Module 8

Evolution & Domestication

From Felis silvestris lybica to Felis catus — phylogenetics, population genetics, and the feline genome

1. From Wildcat to House Cat

The domestic cat (Felis catus) descends from the African wildcat (Felis silvestris lybica), a small solitary felid native to the Near East, North Africa, and the Arabian Peninsula. Unlike virtually every other domestic animal, cats were not deliberately selected for any human purpose — they self-domesticated.

The Commensal Pathway

Approximately 10,000 years ago, the Neolithic agricultural revolution in the Fertile Crescent created the ecological niche that drew wildcats to human settlements. Grain stores attracted rodents (Mus musculus); wildcats followed the mice. Humans tolerated and eventually encouraged the cats' presence because of their rodent control services. This “commensal pathway” to domestication contrasts sharply with the directed pathway used for dogs, cattle, and horses.

Key Archaeological Evidence

• Cyprus burial (Vigne et al., 2004): A cat was deliberately buried alongside a human at Shillourokambos, ~9,500 years ago. Since there are no native felids on Cyprus, the cat must have been transported by boat — implying a valued human-cat relationship.
• Egyptian domestication (Driscoll et al., 2007): Mitochondrial DNA analysis of 979 cats showed all domestic cats cluster within clade IV (F. s. lybica), with genetic signatures pointing to Near Eastern origin.
• Chinese millet farmers (Hu et al., 2014): Isotopic analysis of 5,300-year-old cat bones from Quanhucun showed cats were eating millet-fed rodents, confirming the commensal relationship.

Population Genetics of Domestication

The transition from wild to domestic involved several population genetic processes:

Founder Effect

When a small number of wildcats established the founding population around human settlements, the allele frequencies in this subpopulation differed from the source population simply by chance. If the founding group contained $N_f$ individuals drawn from a population with allele frequency $p$, the expected variance in allele frequency is:

\[\text{Var}(p_f) = \frac{p(1-p)}{2N_f}\]

With a founding population of perhaps $N_f = 20\text{--}50$ individuals, the variance is substantial, leading to random fixation or loss of alleles unrelated to selection.

Genetic Drift in Small Populations

The probability that a neutral allele with current frequency $p$ eventually reaches fixation in a population of effective size $N_e$ is simply:

\[P(\text{fixation}) = p\]

The expected time to fixation (given it occurs) is:

\[\bar{t}_{\text{fix}} = -\frac{4N_e(1-p)}{p} \ln(1-p)\]

In small populations associated with early agricultural villages ($N_e \sim 50\text{--}200$), drift operates rapidly, potentially fixing coat colour variants and behavioural alleles (e.g., reduced fear of humans) within hundreds of generations.

Relaxed Selection

In the commensal environment, predation pressure on cats was reduced and food was abundant (mice). Traits under strong purifying selection in the wild — such as cryptic coloration, extreme neophobia, and solitary behaviour — experienced relaxed selection. This allowed previously deleterious variants (non-agouti coat, increased tameness) to persist and spread. Molecular signatures of selection on the domestic cat genome include regions near genes for neural crest cell development (linked to the “domestication syndrome”).

2. Feline Phylogenetics

The family Felidae comprises 37 extant species arranged in 8 lineages that diverged from a common ancestor approximately 11 million years ago (MYA) in Asia (Johnson et al., 2006; Li et al., 2016).

Molecular Clock Analysis

Divergence times are estimated using molecular clock methods applied to mitochondrial (cytochrome b, ND5) and nuclear gene sequences. The molecular clock assumes that mutations accumulate at an approximately constant rate $\mu$ per site per year. The divergence time between two lineages is:

\[T = \frac{d}{2\mu}\]

where $d$ is the observed genetic distance (substitutions per site) and the factor of 2 accounts for divergence along both lineages since their common ancestor. For feline cytochrome b, the calibrated rate is approximately$\mu \approx 2\text{--}4 \times 10^{-8}$ substitutions/site/year.

Coalescent Theory

Modern phylogenetic inference uses coalescent theory to account for incomplete lineage sorting (ILS) — the phenomenon where gene trees differ from the species tree due to ancestral polymorphism. For two gene copies in a population of effective size $N_e$, the expected coalescence time is:

\[E[T_{\text{MRCA}}] = 2N_e \text{ generations}\]

The probability that two lineages fail to coalesce before a speciation event (causing ILS) depends on the ratio of population size to divergence time. For rapid radiations (such as the felid diversification ~8–10 MYA), ILS is common. For $n$ gene copies:

\[P(\text{no coalescence in } t \text{ gens}) = \exp\!\left(-\frac{\binom{n}{2}t}{N_e}\right)\]

The Eight Felid Lineages

Panthera Lineage (7 spp)

Divergence: ~10.8 MYA

Lion, tiger, leopard, jaguar, snow leopard

Bay Cat Lineage (3 spp)

Divergence: ~9.4 MYA

Bay cat, Asian golden cat, marbled cat

Caracal Lineage (3 spp)

Divergence: ~8.5 MYA

Caracal, African golden cat, serval

Ocelot Lineage (8 spp)

Divergence: ~8.0 MYA

Ocelot, margay, oncilla, and 5 others

Lynx Lineage (4 spp)

Divergence: ~7.2 MYA

Eurasian, Canada, Iberian lynx, bobcat

Puma Lineage (3 spp)

Divergence: ~6.7 MYA

Puma, jaguarundi, cheetah

Leopard Cat Lineage (5 spp)

Divergence: ~5.9 MYA

Leopard cat, fishing cat, Pallas cat, and 7 others

Felis Lineage (5 spp)

Divergence: ~3.4 MYA

Wildcat, sand cat, black-footed cat, jungle cat, Chinese mountain cat

The domestic cat belongs to the Felis lineage, the most recently diverged group. Within this lineage, F. catus and F. s. lybica are so closely related that many taxonomists consider the domestic cat a subspecies rather than a separate species. The genetic distance between them is only ~0.2% at the nuclear level.

3. Population Genetics of Breeds

There are approximately 73 recognised cat breeds, but most were established less than 150 years ago during the Victorian cat fancy movement. This is dramatically different from dog breeds, many of which have centuries-old histories tied to specific working functions.

Heterozygosity Decay Under Drift

When a breed is founded from a small number of individuals and maintained as a closed population, heterozygosity declines each generation due to genetic drift. The expected heterozygosity after $t$ generations is:

\[H_t = H_0 \left(1 - \frac{1}{2N_e}\right)^t\]

where $H_0$ is the initial heterozygosity and $N_e$ is the effective population size. The half-life of heterozygosity (time to lose 50%) is:

\[t_{1/2} = \frac{\ln 2}{\ln\!\left(\frac{2N_e}{2N_e - 1}\right)} \approx 2N_e \ln 2 \approx 1.39 N_e\]

Breed Bottlenecks

Many cat breeds experienced extreme genetic bottlenecks at foundation:

Persian

Founder population estimated at ~50 individuals. Effective population size$N_e \approx 30\text{--}40$. High prevalence of polycystic kidney disease (PKD1, autosomal dominant, ~38% carrier frequency in some populations). Single C→A transversion in exon 29 of PKD1.

Maine Coon

North American breed with moderate genetic diversity. However, hypertrophic cardiomyopathy (HCM) affects ~30% due to a single missense mutation (A31P) in the MYBPC3 gene (myosin-binding protein C).

Siamese

Originally imported from Thailand (Siam) in the 1880s. Modern show Siamese have diverged significantly from the traditional type. Prone to amyloidosis (hepatic), progressive retinal atrophy, and the c^s tyrosinase allele (Module 6) is fixed in the breed.

Scottish Fold

All descended from a single farm cat (Susie, 1961, Scotland). The fold ear is caused by a dominant mutation in TRPV4 (previously mapped toFd). Homozygotes (Fd/Fd) develop severe osteochondrodysplasia — a strong argument against Fd/Fd breeding.

Inbreeding Coefficient

The inbreeding coefficient $F$ measures the probability that two alleles at a locus are identical by descent:

\[F_t = 1 - \left(1 - \frac{1}{2N_e}\right)^t \approx 1 - e^{-t/(2N_e)}\]

For a breed with $N_e = 40$ maintained for 50 generations (~100 years with 2-year generation interval): $F_{50} \approx 1 - e^{-50/80} \approx 0.47$. This extreme inbreeding coefficient (compared to $F < 0.05$ in outbred populations) explains the high prevalence of recessive genetic diseases in pedigreed cats.

4. The Feline Genome

The domestic cat genome was first sequenced in 2007 (Pontius et al.) from an Abyssinian cat named Cinnamon. Subsequent assemblies have refined the picture:

2.7 Gb

Genome size

19,493

Protein-coding genes

Chromosomes (2n)

~60%

Repetitive elements

Comparative Genomics

Cat

2n = 38

2.7 Gb

~19,500 genes

Dog

2n = 78

2.4 Gb

~19,300 genes

Human

2n = 46

3.1 Gb

~20,400 genes

Notable Gene Losses

Several pseudogenizations reveal the molecular basis of distinctly feline traits:

• TAS1R2 (sweet taste receptor): A 247-bp microdeletion renders this gene non-functional. Cats cannot taste sweet — they are the only known mammalian order with a universally pseudogenised sweet receptor. This is consistent with their obligate carnivore ecology: no selective advantage to detecting sugars.
• GCKR (glucokinase regulatory protein): Loss of this hepatic enzyme regulator contributes to the cat's inability to efficiently metabolise carbohydrates. Feline hepatocytes have constitutively active glucokinase, driving continuous gluconeogenesis (Module 4).
• SLC6A18 (renal amino acid transporter): Pseudogenised in cats, potentially explaining their high protein requirement and susceptibility to amino acid imbalances.

Notable Gene Expansions

• CYP2A6 (cytochrome P450 family): Expanded in cats, providing enhanced detoxification of plant alkaloids. This compensates for their reduced UDP-glucuronosyltransferase activity and helps explain why cats are sensitive to some drugs (paracetamol) but resistant to others.
• Vomeronasal receptor genes (V1R): Cats retain ~30 functional V1R genes (vs ~5 in humans), consistent with their reliance on pheromone detection via the vomeronasal organ (Module 5).
• Olfactory receptor genes: ~800 functional OR genes (~3% of the genome), supporting their excellent sense of smell.

5. Phylogenetic Tree of Felidae

The phylogeny below shows the 8 major felid lineages with approximate divergence times, based on Johnson et al. (2006) and Li et al. (2016).

6. Simulations

Population Genetics & Phylogenetic Analysis

This simulation produces three panels: (1) heterozygosity decay under genetic drift for different effective population sizes, simulating breed formation, (2) stochastic simulation of allele frequency drift in a small breed population, and (3) a phylogenetic distance matrix heatmap for the 8 felid lineages.

Python

script.py178 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

np.random.seed(42)

# ================================================================
# Panel 1: Heterozygosity Decay H_t = H_0 (1 - 1/(2Ne))^t
# ================================================================
generations = np.arange(0, 201)
H0 = 0.65  # initial heterozygosity (typical mixed-breed cat)

Ne_values = [20, 50, 100, 200, 500, 2000]
colors_ne = ['#ef4444', '#f97316', '#fbbf24', '#22c55e', '#60a5fa', '#a78bfa']
labels_ne = [f'$N_e$ = {n}' for n in Ne_values]

H_curves = []
for Ne in Ne_values:
    H = H0 * (1 - 1/(2*Ne))**generations
    H_curves.append(H)

# ================================================================
# Panel 2: Stochastic Drift Simulation (Wright-Fisher)
# ================================================================
Ne_drift = 40  # typical breed effective size
n_alleles = 2 * Ne_drift  # diploid
p0 = 0.5  # initial frequency
n_sims = 20
n_gen = 150

drift_trajectories = []
for _ in range(n_sims):
    freq = p0
    traj = [freq]
    for g in range(n_gen):
        n_copies = np.random.binomial(n_alleles, freq)
        freq = n_copies / n_alleles
        traj.append(freq)
        if freq == 0 or freq == 1:
            traj.extend([freq] * (n_gen - g - 1))
            break
    drift_trajectories.append(traj[:n_gen+1])

# ================================================================
# Panel 3: Phylogenetic Distance Matrix (cytochrome b divergence)
# ================================================================
lineages = ['Panthera', 'Bay Cat', 'Caracal', 'Ocelot', 'Lynx', 'Puma', 'Leopard Cat', 'Felis']
# Approximate divergence times (MYA) from common ancestor
div_times = [10.8, 9.4, 8.5, 8.0, 7.2, 6.7, 5.9, 3.4]

# Distance matrix: pairwise divergence ~ 2 * max(div_time_i, div_time_j) / 11
# More sophisticated: use actual estimated pairwise times
n_lin = len(lineages)
dist_matrix = np.zeros((n_lin, n_lin))
for i in range(n_lin):
    for j in range(n_lin):
        if i == j:
            dist_matrix[i, j] = 0
        else:
            # Pairwise divergence time is approximately the split from their MRCA
            # For lineages branching off main trunk, this is max of the two divergence times
            dist_matrix[i, j] = max(div_times[i], div_times[j])

# Convert to genetic distance (substitutions/site) using molecular clock
mu = 3e-8  # substitutions/site/year for cytochrome b
gen_dist = dist_matrix * 2 * mu * 1e6  # factor of 1e6 for MYA

# ================================================================
# Plot
# ================================================================
fig = plt.figure(figsize=(16, 6))
fig.patch.set_facecolor('#0a0a1a')
gs = gridspec.GridSpec(1, 3, wspace=0.35, width_ratios=[1, 1, 1])

# Panel 1: Heterozygosity decay
ax1 = fig.add_subplot(gs[0])
ax1.set_facecolor('#0a0a1a')
for H, c, lab in zip(H_curves, colors_ne, labels_ne):
    ax1.plot(generations, H, color=c, linewidth=1.8, label=lab, alpha=0.9)

ax1.axhline(y=H0/2, color='#9ca3af', linestyle=':', linewidth=0.8, alpha=0.5)
ax1.text(150, H0/2 + 0.015, '$H_0/2$', color='#9ca3af', fontsize=8)

ax1.set_title('Heterozygosity Decay by Breed Size', color='#fbbf24',
              fontsize=11, fontweight='bold', pad=10)
ax1.set_xlabel('Generations', color='#9ca3af', fontsize=9)
ax1.set_ylabel('Heterozygosity $H_t$', color='#9ca3af', fontsize=9)
ax1.set_xlim(0, 200)
ax1.set_ylim(0, 0.7)
ax1.legend(fontsize=7, loc='upper right', facecolor='#1a1a2e',
           edgecolor='#334155', labelcolor='#d1d5db', ncol=2)
ax1.tick_params(colors='#6b7280', labelsize=8)
ax1.spines['bottom'].set_color('#334155')
ax1.spines['left'].set_color('#334155')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.grid(True, alpha=0.15, color='#475569')

# Breed annotations
ax1.annotate('Persian/Siamese\n($N_e$ ~ 30-50)', xy=(100, 0.24), fontsize=7,
             color='#f97316', ha='center',
             bbox=dict(boxstyle='round,pad=0.3', facecolor='#0a0a1a',
                       edgecolor='#f97316', alpha=0.8))
ax1.annotate('Random-bred\n($N_e$ ~ 500+)', xy=(100, 0.58), fontsize=7,
             color='#60a5fa', ha='center',
             bbox=dict(boxstyle='round,pad=0.3', facecolor='#0a0a1a',
                       edgecolor='#60a5fa', alpha=0.8))

# Panel 2: Drift trajectories
ax2 = fig.add_subplot(gs[1])
ax2.set_facecolor('#0a0a1a')
for i, traj in enumerate(drift_trajectories):
    gens = np.arange(len(traj))
    alpha = 0.5 if traj[-1] not in [0, 1] else 0.8
    color = '#ef4444' if traj[-1] == 0 else '#22c55e' if traj[-1] == 1 else '#fbbf24'
    ax2.plot(gens, traj, color=color, linewidth=0.8, alpha=alpha)

ax2.axhline(y=0.5, color='#9ca3af', linestyle=':', linewidth=0.8, alpha=0.4)
ax2.set_title(f'Genetic Drift ($N_e$ = {Ne_drift})', color='#fbbf24',
              fontsize=11, fontweight='bold', pad=10)
ax2.set_xlabel('Generations', color='#9ca3af', fontsize=9)
ax2.set_ylabel('Allele frequency $p$', color='#9ca3af', fontsize=9)
ax2.set_xlim(0, n_gen)
ax2.set_ylim(-0.05, 1.05)
ax2.tick_params(colors='#6b7280', labelsize=8)
ax2.spines['bottom'].set_color('#334155')
ax2.spines['left'].set_color('#334155')
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.grid(True, alpha=0.15, color='#475569')

# Count fixed/lost
n_fixed = sum(1 for t in drift_trajectories if t[-1] == 1.0)
n_lost = sum(1 for t in drift_trajectories if t[-1] == 0.0)
n_poly = n_sims - n_fixed - n_lost
ax2.text(5, 0.95, f'Fixed: {n_fixed}', color='#22c55e', fontsize=8, fontweight='bold')
ax2.text(5, 0.88, f'Lost: {n_lost}', color='#ef4444', fontsize=8, fontweight='bold')
ax2.text(5, 0.81, f'Polymorphic: {n_poly}', color='#fbbf24', fontsize=8, fontweight='bold')

# Panel 3: Phylogenetic distance heatmap
ax3 = fig.add_subplot(gs[2])
ax3.set_facecolor('#0a0a1a')

im = ax3.imshow(gen_dist, cmap='inferno', aspect='auto', origin='lower')
ax3.set_xticks(range(n_lin))
ax3.set_yticks(range(n_lin))
ax3.set_xticklabels(lineages, rotation=45, ha='right', fontsize=7, color='#d1d5db')
ax3.set_yticklabels(lineages, fontsize=7, color='#d1d5db')
ax3.set_title('Felidae Genetic Distance\n(cyt b, subs/site)', color='#fbbf24',
              fontsize=11, fontweight='bold', pad=10)

cbar = plt.colorbar(im, ax=ax3, fraction=0.046, pad=0.04)
cbar.ax.tick_params(colors='#6b7280', labelsize=7)
cbar.set_label('Genetic distance', color='#9ca3af', fontsize=8)

# Add distance values in cells
for i in range(n_lin):
    for j in range(n_lin):
        val = gen_dist[i, j]
        text_color = 'white' if val < 0.4 else 'black'
        ax3.text(j, i, f'{val:.2f}', ha='center', va='center',
                 fontsize=5, color=text_color, fontweight='bold')

fig.suptitle('Feline Evolution: Breed Genetics & Felidae Phylogenetics',
             color='#f59e0b', fontsize=14, fontweight='bold', y=0.99)

plt.savefig('output.png', dpi=150, bbox_inches='tight',
            facecolor='#0a0a1a', edgecolor='none')
print("Simulation complete.")
print("Left: Heterozygosity decay - breeds with small Ne lose diversity rapidly.")
print("  Persian (Ne~40): loses 50% heterozygosity in ~55 generations (~110 years).")
print("Center: Wright-Fisher drift simulation showing allele fixation/loss.")
print("  In small breed populations (Ne=40), most alleles fix or are lost within 150 gens.")
print("Right: Genetic distance heatmap for the 8 felid lineages.")
print("  Felis lineage (domestic cat) is most recently diverged from common ancestor.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

References

Driscoll, C. A. et al. (2007). “The Near Eastern origin of cat domestication.” Science, 317(5837), 519–523.
Vigne, J.-D. et al. (2004). “Early taming of the cat in Cyprus.” Science, 304(5668), 259.
Johnson, W. E. et al. (2006). “The Late Miocene radiation of modern Felidae: A genetic assessment.” Science, 311(5757), 73–77.
Li, G., Davis, B. W., Eizirik, E., & Murphy, W. J. (2016). “Phylogenomic evidence for ancient hybridization in the genomes of living cats (Felidae).” Genome Research, 26(1), 1–11.
Hu, Y. et al. (2014). “Earliest evidence for commensal processes of cat domestication.” Proceedings of the National Academy of Sciences, 111(1), 116–120.
Pontius, J. U. et al. (2007). “Initial sequence and comparative analysis of the cat genome.” Genome Research, 17(11), 1675–1689.
Lyons, L. A. (2010). “Feline genetics: Clinical applications and genetic testing.” Topics in Companion Animal Medicine, 25(4), 203–212.
Li, X. et al. (2005). “Pseudogenization of a sweet-receptor gene accounts for cats' indifference toward sugar.” PLoS Genetics, 1(1), e3.
Montague, M. J. et al. (2014). “Comparative analysis of the domestic cat genome reveals genetic signatures underlying feline biology and domestication.” Proceedings of the National Academy of Sciences, 111(48), 17230–17235.
Gandolfi, B. et al. (2013). “A splice variant in KIT is associated with the Dominant White and White Spotting phenotypes in the domestic cat.” Animal Genetics, 44(5), 586–603.
Meurs, K. M. et al. (2005). “A cardiac myosin binding protein C mutation in the Maine Coon cat with familial hypertrophic cardiomyopathy.” Human Molecular Genetics, 14(23), 3587–3593.

← Module 7: Neuroscience & Behavior Back to Course Overview →