5D Library › Early Experience, Epigenetic Methylation and the Limits of Intergenerational Transmission · 中文
Chapter 1 Is Trauma “Written into DNA”? Animal Experiments and Human Evidence
First, some definitions
Epigenetic marks are molecular marks that do not change the DNA sequence but can affect whether genes are expressed, and that can be maintained through cell division. The most studied is DNA methylation: the addition of a methyl group to certain cytosines in DNA, usually at CpG dinucleotides. More methylation in a promoter region is often, though not always, associated with lower expression of the gene.
So the phrase “experience written into DNA” is inaccurate from the start: what experience may change is the chemical marking on DNA, not the DNA “text” itself. The real question is whether such marks can cross the germ cells and reach descendants who never went through the experience.
Calculation
Intergenerational and transgenerational. Call the exposed individual F0. Exposed along with the body are the germ cells inside it that will form later generations. An effect seen only in directly exposed generations is intergenerational; only an effect in the first unexposed generation or later could be transgenerational epigenetic inheritance. The second part of the program is a toy model with purely illustrative parameters: a mark in the germ cells must escape two waves of reprogramming in every generation, whereas a behavioural channel (maternal care → pup → the pup’s own care as an adult) is rebuilt in every generation.
# Which generations are DIRECTLY exposed, and which is the first that is not?
# The exposed individual is F0; germ cells are exposed with the body carrying
# them. Part 2 is a toy model with ILLUSTRATIVE numbers: a germline mark must
# escape two waves of reprogramming per generation, whereas a behavioural
# channel (maternal care -> pup -> her own care) is rebuilt every generation.
EXPOSED = {"adult male": ["F0", "F1 (germ cells)"],
"non-pregnant female": ["F0", "F1 (germ cells)"],
"pregnant female": ["F0", "F1 (fetus)", "F2 (germ cells)"]}
for case, gens in EXPOSED.items():
print(f"{case:19s}: exposed {', '.join(gens)}; first unexposed F{len(gens)}")
esc = 0.10 * 0.50 # escape both waves (illustrative)
fidelity = 0.70 # behavioural channel: share kept per generation
print("share of an induced signal left (illustrative)")
print(" generation | germline mark, escape 0.10 x 0.50 | behavioural channel 0.70")
for g in range(1, 5):
print(f" F{g} | {esc ** g:33.6f} | {fidelity ** g:24.3f}")
# adult male : exposed F0, F1 (germ cells); first unexposed F2
# non-pregnant female: exposed F0, F1 (germ cells); first unexposed F2
# pregnant female : exposed F0, F1 (fetus), F2 (germ cells); first unexposed F3
# share of an induced signal left (illustrative)
# generation | germline mark, escape 0.10 x 0.50 | behavioural channel 0.70
# F1 | 0.050000 | 0.700
# F2 | 0.002500 | 0.490
# F3 | 0.000125 | 0.343
# F4 | 0.000006 | 0.240The first part is a matter of definition, not of data: when a pregnant mother is exposed, the first unexposed generation is F3; when a father or a non-pregnant mother is exposed, it is F2. “Transgenerational = F3 and later” holds only for exposure during pregnancy. The second part only illustrates a structure. If a germline mark had a 5 % chance per generation of escaping reprogramming, about one in ten thousand would be left by F3, whereas a behavioural channel that keeps 70 % of its signal per generation would still hold about a third. Both numbers are assumptions, but the structure is real: a behavioural channel need not escape reprogramming, because it is rebuilt in every generation.
Reprogramming: why marks are hard to pass on
Evidence
Two waves of reprogramming. Mammalian development includes two large-scale waves of epigenetic reprogramming: one in the primordial germ cells, where most methylation is erased and then re-established, and one in the early embryo after fertilisation, where the genomes from both parents again undergo widespread demethylation and remethylation (Reik et al. 2001). A few regions, such as imprinted genes, escape part of the erasure, but these are exactly the exceptions. Reviews conclude that true germline transgenerational epigenetic inheritance is rare in mammals and that many claimed examples can be explained by other mechanisms (Heard and Martienssen 2014).
Maternal licking and grooming
Evidence
Care and the stress system. Meaney’s laboratory observed stable individual differences in how often rat mothers lick and groom (LG) their pups and nurse them in an arched-back posture. As adults, the offspring of high-LG mothers had higher expression of glucocorticoid receptors in the hippocampus, more sensitive glucocorticoid negative feedback and more modest hypothalamic–pituitary–adrenal responses to acute stress (Liu et al. 1997).
Methylation. Weaver and colleagues found that this difference is associated with DNA methylation of the exon 17 promoter of the hippocampal glucocorticoid-receptor gene (rat Nr3c1): the offspring of low-LG mothers had more methylation in this promoter, especially at a CpG site within the binding site of the transcription factor NGFI-A. The difference emerged in the first week of life and persisted into adulthood. Cross-fostering (giving the pups of low-LG mothers to high-LG mothers and vice versa) made the methylation pattern follow the rearing mother, not the birth mother; and infusing a histone deacetylase inhibitor into the brains of adult animals reversed the differences in methylation and stress responses (Weaver et al. 2004).
This is one of the most influential studies in epigenetics, because it links early care to a chemical mark on a specific regulatory region of a specific gene. But what it implies for “passing it on” is the opposite of the popular version.
Evidence
Non-genomic transmission. Francis and colleagues used cross-fostering to test the transmission of the style of care itself: female pups born to low-LG mothers but reared by high-LG mothers became high-LG mothers themselves, and vice versa. The authors called this “non-genomic” transmission across generations (Francis et al. 1999).
Calculation
A simulated cross-fostering experiment. Each female’s LG score is standardised; under the two hypotheses, a daughter’s adult LG is set by her rearing mother or by her birth mother (coefficient 0.6, illustrative). Half the pups are reared by their own mother and half by an unrelated mother. Looking only at the cross-fostered daughters: whom does she resemble?
# A simulated cross-fostering experiment in the style of Francis et al. (1999).
# Daughter's adult licking-and-grooming score (LG, standardised) =
# 0.6 x LG of her REARING mother + noise (behavioural channel), or
# 0.6 x LG of her BIRTH mother + noise (germline channel).
# Half the pups are swapped to an unrelated mother. Numbers illustrative.
import random, statistics as st
random.seed(22)
def experiment(channel, n=400, k=0.6):
bio = [random.gauss(0, 1) for _ in range(n)]
rear = list(bio)
idx = list(range(n // 2, n)) # second half: swapped mothers
for i, j in zip(idx, idx[1:] + idx[:1]): rear[i] = bio[j]
src = rear if channel == "behavioural" else bio
dau = [k * s + random.gauss(0, (1 - k * k) ** 0.5) for s in src]
f = slice(n // 2, n)
return st.correlation(bio[f], dau[f]), st.correlation(rear[f], dau[f])
print("cross-fostered daughters only (200 per hypothesis)")
print(" hypothesis | r with biological mother | r with foster mother")
for ch in ("behavioural", "germline"):
rb, rr = experiment(ch)
print(f" {ch:11s} | {rb:24.2f} | {rr:20.2f}")
# cross-fostered daughters only (200 per hypothesis)
# hypothesis | r with biological mother | r with foster mother
# behavioural | 0.07 | 0.63
# germline | 0.65 | -0.07If the style of care is transmitted through behaviour, the cross-fostered daughters correlate about 0.63 with their foster mothers and hardly at all (0.07) with their birth mothers; if it is transmitted through the germline, the pattern is reversed. Francis and colleagues saw the first. So in this most classic of models, what passes “from one generation to the next” is a way of caring, rewritten in each generation through the mother’s actual behaviour towards her pups. That is an important pointer for humans, but not a proof: nursing care in rats is a long way from the complexity of human parenting.
Evidence
A corresponding finding in human brains. McGowan and colleagues compared post-mortem hippocampal tissue from people who died by suicide: those with a history of childhood abuse (12) had more methylation of the exon 1F promoter of the glucocorticoid-receptor gene NR3C1 (corresponding to the rat 17) and lower receptor expression than suicide victims without such a history (12) and controls who died suddenly of other causes (12) (McGowan et al. 2009). This is a small, cross-sectional post-mortem study of an association in the people who were abused; it says nothing about transmission to offspring.
Human observational studies
Evidence
The Dutch famine. In the winter of 1944–45 a famine struck the western Netherlands under the Nazi blockade. Heijmans and colleagues compared 60 people exposed to the famine around the time of conception with their unexposed same-sex siblings: six decades later, the exposed had on average about 5.2 % lower methylation of the differentially methylated region of the insulin-like growth factor gene IGF2 (0.488 versus 0.515); among 62 people exposed only late in gestation there was no significant difference (Heijmans et al. 2008).
This is important evidence of an association between the early nutritional environment and long-term methylation, and sibling controls partly account for family and genetic background. But its subjects are the F1 who experienced the famine directly in the womb: this is intrauterine exposure (often called fetal programming), not transgenerational inheritance.
Evidence
Offspring of Holocaust survivors. Yehuda and colleagues measured methylation in a region of intron 7 of the FKBP5 gene in 32 Holocaust survivors and 22 adult offspring, and in a smaller group of Jewish control families: the survivors’ methylation was about 10 % higher than that of control parents, while the survivors’ offspring had about 7.7 % lower methylation than control offspring (Yehuda et al. 2016).
This study was widely reported as evidence that “trauma is inherited”, but it has several key limitations. The samples are small, especially the controls; only one region of one gene was examined; offspring and parents differ in opposite directions, which does not fit the image of a mark handed down unchanged; and, most importantly, the offspring developed in their mothers’ wombs, were raised by survivor parents and grew up within the family’s stories and atmosphere. The design cannot separate the intrauterine environment, postnatal rearing and the germline.
Evidence
A critical review. Horsthemke points out that true transgenerational epigenetic inheritance means the transmission of epigenetic information through the germline; after exposure of a pregnant female, F3 and later must be studied, and after exposure of a male, F2; chromatin remodelling during germ-cell differentiation and after fertilisation is the main barrier to transmission; unlike laboratory animals, in humans ecological and cultural inheritance cannot be ruled out, while genetic differences must be; and a nearby genetic variant can cause a “secondary epimutation” that looks heritable. His conclusion is that the evidence for transgenerational epigenetic inheritance in humans is “not (yet) conclusive” (Horsthemke 2018).
Calculation
Why do small methylation studies especially need replication? Simulate 20 “exposed offspring” and 10 controls, with Welch’s t-test at 20 CpG sites. Case 1: no true difference at any site. Case 2: one site has a true difference of 0.3 standard deviations. Sample sizes and effect are illustrative.
# Why small human methylation studies need replication. Simulated studies
# compare "exposed offspring" (n = 20) with controls (n = 10) at 20 CpG sites,
# using Welch's t-test. Case 1: no true difference anywhere. Case 2: one site
# has a true difference of 0.3 SD. Sizes and effect are illustrative.
import math, random, statistics as st
random.seed(7)
def betacf(a, b, x): # continued fraction, incomplete beta function
qab, qap, qam, c, d = a + b, a + 1, a - 1, 1.0, 1 - (a + b) * x / (a + 1)
d = 1 / d; h = d
for m in range(1, 200):
m2 = 2 * m
for aa in (m * (b - m) * x / ((qam + m2) * (a + m2)),
-(a + m) * (qab + m) * x / ((a + m2) * (qap + m2))):
d = 1 / (1 + aa * d); c = 1 + aa / c; h *= d * c
return h
def p_two_sided(t, df): # P(|T| > |t|), Student's t with df d.o.f.
x = df / (df + t * t)
lb = math.lgamma(df / 2 + 0.5) - math.lgamma(df / 2) - math.lgamma(0.5)
bt = math.exp(lb + (df / 2) * math.log(x) + 0.5 * math.log(1 - x))
if x < (df / 2 + 1) / (df / 2 + 1.5):
return bt * betacf(df / 2, 0.5, x) / (df / 2)
return 1 - bt * betacf(0.5, df / 2, 1 - x) / 0.5
def welch(x, y):
vx, vy = st.variance(x) / len(x), st.variance(y) / len(y)
t = (st.fmean(x) - st.fmean(y)) / math.sqrt(vx + vy)
df = (vx + vy) ** 2 / (vx ** 2 / (len(x) - 1) + vy ** 2 / (len(y) - 1))
sp = math.sqrt((st.variance(x) + st.variance(y)) / 2)
return p_two_sided(t, df), (st.fmean(x) - st.fmean(y)) / sp
def study(true_d, n1=20, n0=10, sites=20):
res = []
for s in range(sites):
shift = true_d if s == 0 else 0.0
res.append(welch([random.gauss(shift, 1) for _ in range(n1)],
[random.gauss(0, 1) for _ in range(n0)]))
return res
R = 1000
null = [study(0.0) for _ in range(R)]
share = sum(any(p < .05 for p, _ in s) for s in null) / R
print(f"no true effect: studies with at least one site p < .05: {share:.0%}")
true = [study(0.3) for _ in range(R)]
hits = [d for s in true for (p, d) in s[:1] if p < .05]
print(f"true 0.3 SD at one site: power at that site {len(hits) / R:.0%};")
print(f" mean estimated effect when significant {st.fmean(hits):.2f} SD")
big = sum(study(0.3, 200, 100, 1)[0][0] < .05 for _ in range(500)) / 500
print(f"with n = 200 vs 100 the same site is detected in {big:.0%} of studies")
# no true effect: studies with at least one site p < .05: 65%
# true 0.3 SD at one site: power at that site 12%;
# mean estimated effect when significant 0.91 SD
# with n = 200 vs 100 the same site is detected in 66% of studiesEven with no true difference at any site, about 65 % of studies find at one site or more; a real difference of 0.3 standard deviations is detected only about 12 % of the time, and when detected its estimate averages about 0.91 standard deviations, three times the true value. With 200 against 100 participants, detection rises to about 66 %. This does not mean the studies above are false positives. It means that a “significant” methylation difference from a single small study cannot be treated as a conclusion until an independent, larger sample replicates it.
Where, then, does resemblance between generations come from?
Taken together, the evidence of this chapter gives a more accurate picture than the popular claim:
- in mammals, germline transgenerational epigenetic inheritance is strongly limited by two waves of reprogramming, and in humans the evidence is very thin;
- the better evidence concerns intrauterine exposure: maternal nutrition and stress during pregnancy can be associated with long-term biological differences in offspring, which is an intergenerational effect;
- larger sources of resemblance between family members are genetic inheritance (Chapters 2 and 3) and the postnatal social-learning environment (Chapters 4 to 6). The latter is precisely what can change.
Note
Do not treat methylation as destiny. Methylation is dynamic and varies with tissue, age and environment; methylation measured in blood or saliva does not necessarily reflect the state of the brain; and commercial “epigenetic age” or “trauma imprint” tests cannot be used to judge any person or family. The studies in this chapter show population-level associations and cannot be used to infer what a particular child has “inherited”.
Chapter summary
Methylation changes marks on DNA, not its sequence. In the rat studies the resemblance in styles of care follows the rearing mother, not the birth mother, and is rebuilt through behaviour each generation; the Dutch famine is evidence of intrauterine exposure, and the Holocaust-offspring study is small, shows opposite directions and cannot separate pathways; human transgenerational epigenetic inheritance is not established. The first unexposed generation is F3 after exposure in pregnancy and F2 after exposure of a father or a non-pregnant mother. The next chapter turns to a larger source of resemblance: genes.
References
Fan, Y. (2026). Early Experience, Epigenetic Methylation and the Limits of Intergenerational Transmission: From Glucocorticoid-Receptor Programming and Twin Variance Decomposition to Social-Learning Channels, Read Beside Classical Chinese Family Instruction [早期经验、表观遗传甲基化与跨代行为先验的传递边界] (Version 1.0.0). 5D Library. https://doi.org/10.5281/zenodo.23761337 · Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).