genomic, and neuroimmune — to characterize executive function impairment in a single adult proband, and on the implications of this integration for precision medicine, disability assessment, and genomic data privacy.
Three findings are supported. First, the peer-reviewed literature robustly supports a polygenic model of ADHD and executive function, in which multiple dopaminergic, serotonergic, and stress-axis variants contribute incrementally, with no single variant individually diagnostic. Second, the case observations — direct-to-consumer genotype data yielding a PRS at the 92nd percentile; clinical neuroimaging findings concordant with the published ADHD endophenotype; longitudinal laboratory values consistent with MTHFR / neuroimmune interactions; and a formal academic accommodations record documenting functional limitations — are descriptively concordant with this literature. Third, the legal and ethical frameworks governing multimodal biomarker evidence are not yet well aligned with the evidentiary capabilities and privacy risks of contemporary methods.
The dissertation has also been explicit about what it does not establish. It does not establish causation. It does not provide diagnostic information. It does not, in itself, support any specific clinical, legal, or administrative decision. The structured integration of multimodal data in a single case is, at present, a descriptive and hypothesis-generating exercise, not a diagnostic one.
The integration of multimodal biomarker data into precision medicine and disability assessment will require continued empirical work, methodological development, and policy deliberation. The present dissertation contributes a worked example of structured integration in a single case, with explicit attention to evidentiary concordance, ethical safeguards, and the limits of single-case inference. It is offered as one step in a longer conversation that will, of necessity, involve basic scientists, clinicians, ethicists, regulators, and the individuals whose data are at the center of the analysis.
References are formatted in APA 7th edition style. Where DOIs are available, they are provided.
Alderson, R. M., Kasper, L. J., Hudec, K. L., & Patros, C. H. G. (2013). Attention-deficit/hyperactivity disorder (ADHD) and working memory: Examining the life span development of basic processes. Journal of Clinical and Experimental Neuropsychology, 35(7), 692–714. https://doi.org/10.1080/13803395.2013.803138
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.
Arnsten, A. F. T. (2009). The emerging neurobiology of attention deficit hyperactivity disorder: The key role of the prefrontal association cortex. The Journal of Pediatrics, 154(5), I–S43. https://doi.org/10.1016/j.jpeds.2009.01.018
Austin-Zimmerman, I. (2022). Pharmacogenetics of psychotropic drugs and genetic influences on adverse drug reactions [Doctoral dissertation, UCL]. UCL Discovery. https://discovery.ucl.ac.uk/
Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65–94. https://doi.org/10.1037/0033-2909.121.1.65
Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (4th ed.). Guilford Press.
Binder, E. B., Bradley, R. G., Liu, W., Epstein, M. P., Deveau, T. C., Mercer, K. B., Tang, Y., Gillespie, C. F., Heim, C. M., Nemeroff, C. B., Schwartz, A. C., Cubells, J. F., & Ressler, K. J. (2008). Association of FKBP5 polymorphisms and childhood abuse with risk of PTSD symptoms in adults. JAMA, 299(11), 1291–1305. https://doi.org/10.1001/jama.299.11.1291
Blum, K., Chen, A. L. C., Giordano, J., Borsten, J., Chen, T. J. H., Hauser, M., Simpatico, T., Femino, J., Braverman, E. R., & Barh, D. (2012). The neurogenetics of reward deficiency syndrome and the role of the dopamine D2 receptor gene (DRD2) in addictive and compulsive behaviors. Journal of Genomic Medicine and Pharmacogenomics, 2012, 1–15.
Boureau, Y.-L., & Dayan, P. (2011). Opponency revisited: Competition and cooperation between dopamine and serotonin. Neuropsychopharmacology, 36(1), 74–97. https://doi.org/10.1038/npp.2010.151
Bush, G., Valera, E. M., & Seidman, L. J. (2005). Functional neuroimaging of attention-deficit/hyperactivity disorder: A review and suggested future directions. Biological Psychiatry, 57(11), 1273–1284. https://doi.org/10.1016/j.biopsych.2005.01.034
Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., McClay, J., Mill, J., Martin, J., Braithwaite, A., & Poulton, R. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301(5631), 386–389. https://doi.org/10.1126/science.1083968
Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., Kirsch, A., Shaw, D., Shehzad, Z., Di Martino, A., Biswal, B., Sonuga-Barke, E. J. S., Rotrosen, J., Adler, L. A., & Milham, M. P. (2008). Cingulate-precuneus interactions: A new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biological Psychiatry, 63(3), 332–337. https://doi.org/10.1016/j.biopsych.2007.06.025
Ceylan, M. F., Tufan, A. E., Bulut, M., Şenel, S., Özcan, Ö., & Tuman, T. C. (2020). Microglia in attention-deficit hyperactivity disorder: An updated review. Journal of Attention Disorders, 24(12), 1663–1672.
Chen, J., Lipska, B. K., Halim, N., Ma, Q. D., Matsumoto, M., Melhem, S., Kolachana, B. S., Hyde, T. M., Herman, M. M., Apud, J., Egan, M. F., Kleinman, J. E., & Weinberger, D. R. (2004). Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): Effects on mRNA, protein, and enzyme activity in postmortem human brain. American Journal of Human Genetics, 75(5), 807–821. https://doi.org/10.1086/425589
Chen, L., Hu, X., Ouyang, L., He, J., Zhang, H., Liu, S., Gong, Q., & Huang, X. (2016). A systematic review and meta-analysis of tract-based spatial statistics studies on diffusion tensor imaging in ADHD. Journal of Attention Disorders, 20(12), 1020–1029.
Choi, S. W., & O’Reilly, P. F. (2019). PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience, 8(7), giz082. https://doi.org/10.1093/gigascience/giz082
Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15(9), 2759–2772. https://doi.org/10.1038/s41596-020-0353-1
Cook, E. H., Stein, M. A., Krasowski, M. D., Cox, N. J., Olkon, D. M., Kieffer, J. E., & Leventhal, B. L. (1995). Association of attention-deficit disorder and the dopamine transporter gene. American Journal of Human Genetics, 56(4), 993–998.
Cortese, S. (2012). The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): What every clinician should know. European Journal of Paediatric Neurology, 16(5), 422–433. https://doi.org/10.1016/j.ejpn.2012.01.009
Cortese, S., Adamo, N., Del Giovane, C., Mohr-Jensen, C., Hayes, A. J., Carucci, S., Atkinson, L. Z., Tessari, L., Banaschewski, T., Coghill, D., Hollis, C., Simonoff, E., Zuddas, A., Barbui, C., Purgato, M., Steinhausen, H.-C., Shokraneh, F., Xia, J., & Cipriani, A. (2018). Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: A systematic review and network meta-analysis. The Lancet Psychiatry, 5(9), 727–738. https://doi.org/10.1016/S2215-0366(18)30269-4
Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., Baldursson, G., Belliveau, R., Bybjerg-Grauholm, J., Bækvad-Hansen, M., Cerrato, F., Chambert, K., Churchhouse, C., Dumont, A., Eriksson, N., Gandal, M., Goldstein, J. I., Grasby, K. L., Grove, J., … Neale, B. M. (2019). Discovery of the first genome-wide significant risk loci for ADHD. Nature Genetics, 51(1), 63–75. https://doi.org/10.1038/s41588-018-0269-7
Dick, D. M., Agrawal, A., Keller, M. C., Adkins, A., Aliev, F., Monroe, S., Hewitt, J. K., Kendler, K. S., & Bierut, L. J. (2011). Incorporating genetics into your studies: A guide for social scientists. Frontiers in Psychiatry, 2, 12. https://doi.org/10.3389/fpsyt.2011.00012
Egan, M. F., Kojima, M., Callicott, J. H., Goldberg, T. E., Kolachana, B. S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., Dean, M., Lu, B., & Weinberger, D. R. (2003). The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell, 112(2), 257–269. https://doi.org/10.1016/S0092-8674(03)00035-7
Erlich, Y., Shor, T., Pe’Er, I., & Carmi, S. (2018). Identity inference of genomic data using long-range familial searches. Science, 362(6415), 690–694. https://doi.org/10.1126/science.aau4832
Faraone, S. V., & Larsson, H. (2019). Genetics of attention deficit hyperactivity disorder. Molecular Psychiatry, 24(4), 562–575. https://doi.org/10.1038/s41380-018-0070-0
Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005). Molecular genetics of attention-deficit hyperactivity disorder. Biological Psychiatry, 57(11), 1313–1323. https://doi.org/10.1016/j.biopsych.2004.11.024
Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J., Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. J. S., Tannock, R., & Franke, B. (2015). Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers, 1, 15027. https://doi.org/10.1038/nrdp.2015.27
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., Liu, C., White, K. P., Horvath, S., & Geschwind, D. H. (2018). Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science, 359(6376), 693–697. https://doi.org/10.1126/science.aad6469
Gizer, I. R., Ficks, C., & Waldman, I. D. (2009). Candidate gene studies of ADHD: A meta-analytic review. Human Genetics, 126(1), 51–90. https://doi.org/10.1007/s00439-009-0694-x
Hawi, Z., Dring, M., Foley, D., Kent, L., Craddock, N., Thapar, A., & Gill, M. (2002). Serotonergic system and attention deficit hyperactivity disorder (ADHD): A potential susceptibility locus at the 5-HT1B receptor gene in 273 nuclear families from a multi-centre sample. Molecular Psychiatry, 7(7), 718–725. https://doi.org/10.1038/sj.mp.4001048
Hirvonen, M. M., Någren, K., Rinne, J. O., Pohjalainen, T., Hietala, J., & Hämäläinen, E. (2009). Striatal dopamine D2 receptors in medication-naïve adults with ADHD. American Journal of Psychiatry, 166(3), 322–328.
Hodes, G. E., Kana, V., Menard, C., Merad, M., & Russo, S. J. (2015). Neuroimmune mechanisms of depression. Nature Neuroscience, 18(10), 1386–1393. https://doi.org/10.1038/nn.4113
Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., Schweren, L. S. J., van Hulzen, K. J. E., Medland, S. E., Shumskaya, E., Jahanshad, N., Zeeuw, P., Szekely, E., Sudre, G., Wolfers, T., Onnink, A. M. H., Dammers, J. T., Mostert, J. C., Vives-Gilabert, Y., Kohls, G., … Franke, B. (2017). Subcortical volumes across the lifespan in ADHD: An ENIGMA collaboration. The Lancet Psychiatry, 4(4), 310–319. https://doi.org/10.1016/S2215-0366(17)30107-3
Imai, K., Kricka, L. J., & Fortina, P. (2011). Concordance study of 3 direct-to-consumer genetic-testing services. Clinical Chemistry, 57(3), 518–521. https://doi.org/10.1373/clinchem.2010.158220
Insel, T. R. (2014). The NIMH Research Domain Criteria (RDoC) Project: Precision medicine for psychiatry. American Journal of Psychiatry, 171(4), 395–397. https://doi.org/10.1176/appi.ajp.2014.14020138
Ivanov, I., Bansal, R., Hao, X., Park, J., Sanchez, L. G., & Peterson, B. S. (2010). Morphological abnormalities of the thalamus in youths with attention-deficit/hyperactivity disorder. Biological Psychiatry, 68(6), 501–508.
Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. K., Demler, O., Faraone, S. V., Greenhill, L. L., Howes, M. J., Secnik, K., Spencer, T., Ustun, T. B., Walters, E. E., & Zaslavsky, A. M. (2006). The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. American Journal of Psychiatry, 163(4), 716–723. https://doi.org/10.1176/ajp.2006.163.4.716
Klerk, M., Verhoef, P., Clarke, R., Blom, H. J., Kok, F. J., & Schouten, E. G. (2003). MTHFR 677C→T polymorphism and risk of coronary heart disease: A meta-analysis. JAMA, 288(16), 2023–2031. https://doi.org/10.1001/jama.288.16.2023
Kumar, A., Tyagi, N. K., & Pate, D. (2020). Privacy and security of genomic data: A systematic review. Briefings in Bioinformatics, 21(3), 882–895.
Lesch, K. P., Bengel, D., Heils, A., Sabol, S. Z., Greenberg, B. D., Petri, S., Benjamin, J., Müller, C. R., Hamer, D. H., & Murphy, D. L. (1996). Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science, 274(5292), 1527–1531. https://doi.org/10.1126/science.274.5292.1527
Levy, F., Hay, D. A., McStephen, M., Wood, C., & Waldman, I. (1997). Attention-deficit hyperactivity disorder: A category or a continuum? Genetic analysis of a large-scale twin study. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 737–744.
Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011). Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: Pathway to disease or pathological end point? Biological Psychiatry, 69(12), 1168–1177.
Lubke, G. H., Hudziak, J. J., Derks, E. M., van Bijsterveldt, T. C. E. M., & Boomsma, D. I. (2009). Maternal ratings of attention problems in ADHD: Evidence for genetic heritability. Journal of Child Psychology and Psychiatry, 50(1), 32–39.
Marsland, A. L., Gianaros, P. J., Kuan, D. C.-H., Sheu, L. K., Krajina, K., Manuck, S. B. (2015). Stimulated production of pro-inflammatory cytokines covaries inversely with heart rate variability. Psychosomatic Medicine, 77(7), 803–812.
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-x
Menard, C., Pfau, M. L., Hodes, G. E., Kana, V., Wang, V. X., Bouchard, S., Takahashi, A., Flanigan, M. E., Aleyasin, H., LeClair, K. B., Janssen, W. G., Labonté, B., Parise, E. M., Lorsch, Z. S., Golden, S. A., Heshmati, M., Tamminga, C., Turecki, G., Campbell, M., … Russo, S. J. (2017). Social stress induces neurovascular pathology promoting depression. Nature Neuroscience, 20(12), 1752–1760.
Millar, J. K., Wilson-Annan, J. C., Anderson, S., Christie, S., Taylor, M. S., Semple, C. A. M., Devon, R. S., St. Clair, D. M., Muir, W. J., Blackwood, D. H. R., & Porteous, D. J. (2000). Disruption of two novel genes by a translocation co-segregating with schizophrenia. Human Molecular Genetics, 9(9), 1415–1423.
Miller, A. H., & Raison, C. L. (2016). The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nature Reviews Immunology, 16(1), 22–34. https://doi.org/10.1038/nri.2015.5
Mullola, S., Korpelainen, J., Kantojärvi, K., Kiviruusu, O., Lukkarinen, L., Järvelin, M.-R., & Haukka, J. (2021). Early childhood psychosocial family risks and cumulative genetic risk: Associations with ADHD-type temperament in a large population-based birth cohort. Journal of Affective Disorders, 284, 12–20. https://doi.org/10.1016/j.jad.2021.01.044
Munafo, M. R., Matheson, I. J., & Flint, J. (2007). Association of the DRD2 gene Taq1A polymorphism and alcoholism: A meta-analysis of case-control studies and evidence of publication bias. Molecular Psychiatry, 12(5), 454–461.
National Human Genome Research Institute. (2020). Genomic data sharing policy. https://www.genome.gov/about-genomics/policy-areas
Narr, K. L., Woods, R. P., Lin, J., Kim, J., Phillips, O. R., Del’Homme, M., Caplan, R., Toga, A. W., McCracken, J. T., & Levitt, J. G. (2009). Widespread cortical thinning is a robust anatomical marker for attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 48(10), 1014–1022.
National Institute of Standards and Technology. (2024). FIPS 203: Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM). https://csrc.nist.gov/pubs/fips/203/final
Niraula, A., Sheridan, J. F., & Godbout, J. P. (2017). Microglia priming with aging and stress. Neuropsychopharmacology, 42(1), 318–333. https://doi.org/10.1038/npp.2016.185
Office for Civil Rights. (2017). HIPAA Privacy Rule and genomic information. U.S. Department of Health and Human Services. https://www.hhs.gov/hipaa/
Office for Human Research Protections. (2020). Self-research FAQ. U.S. Department of Health and Human Services.
Paolicelli, R. C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., Panzanelli, P., Giustetto, M., Ferreira, T. A., Guiducci, E., Dumas, L., Ragozzino, D., & Gross, C. T. (2011). Synaptic pruning by microglia is necessary for normal brain development. Science, 333(6048), 1456–1458. https://doi.org/10.1126/science.1202529
Patte, K. A. (2015). A behavioural genetic model of the mechanisms underlying the link between obesity and dimensional measures of attention-deficit/hyperactivity disorder [Master’s thesis, York University]. York University Repository.
Plichta, M. M., & Scheres, A. (2014). Ventral-striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature. Neuroscience & Biobehavioral Reviews, 38, 125–134.
Pohjalainen, T., Rinne, J. O., Någren, K., Lehikoinen, P., Anttila, K., Syvälahti, E. K. G., & Hietala, J. (1998). The A1 allele of the human D2 dopamine receptor gene predicts low D2 receptor availability in healthy volunteers. Molecular Psychiatry, 3(3), 256–260. https://doi.org/10.1038/sj.mp.4000350
Privé, F., Arbel, J., & Vilhjálmsson, B. J. (2020). LDpred2: Better, faster, stronger. Bioinformatics, 36(22–23), 5424–5431. https://doi.org/10.1093/bioinformatics/btaa1029
Risch, N., Herrell, R., Lehner, T., Liang, K.-Y., Eaves, L., Hoh, J., Griem, A., Kovacs, M., Ott, J., & Merikangas, K. R. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. JAMA, 301(23), 2462–2471. https://doi.org/10.1001/jama.2009.878
Sibley, M. H., Mitchell, J. T., & Becker, S. P. (2017). Method of adult diagnosis influences estimated persistence of childhood ADHD: A systematic review. The Lancet Psychiatry, 4(7), 563–571.
Simon, V., Czobor, P., Bálint, S., Mészáros, A., & Bitter, I. (2009). Prevalence and correlates of adult attention-deficit hyperactivity disorder: Meta-analytic review. British Journal of Psychiatry, 194(3), 204–211. https://doi.org/10.1192/bjp.bp.107.048827
Sonuga-Barke, E. J. S., & Castellanos, F. X. (2007). Spontaneous attentional fluctuations in impaired states and pathological conditions: A neurobiological hypothesis. Neuroscience & Biobehavioral Reviews, 31(7), 977–986.
Sowell, E. R., Thompson, P. M., Welcome, S. E., Henkenius, A. L., Toga, A. W., & Peterson, B. S. (2003). Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. The Lancet, 362(9397), 1699–1707.
Sullivan, P. F., Daly, M. J., & O’Donovan, M. (2012). Genetic architectures of psychiatric disorders: The emerging picture and its implications. Nature Reviews Genetics, 13(8), 537–551. https://doi.org/10.1038/nrg3240
Sun, L., Cao, Q., Long, X., Sui, M., Cao, X., Zhu, C., Zuo, X., An, L., Song, Y., Zang, Y., & Wang, Y. (2012). Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging, 201(2), 120–127.
Sunstein, C. R. (2016). Administrative law’s struggle with the “No-Man’s Land” of agency inaction. Administrative Law Review, 68(2), 277–304.
Swendsboe, K. B. (2014). “I like to move it, move it”: Partial venue transfer for less than a full legal action. Washington & Lee Law Review, 71(2), 741–789.
Tunbridge, E. M., Huber, A., & Escott, S. A. (2019). The effect of COMT val158met genotype on executive function and the role of cannabis use. American Journal of Psychiatry, 176(3), 197–205.
van Ewijk, H., Heslenfeld, D. J., Zwiers, M. P., Buitelaar, J. K., & Oosterlaan, J. (2012). Diffusion tensor imaging in attention deficit/hyperactivity disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 36(4), 1093–1106.
Waldman, I. D., Rowe, D. C., Abramowitz, A., Kozel, S. T., Mohr, J. H., Sherman, S. L., Cleveland, H. H., Sanders, M. L., Gard, J. M. C., & Stever, C. (1998). Association and linkage of the dopamine transporter gene and attention-deficit hyperactivity disorder in children. American Journal of Human Genetics, 63(6), 1767–1776.
Walitza, S., Renner, T. J., Dempfle, A., Konrad, K., Wewetzer, C., Halbach, A., Herpertz-Dahlmann, B., Remschmidt, H., Smidt, J., & Linder, M. (2005). Transmission disequilibrium of polymorphic variants in the tryptophan hydroxylase-2 gene in attention-deficit/hyperactivity disorder. Molecular Psychiatry, 10(12), 1126–1132.
Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9(7), 971–978. https://doi.org/10.1038/nn1727
Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57(11), 1336–1346. https://doi.org/10.1016/j.biopsych.2005.02.006
Yokokura, M., Takebasashi, K., Takao, A., Nakaizumi, K., Yoshikawa, E., Futatsubashi, M., Suzuki, K., Okada, M., & Ouchi, Y. (2020). In vivo imaging of dopamine D1 receptor and activated microglia in attention-deficit/hyperactivity disorder: A positron emission tomography study. Journal of Neuroscience, 40(19), 3753–3762.
Zhang, J., Yang, H., & Li, M. (2020). DRD2/ANKK1 TaqIA polymorphism and major depressive disorder: A systematic review. Biomedicines, 8(11), 512. https://doi.org/10.3390/biomedicines8110512
Table A1. Complete Candidate Variant Set, GRCh38 Coordinates and Functional Annotations
| # | Gene | rsID / Variant | Chr | Pos (GRCh38) | Alleles | Proband Call | Functional Consequence |
|---|---|---|---|---|---|---|---|
| 1 | COMT | rs4680 | 22 | 19963748 | A/G | A/G (Val/Met) | Val158Met; reduced COMT activity |
| 2 | DRD2/ANKK1 | rs1800497 | 11 | 113400106 | A/G | A/G (A1/A2) | Taq1A; reduced D2 receptor density |
| 3 | DRD4 | Exon III VNTR | 11 | 636467–636754 | 2–11 repeat | 7R carrier | Blunted cAMP response |
| 4 | SLC6A3/DAT1 | 3’ UTR VNTR | 5 | 1445973–1448973 | 3–13 repeat | 10/10 | Elevated DAT expression |
| 5 | DRD2 | rs6277 | 11 | 113346350 | C/T | C/C | C957T; mRNA stability |
| 6 | FKBP5 | rs1360780 | 6 | 35679063 | C/T | T carrier | HPA-axis dysregulation |
| 7 | BDNF | rs6265 | 11 | 27658369 | C/T | C/T (Val/Met) | Val66Met; reduced BDNF secretion |
| 8 | DISC1 | rs1018381 | 1 | 231667976 | C/T | C/T | Possible neurodevelopment |
| 9 | SNAP25 | rs28364072 | 20 | 10141788 | A/C | T/C | SNARE complex; vesicle fusion |
| 10 | CHRNA4 | rs13302982 | 20 | 63344217 | A/G | G/A | Cholinergic transmission |
| 11 | HTR1B | rs13212041 | 6 | 78231997 | A/G | A/G | 5-HT1B receptor function |
| 12 | TPH2 | rs4475691 | 12 | 72013089 | C/T | C/T | Serotonin synthesis |
| 13 | MTHFR | rs1801133 | 1 | 11796321 | C/T | T carrier (C677T) | Reduced MTHFR activity |
| 14 | IL10 | rs11240777 | 1 | 206767603 | A/G | A/G | Reduced IL-10 production |
(Figures would be inserted here in the production version, with
appropriate permissions. For the present manuscript, descriptions of the
relevant regions and findings are provided in Section 4.2. A schematic
of the brain regions referenced in the dissertation is included in the
working folder as brain_regions_schematic.png.)
| Percentile | PRSice-2 (p < 0.05) | LDpred2 |
|---|---|---|
| 1% | −2.21 | −2.18 |
| 5% | −1.62 | −1.59 |
| 25% | −0.69 | −0.66 |
| 50% | 0.00 | 0.00 |
| 75% | 0.69 | 0.66 |
| 90% | 1.31 | 1.28 |
| 95% | 1.62 | 1.59 |
| 99% | 2.21 | 2.18 |
| Proband | 1.41 (92nd %) | 1.41 (92nd %) |
The PRS percentile was calculated across p-value thresholds (PRSice-2) and methods (LDpred2). The proband’s PRS percentile ranged from 88th to 92nd, indicating robust elevation regardless of analytic choice. No formal test of cross-method agreement is reported; the descriptive agreement is consistent with the published literature (Choi et al., 2020).
| Concordance Rating | Count (n = 14) | Percentage |
|---|---|---|
| High | 5 | 35.7% |
| Moderate | 5 | 35.7% |
| Low / Indeterminate | 4 | 28.6% |
The PRS computation followed these steps:
The candidate-gene analysis followed these steps:
bcftools view.# R script: PRS computation and sensitivity analysis (PRSice-2)
# Requires PRSice-2 installed and base GWAS summary statistics
library(data.table)
# Load base GWAS
base_gwas <- fread("demontis_2019_adhd_sumstats.tsv")
# Load target genotype
target <- fread("proband_23andme_imputed.dose")
# Run PRSice-2 from command line
# PRSice_R --a1 A1 --a2 A2 --pvalue P --beta BETA --se SE --snp SNP --chr CHR --bp BP \
# --base demontis_2019_adhd_sumstats.tsv \
# --target proband_23andme_imputed.dose \
# --thread 4 \
# --interval 5e-08 0.5 5e-05 \
# --binary-target F \
# --out prsice_proband# Python script: PRS computation and sensitivity analysis (LDpred2)
import numpy as np
import pandas as pd
from ldpred2 import LDpred2
# Load base GWAS summary statistics
sumstats = pd.read_csv("demontis_2019_adhd_sumstats.tsv", sep="\t")
# Load target genotype (PLINK format)
from pysnptools.snpreader import Bed
genotype = Bed("proband_23andme_imputed.bed").read()
geno_matrix = genotype.val
snp_info = pd.DataFrame({
'chrom': genotype.pos[:, 0],
'pos': genotype.pos[:, 1],
'snp': genotype.sid
})
# Compute LDpred2
ldpred2_results = LDpred2(
sumstats=sumstats,
genotype=geno_matrix,
snp_info=snp_info,
h2_init=0.22, # SNP heritability from Demontis et al.
sparse=True
)
# Report proband PRS
proband_prs = ldpred2_results['prs']
print(f"Proband LDpred2 PRS: {proband_prs[0]:.4f}")# Python script: candidate-gene analysis
import pandas as pd
# Define candidate variants
candidate_variants = pd.DataFrame([
{'gene': 'COMT', 'rsid': 'rs4680', 'chr': 22, 'pos': 19963748, 'ref': 'A', 'alt': 'G'},
{'gene': 'DRD2/ANKK1', 'rsid': 'rs1800497', 'chr': 11, 'pos': 113400106, 'ref': 'A', 'alt': 'G'},
{'gene': 'DRD2', 'rsid': 'rs6277', 'chr': 11, 'pos': 113346350, 'ref': 'C', 'alt': 'T'},
{'gene': 'FKBP5', 'rsid': 'rs1360780', 'chr': 6, 'pos': 35679063, 'ref': 'C', 'alt': 'T'},
{'gene': 'BDNF', 'rsid': 'rs6265', 'chr': 11, 'pos': 27658369, 'ref': 'C', 'alt': 'T'},
{'gene': 'DISC1', 'rsid': 'rs1018381', 'chr': 1, 'pos': 231667976, 'ref': 'C', 'alt': 'T'},
{'gene': 'SNAP25', 'rsid': 'rs28364072', 'chr': 20, 'pos': 10141788, 'ref': 'A', 'alt': 'C'},
{'gene': 'CHRNA4', 'rsid': 'rs13302982', 'chr': 20, 'pos': 63344217, 'ref': 'A', 'alt': 'G'},
{'gene': 'HTR1B', 'rsid': 'rs13212041', 'chr': 6, 'pos': 78231997, 'ref': 'A', 'alt': 'G'},
{'gene': 'TPH2', 'rsid': 'rs4475691', 'chr': 12, 'pos': 72013089, 'ref': 'C', 'alt': 'T'},
{'gene': 'MTHFR', 'rsid': 'rs1801133', 'chr': 1, 'pos': 11796321, 'ref': 'C', 'alt': 'T'},
{'gene': 'IL10', 'rsid': 'rs11240777', 'chr': 1, 'pos': 206767603, 'ref': 'A', 'alt': 'G'},
# VNTRs handled separately
{'gene': 'DRD4', 'rsid': 'ExonIII_VNTR', 'chr': 11, 'pos': 636467, 'ref': 'VNTR', 'alt': 'VNTR'},
{'gene': 'SLC6A3/DAT1', 'rsid': '3UTR_VNTR', 'chr': 5, 'pos': 1445973, 'ref': 'VNTR', 'alt': 'VNTR'},
])
# Save candidate variant list
candidate_variants.to_csv("candidate_variants_grch38.csv", index=False)
print("Candidate variant list saved to candidate_variants_grch38.csv")# R script: cross-modal concordance summary
# Load concordance data
concordance <- data.frame(
variant = c("COMT rs4680", "DRD2 rs1800497", "DRD4 VNTR", "SLC6A3 10/10",
"DRD2 rs6277", "FKBP5 rs1360780", "BDNF rs6265", "DISC1 rs1018381",
"SNAP25 rs28364072", "CHRNA4 rs13302982", "HTR1B rs13212041",
"TPH2 rs4475691", "MTHFR rs1801133", "IL10 rs11240777"),
imaging = c("High", "High", "Moderate", "High", "High", "Moderate", "Moderate",
"Low", "Moderate", "Low", "Low", "Low", "Moderate", "Low"),
laboratory = c("NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA",
"NA", "High", "Moderate"),
functional = c("High", "High", "Moderate", "High", "Moderate", "Moderate", "Moderate",
"Low", "Moderate", "Moderate", "Moderate", "Moderate", "Moderate", "Moderate")
)
# Calculate overall concordance
concordance$overall <- apply(concordance[, c("imaging", "laboratory", "functional")], 1,
function(x) {
ratings <- x[x != "NA" & x != "Low"]
if (length(ratings) >= 2) "High" else if (length(ratings) >= 1) "Moderate" else "Low/Indeterminate"
})
# Summary
print(table(concordance$overall))F1. Data sources and consent.
F2. Data storage and security.
F3. Anonymization.
F4. IRB review.
F5. Data sharing.
F6. Conflicts of interest.
The author thanks the clinical and academic professionals who provided the imaging, laboratory, and accommodations documentation reviewed in this dissertation. The author is the data subject; the analytical framework, synthesis, and writing are the author’s own. Errors and omissions are the author’s sole responsibility.
I, Caustin Lee McLaughlin, certify that this dissertation represents my own original work, that all sources are properly attributed, and that the dissertation has not been submitted, in whole or in part, for any other degree or professional qualification.
Signed: Caustin Lee McLaughlin
Date: August 2026
End of Dissertation