Exploring the Evolutionary Basis and Population Genetics of ADHD

Richard Murdoch Montgomery

doi:10.20944/preprints202406.0251.v1

Submitted:

04 June 2024

Posted:

05 June 2024

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Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) exhibits a notable prevalence across diverse populations, with estimates ranging from 4% to 6%. This wide range prompts an investigation into the evolutionary and genetic underpinnings of ADHD, especially considering the markedly different environments faced by ancestral human populations compared to today's structured societies. This study examines the potential adaptive advantages of ADHD-associated traits in hunter-gatherer contexts, where hyperactivity, impulsivity, and novelty-seeking behaviors may have conferred survival benefits. We employ the principles of population genetics, including Hardy-Weinberg equilibrium, selection coefficients, and genetic drift, to model the dynamics of ADHD allele frequencies over time. Additionally, we discuss balancing selection as a mechanism for maintaining genetic diversity and how gene-environment interactions (GxE) contribute to the expression of ADHD in contemporary settings. Our analysis suggests that traits associated with ADHD, while potentially maladaptive in modern structured environments, could have been preserved through evolutionary processes due to their adaptive value in ancestral contexts. Understanding these dynamics provides a comprehensive view of ADHD's prevalence and highlights the significance of genetic and environmental interplay in shaping human behavior.

Keywords:

DHD

;

population genetics

;

characteristics

Subject:

Biology and Life Sciences - Ecology, Evolution, Behavior and Systematics

Section 1. Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by persistent patterns of inattention, hyperactivity, and impulsivity. This disorder affects an estimated 5-7% of children and around 2.5% of adults worldwide, showing significant variability in prevalence across different populations. This variability raises important questions about the evolutionary and genetic underpinnings of ADHD.

From an evolutionary perspective, it has been suggested that ADHD traits may have been advantageous in ancestral environments. For instance, in hunter-gatherer societies, behaviors such as hyperactivity, impulsivity, and novelty-seeking could have conferred survival benefits. These traits might have enhanced an individual’s ability to explore new environments, respond quickly to threats, and maintain vigilance, thereby increasing their chances of survival and reproduction (Jensen & Kettle, 2017; Hartmann, 1997).

The principles of population genetics provide a framework for understanding the persistence and prevalence of ADHD traits in modern populations. The Hardy-Weinberg equilibrium (HWE) model explains how allele frequencies are distributed in a population under ideal conditions without evolutionary forces acting on them. For ADHD, this can be applied to understand the distribution of alleles associated with ADHD traits. Additionally, concepts such as genetic drift, selection coefficients, and balancing selection help elucidate the dynamics that maintain genetic diversity (Gillespie, 2004; Hamilton, 2009). For instance, balancing selection could explain the continued presence of ADHD-related alleles, suggesting that heterozygotes (individuals with one ADHD-associated allele and one non-associated allele) may have a fitness advantage, thus maintaining these alleles in the gene pool.

Moreover, the interaction between genetic predispositions and environmental contexts, known as gene-environment interaction (GxE), is crucial in shaping the expression of ADHD. This perspective aligns with the mismatch theory, which posits that many modern disorders arise from a discordance between our evolved traits and contemporary environments (Barkley, 1997; Crespi, 2010). In structured, modern settings such as schools and workplaces, behaviors that were once adaptive may now be perceived as maladaptive. For example, the constant movement and distractibility that might have been beneficial for a nomadic hunter-gatherer now clash with the sedentary and focused demands of modern classrooms and offices.

This study aims to explore the evolutionary basis and population genetics of ADHD, providing insights into how ancient adaptive traits have persisted and how they interact with modern environments to influence the prevalence and expression of ADHD. By integrating evolutionary theory with population genetics, we can gain a comprehensive understanding of the complex nature of ADHD and its role in human diversity and adaptability. Understanding these dynamics could also inform more effective approaches to managing ADHD in contemporary settings, emphasizing the importance of context and environmental modifications alongside genetic considerations.

Section 2. Methodology

Section 2.1. Mathematical Framework

To investigate the evolutionary basis and population genetics of ADHD, we will use several equations and concepts from population genetics. These include the Hardy-Weinberg equilibrium, selection coefficients, genetic drift, and balancing selection. Below are the detailed equations and how they will be used in this study:

Hardy-Weinberg Equilibrium (HWE):

p + q = 1

(1)

p^{2} + 2 p q + q^{2} = 1

(2)

where

p

is the frequency of allele

A

(associated with ADHD), and

q

is the frequency of allele

a

(not associated with ADHD).

2.

Selection Coefficients:

Δ p = \frac{p (1 - p) (w_{A} - \overline{w})}{\overline{w}}

(3)

where:

$w_{A A}, w_{A a}$ , and $w_{a a}$ are the fitness of genotypes $A A, A a$ , and $a a$ respectively.
$w_{A}$ is the average fitness of individuals with the $A$ allele.
$\overline{w}$ is the average fitness of the population.

3.: Genetic Drift:

Genetic drift will be simulated using the Wright-Fisher model, where allele frequencies change over generations due to random sampling effects.

4.: Balancing Selection:

Balancing selection will be modeled by assuming that heterozygotes

(A a)

have a higher fitness than either homozygote (

A A

or

a a

):

w_{A A} < w_{A a} > w_{a a}

(3)

5.: Gene-Environment Interaction (GxE):

GxE will be considered by varying the fitness values in different environmental contexts to simulate how modern environments influence the expression of ADHD.

Section 2.2. Computational Simulation

We will use Python to simulate the evolutionary dynamics and population genetics of ADHD traits. A Python code will implement the described methodology (see Attachment).

Section 3. Results

Figure 1. Frequency and simulation of the model allele responsible for ADHA in different times of Civilization.

The simulation results demonstrate that allele frequency oscillations are significantly higher in ancestral environments compared to modern environments. This can be attributed to several key factors:

1. Selective Pressures in Ancestral Environments

In ancestral environments, the traits associated with ADHD—such as hyperactivity, impulsivity, and novelty-seeking—likely conferred survival advantages. These traits would have been beneficial for activities like hunting, gathering, and avoiding predators. Consequently, the allele frequencies for ADHD-associated traits would have been subject to positive selection, causing more significant fluctuations in their prevalence over generations.

2. Balancing Selection

Balancing selection could play a crucial role in maintaining genetic diversity in ancestral populations. In environments where a variety of traits were beneficial for survival, individuals with heterozygous genotypes (i.e., possessing both ADHD and non-ADHD alleles) might have had a fitness advantage. This advantage would result in fluctuating allele frequencies as the population balanced between different selective pressures. The equation for balancing selection: wAA<wAa>waawAA<wAa>waa indicates that heterozygotes could maintain both alleles in the population, contributing to oscillations in allele frequencies.

3. Gene-Environment Interactions (GxE)

The gene-environment interaction (GxE) models illustrate how different environments can alter the expression and fitness effects of genetic traits. In the ancestral environment, ADHD traits could have been highly advantageous, leading to positive selection for these alleles. In contrast, modern environments, which often demand sustained attention and controlled behavior (e.g., in academic and professional settings), might impose negative selection on these traits. This shift in selective pressures results in reduced oscillations in allele frequencies in modern environments.

4. Genetic Drift

In smaller ancestral populations (100 to 150 individuals), genetic drift would have had a more pronounced effect on allele frequencies. The Wright-Fisher model used in the simulation shows how random sampling can cause significant fluctuations in smaller populations. Modern human populations, being much larger, experience less genetic drift, leading to more stable allele frequencies over time.

5. Fitness Adjustments in Modern Environments

In the simulation, fitness values were adjusted to reflect the modern environment’s negative selective pressure on ADHD traits. For example, reducing the fitness of heterozygotes in the modern environment to 0.8 illustrates how these traits can be less advantageous today: Fitness in modern environment:wAa=0.8Fitness in modern environment:wAa=0.8. This adjustment results in a more gradual and stable decline in the frequency of ADHD-associated alleles, as opposed to the pronounced oscillations seen in the ancestral environment.

Summary of Results

The higher oscillations in allele frequencies in ancestral environments can be attributed to the following factors:

Positive selection for ADHD traits due to their adaptive advantages.
Balancing selection maintaining genetic diversity.
Significant impact of genetic drift in smaller populations.
Gene-environment interactions favoring ADHD traits in dynamic, survival-oriented settings.

In contrast, modern environments impose different selective pressures that reduce the frequency and oscillations of ADHD-associated alleles. The findings highlight the dynamic interplay between genetic traits and environmental contexts, illustrating how evolutionary forces shape the prevalence and expression of traits like ADHD.

Section 4. Discussion

The prevalence of ADHD in modern populations, ranging from 4% to 6%, prompts an examination of its genetic and evolutionary origins. The simulations and mathematical models utilized in this study provide insights into how ADHD-associated traits might have evolved and persisted over time.

Evolutionary Adaptations

In ancestral environments, traits such as hyperactivity, impulsivity, and novelty-seeking, which characterize ADHD, likely conferred significant survival advantages. These traits would have facilitated exploration, quick response to threats, and high levels of vigilance, essential for hunter-gatherer societies (Jensen & Kettle, 2017; Hartmann, 1997). The positive selection for these traits is reflected in the higher oscillations of allele frequencies in ancestral environments observed in the simulations.

Genetic Diversity and Balancing Selection

Balancing selection plays a crucial role in maintaining genetic diversity within populations. In ancestral environments, heterozygous individuals (carrying both ADHD and non-ADHD alleles) may have had a fitness advantage, ensuring the persistence of both alleles. This is mathematically represented as wAA<wAa>waawAA<wAa>waa, indicating that heterozygotes had higher fitness compared to homozygotes. The resulting genetic diversity would have allowed populations to adapt to a wide range of environmental challenges (Gillespie, 2004; Hamilton, 2009).

Gene-Environment Interactions

The gene-environment interaction (GxE) models illustrate how the expression of ADHD traits can vary significantly across different environments. In the simulations, modern environments were modeled by adjusting the fitness values to reflect the negative selective pressure on ADHD traits in structured settings like schools and workplaces. This aligns with the mismatch theory, which posits that many modern disorders arise from a discordance between our evolved traits and contemporary environments (Barkley, 1997; Crespi, 2010).

Genetic Drift and Population Size

Genetic drift, especially in smaller ancestral populations, contributed to significant fluctuations in allele frequencies. The Wright-Fisher model used in the simulations demonstrated how random sampling effects can lead to pronounced changes in allele frequencies over generations. In contrast, larger modern populations experience less genetic drift, leading to more stable allele frequencies.

Conclusion

This study highlights the dynamic interplay between genetic traits and environmental contexts, illustrating how evolutionary forces have shaped the prevalence and expression of ADHD. Traits associated with ADHD, which were likely advantageous in ancestral environments, have persisted due to positive selection and balancing selection mechanisms. However, the transition to modern environments, with different selective pressures, has altered the expression and perceived maladaptiveness of these traits.

The comprehensive analysis of population genetics and evolutionary psychology provides a deeper understanding of the complex nature of ADHD. By integrating mathematical models and computational simulations, this study underscores the importance of considering both genetic and environmental factors in addressing ADHD. Future research should continue to explore the gene-environment interactions and develop strategies to support individuals with ADHD in modern contexts, recognizing the evolutionary roots of these traits.

*The author declares no conflicts of interests.

Section 6. Attachment: Python Code

import numpy as np

import matplotlib.pyplot as plt

# Constants

generations = 100 # Number of generations to simulate

population_size = 1000 # Population size

initial_p = 0.3 # Initial frequency of allele A

selection_coefficients = {'AA': 0.9, 'Aa': 1.1, 'aa': 1.0} # Fitness values

environments = ['ancestral', 'modern'] # Different environmental contexts

environment_weights = {'ancestral': 0.5, 'modern': 0.5} # Weights for environment impact

# Hardy-Weinberg Equilibrium

def hardy_weinberg(p):

q = 1 - p

return p**2, 2*p*q, q**2

# Selection change in allele frequency

def selection_change(p, fitness):

w_AA = fitness['AA']

w_Aa = fitness['Aa']

w_aa = fitness['aa']

w_bar = p**2 * w_AA + 2 * p * (1 - p) * w_Aa + (1 - p)**2 * w_aa

p_next = (p**2 * w_AA + p * (1 - p) * w_Aa) / w_bar

return p_next

# Genetic Drift (Wright-Fisher model)

def genetic_drift(p, population_size):

return np.random.binomial(population_size, p) / population_size

# Simulation

def simulate(generations, initial_p, fitness, population_size, environment_weights):

p = initial_p

frequencies = [p]

for generation in range(generations):

# Apply selection

p = selection_change(p, fitness)

# Apply genetic drift

p = genetic_drift(p, population_size)

frequencies.append(p)

return frequencies

# Run simulation for different environments

results = {}

for env in environments:

fitness = selection_coefficients

if env == 'modern':

fitness['Aa'] = 0.8 # Adjust fitness for modern environment

results[env] = simulate(generations, initial_p, fitness, population_size, environment_weights)

# Plot results

plt.figure(figsize=(10, 6))

for env in environments:

plt.plot(results[env], label=f'Environment: {env}')

plt.xlabel('Generations')

plt.ylabel('Frequency of allele A')

plt.title('Evolutionary Dynamics of ADHD Alleles')

plt.legend()

plt.grid(True)

plt.show()

References

Barkley, R. A. (1997). ADHD and the Nature of Self-Control. Guilford Press.
Crespi, B. J. (2010). The strategies of the genes: Genomic conflicts, attachment theory, and development of the social brain. Behavioral and Brain Sciences, 33(4), 290-292.
Gillespie, J. H. (2004). Population Genetics: A Concise Guide. JHU Press.
Hamilton, M. B. (2009). Population Genetics. Wiley-Blackwell.
Hartmann, T. (1997). Attention Deficit Disorder: A Different Perception. Underwood Books.
Jensen, P. S., & Kettle, L. (2017). Evolutionary perspectives on ADHD: Explaining the persistence of a maladaptive trait. Current Psychiatry Reports, 19(3), 18.
Polanczyk, G., de Lima, M. S., Horta, B. L., Biederman, J., & Rohde, L. A. (2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry, 164(6), 942-948. [CrossRef]
Simon, V., Czobor, P., Bálint, S., Mészáros, Á., & Bitter, I. (2009). Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. British Journal of Psychiatry, 194(3), 204-211. [CrossRef]

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