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CHAPTER 12
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Genetics
of Mood Disorders
Wade Berrettini, M.D., Ph.D.
In this chapter I summarize some recent advances in genetics of bipolar
disorder and recurrent unipolar disorder (RUP). Emphasis is placed on genome-wide
association study (GWAS) reports that use a bipolar disorder or RUP diagnosis as the
phenotype; these diagnoses may be from a direct semistructured interview, review of
medical records, or simple self-report (e.g., an answer to the question “Have you ever
been depressed?”). Copy number variants (CNVs) influencing risk for mood disorders are also summarized. This chapter also stresses shared genetic liability across
nosological categories. Although future pharmacogenetic studies of bipolar disorder
and RUP will identify alleles that predict a good therapeutic response to specific
pharmacotherapies, this review does not cover pharmacogenetics of mood disorders.
Because independent confirmation has been uncommon for candidate gene association studies not based on previous genome-wide significant data, reviews of such
reports have been omitted; reviews of linkage reports have also been omitted. Finally,
this chapter does not include a review of epigenetic studies of mood disorders.
General Comments on Genome-Wide
Association Studies
GWAS research has been reported since approximately 2005, made possible through
technological advances allowing determination of genotypes for common singlenucleotide polymorphisms (SNPs), the great majority of which have two alleles, differing by a single base pair. The less common allele for these biallelic SNPs in a specified population will have an allele frequency of less than 50%. There are several
million common SNPs (minor allele frequency >1%) across the human genome, or
one common SNP for every 500 or so base pairs of DNA. Modern GWAS technology
205
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involves determining genotypes very inexpensively by direct genotyping at approximately 1 million common SNPs for all participants, typically more than 10,000 individuals. Furthermore, alleles for other common (not genotyped) SNPs adjacent to the
directly genotyped SNPs can be inferred through a process of imputation (Das et al.
2018), which uses DNA sequence data for large numbers of people to infer the miss
ing alleles.
GWAS research involves testing the statistical significance of allele frequency differences between case subjects and control subjects at these millions of common
SNPs. Because there is a multiple hypothesis testing problem, a statistical adjustment
must be used for testing each SNP for a case-control difference in allele frequency. A
value of P≈5×10
–8
is the equivalent of P=0.05 for a GWAS experiment on a single phenotype. The stringent statistical correction for multiple testing no doubt masks true
signals from alleles that confer only very modest risk of disease, although in theory
this limitation could be overcome by ever-larger sample sizes. GWAS results for psy
chiatric disorders have not uncovered single common alleles that convey risk greater
than an odds ratio (OR) of 1.3. The absolute value of the odds ratio is a measure of the
magnitude of risk conveyed by the relevant allele. The sample size required to
achieve adequate power for the modest expected effect sizes (OR ≤1.3), given the correction for a genome-wide search, is approximately 10,000 case subjects and 10,000
control subjects.
Heritability (h
ited factors. For bipolar disorder, h
sibling study as approximately 55% (Pettersson et al. 2019). For RUP, h
2
) may be defined as that fraction of total risk attributable to inher-
2
has been estimated recently from a large Swedish
2
has been estimated from this sibling study as ~30% (Pettersson et al. 2019). These sibling estimates
agree well with data from twin studies (Polderman et al. 2015). GWAS reports on bi
polar disorder and RUP estimate that SNP h
GWAS) explains approximately 20%–35% of total h
al. 2013; Pettersson et
al. 2019) (Table 12–1).
If common SNPs explain approximately 20%–35% of total h
RUP, then what factors might account for the missing h
maining h
2
is probably explained by rare variants of large effect and epistasis (the mul-
2
(h2 explained by all common SNPs in a
2
for these mood disorders (Lee et
2
2
for bipolar disorder and
(Manolio et al. 2009)? The re-
tiplicative risk-increasing effect due to interaction among two or more risk alleles).
-
-
-
Bipolar Disorder Genome-Wide
Association Studies
A recent sibling bipolar disorder h2 estimate of approximately 50% from several million Swedish siblings (Pettersson et al. 2019) agrees well with data from twin studies
(Polderman et al. 2015). These and several previous h
ronmental and stochastic influences play substantial roles in risk for bipolar disorder.
The initial bipolar disorder GWAS reports were seriously underpowered, in retrospect, to detect alleles of small effect, conveying odds ratios of less than1.3. Baum et
al. (2008), Sklar et al. (2008), and the Wellcome Trust Case Control Consortium (2007)
each consisted of fewer than 2,000 case subjects. It is noteworthy that the strongest association signals (e.g., the “top 10”) in each of these three studies were not confirmed
2
estimates indicate that envi-
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TABLE 12–1. Heritability (h2) and single-nucleotide polymorphism (SNP)
heritability
Disorder h
Bipolar disorder 55% 20%
Recurrent unipolar disorder 30% 10%
Source. Data from Pettersson et al. 2019.
2
SNP h
2
in either of the other two, suggesting that the individual sample sizes lacked sufficient power to detect small effects.
This sample size limitation was partially addressed by a collaborative effort including 4,387 case subjects and 6,209 control subjects, which were analyzed jointly using
the SNP genotypes from several bipolar disorder GWAS reports (Ferreira et al. 2008).
The strongest statistical signal was in the ankyrin 3 (ANK3) gene on chromosome
10q21, with single SNP and haplotype (rs4582919—rs10994357—rs7910492, risk hap
lotype CCT) analyses yielding a P of approximately 10
–9
. This statistical level indicates
genome-wide significance. The 10q21 ANK3 location coincides with a previous report
of bipolar disorder genetic susceptibility occurring at this location on chromosome 10
(Segurado et al. 2003). A meta-analysis (Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011) of bipolar disorder GWAS data revealed evidence for risk
alleles in ANK3, CACNA1c, SYNE1, and ODZ4. Confirmation of these alleles in addi
tional samples has been published (e.g., Green et al. 2013).
Stahl et al. (2019) and Mullins et al. (2021) reported on substantial numbers of bipolar disorder case subjects and control subjects of European ancestry (Figure 12–1). With
64 loci of genome-wide significance, the findings of Mullins et al. (2021) provided a
wealth of statistically sound data. The loci included ion channels and neurotransmitter transporters (CACNA1C, GRIN2A, SCN2A, SLC4A1), as well as synaptic proteins
(RIMS1, ANK3). In keeping with findings for other psychiatric disorders, the odds ra
tios were less than 1.2 for each common allele, consistent with hundreds of bipolar
disorder risk alleles.
-
-
-
Shared Genetic Susceptibility of Bipolar Disorder
With Other Psychiatric Disorders
Twin and family studies indicate that bipolar disorder and RUP share genetic susceptibility alleles (e.g., Gershon et al. 1982; Weissman et al. 1984). Family studies also indicate shared susceptibility for bipolar disorder and schizophrenia (Lichtenstein et al.
2009), as well as shared susceptibility for RUP and schizophrenia (Kendler and Gardner 1997; Lichtenstein et al. 2009). Because all common risk-increasing alleles for psychiatric disorders have small effect sizes (OR ≤1.3), very large sample sizes must be
ascertained and studied to detect them at genome-wide levels of significance (5×10
Thus, there are large numbers of putative risk alleles with odds ratios between 1.01
and 1.05 that remain undetected. One method of assembling such common alleles of
small effect in a single statistic is termed a polygenic risk score (PRS).
–8
).
208 The APA Publishing Textbook of Mood Disorders, Second Edition
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FIGURE 12–1. Bipolar disorder genome-wide association study (GWAS) meta-analysis reveals many risk loci.
To view this figure in color, see Plate 4 in Color Gallery in middle of book.
Manhattan plot for the primary GWAS meta-analysis of 41,917 bipolar disorder case subjects and 371,549
control subjects (Mullins et al. 2021). GWAS –log
phisms (SNPs) across chromosomes 1–22. The red line shows the genome-wide significance threshold
(P<5×10
lar disorder and yellow for novel associations from this study.
Source. Reprinted from Figure 1 in Mullins N, Forstner AJ, O’Connell KS, et al.: “Genome-Wide Association Study of More Than 40,000 Bipolar Disorder Cases Provides New Insights Into the Underlying Biology.” Nature Genetics 53(6):817–829, 2021. PMCID: PMC8192451.
–8
). SNPs in genome-wide significant loci are colored green for loci previously associated with bipo-
(P) values are plotted for all single-nucleotide polymor-
10
A PRS is constructed in the following manner. All alleles with nominal statistical
significance in a case-control GWAS (e.g., with a cutoff P<0.01) are assembled and
weighted by their individual odds ratios into a single PRS, simply a large number of
putative risk alleles. In practice, investigators often use P values at five or more levels
(e.g., P<0.1, 0.05, 0.01, 0.001, and 0.0001), and the number of alleles can vary between
~100,000 at P<0.1 and ~1,000 at P<0.0001. Most of these alleles are false positives, but
some unknown fraction are true positives.
The validity of the PRS can be established by comparing the degree of overlap (percentage of all alleles shared) among genotyped individuals with the disorder (who were
not part of the original studies defining the PRS) with that of GWAS-genotyped persons
without the disorder. For example, a PRS for bipolar disorder should show a higher degree of overlap than would be expected randomly when studying a PRS derived from
individuals with bipolar disorder or RUP. Conversely, there should be only randomly
expected overlap (approximately 5%) when a bipolar disorder PRS is compared with a
PRS derived from inflammatory bowel disease or some other unrelated illness.
Using a PRS for RUP and schizophrenia, Stahl et al. (2019) and Mullins et al. (2021)
confirmed earlier reports (Athanasiu et al. 2010; Lee et al. 2013; Schizophrenia Psychiatric Genome-Wide Association Study Consortium 2011) of substantial overlap in
risk alleles among persons carrying a diagnosis of schizophrenia, bipolar disorder, or
RUP (Table 12–2). These findings are consistent with the family study reports noted
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TABLE 12–2. Genetic correlations (rg) among schizophrenia, recurrent unipolar
disorder, and bipolar disorder
Disorders r
Schizophrenia–bipolar disorder 0.70
Schizophrenia–recurrent unipolar disorder 0.35
Bipolar disorder–recurrent unipolar disorder 0.34
Source. Data from Stahl et al. 2019.
g
earlier. Given the evidence for overlap in genetic susceptibility for bipolar disorder,
RUP, and schizophrenia, bivariate analyses of GWAS reports were hypothesized to
reveal novel loci for the shared risk alleles. This indeed was the case, as reported by
Amare et al. (2020), confirming the hypothesis that these three nosological categories
share a fraction of risk alleles.
The genetic origins of cognitive impairment in schizophrenia remain undefined.
To study this issue, Smeland et al. (2019) conducted conditional genetic analyses of
assembled GWAS data for general intelligence (n =~270,000), schizophrenia
(n=~82,000), and bipolar disorder (n=~52,000). They identified 75 distinct genomic
loci associated with both schizophrenia and intelligence and 12 loci associated with
both bipolar disorder and intelligence. Of these 87 loci, 20 were novel for schizophrenia
and 4 were novel for bipolar disorder. The great majority of the 75 alleles associated
with both schizophrenia and intelligence conveyed risk for cognitive impairment. In
contrast, 9 of the 12 alleles associated with both bipolar disorder and intelligence were
associated with improved cognition. These data suggest that cognitive impairment in
schizophrenia is due at least in part to the cognitive effects of some schizophrenia risk
alleles.
Conclusions Regarding Bipolar Disorder
The bipolar disorders are a substantially heritable group of disorders, as indicated by
results from GWAS reports that confirm the h
(Pettersson et al. 2019; Polderman et al. 2015). There is a distinct absence of common
alleles conveying even moderate risk for bipolar disorder, because nearly all have
odds ratios of less than 1.2, consistent with a polygenic inheritance, in which hundreds of common alleles increase risk for bipolar disorder. These susceptibility alleles
conveying modest amounts of risk require tens of thousands of case subjects and control subjects to achieve adequate power to detect them. In this scenario, it is difficult
to imagine that widespread genetic testing for bipolar disorder in symptomatic individuals will become common and useful in establishing diagnosis or predicting
course of illness or response to medications. Whole-genome sequencing is not likely
to improve on these results for common alleles but may identify rare alleles.
A substantial challenge exists in translating the genetic data into novel treatments
for bipolar disorder. Even in Mendelian (single-gene) diseases, this has proved very
difficult. For success in this area, it may be necessary to define subgroups of bipolar
2
indicated by family and twin studies
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disorder that have specific environmental contributions, such as in utero viral infections or early childhood adversity.
Genome-Wide Association Studies of Recurrent
Unipolar Disorder
The h2 of RUP has been estimated by twin, sibling, and family studies across the past
75 years, with a recent sibling h
Swedish sibling pairs (Pettersson et al. 2019), an estimate that agrees well with twin
studies (Polderman et al. 2015). RUP SNP h
son et al. 2019). These and several previous estimates indicate that environmental and
stochastic factors play substantial roles in RUP risk.
Several small RUP GWAS reports were the first to be published, including Muglia
et al. (2010), Shyn et al. (2011), Sullivan et al. (2009), and Wray et al. (2012). There were
no genome-wide significant results, even in the largest study, which included 2,431
case subjects and 3,673 screened control subjects (Wray et al. 2012). In a study of 5,303
Han Chinese women with RUP and 5,337 screened control subjects, using wholegenome sequencing, two genome-wide significant associations were identified, and
were subsequently replicated in an independent Han Chinese sample (CONVERGE
Consortium 2015). The two loci contributing to risk for RUP were located on chromosome 10: one near the SIRT1 gene (P=2.53×10
LHPP gene (P=6.45×10
A self-reported diagnosis of depression as the phenotype was used in a large RUP
GWAS that reported 15 associated loci (N=459,481; 121,380 case subjects and 338,101
control subjects) from patrons of a commercial genetics company (Hyde et al. 2016). A
United Kingdom Biobank GWAS identified 17 variants associated across three definitions of RUP (maximum N=322,580; 113,769 case subjects and 208,811 control subjects). These three RUP phenotypes included a “broad” definition (RUP based on selfreported help-seeking for problems with nerves, anxiety, tension, or depression), a
probable RUP phenotype (based on self-reported depressive symptoms with associated impairment), and definite RUP (defined by hospital records) (Howard et al.
2018a). A meta-analysis of 35 cohorts (130,664 RUP case subjects and 330,470 control
subjects) reported 44 genome-wide significant loci, using several RUP phenotypes
(Wray et al. 2018).
Howard et al. (2019) reported the largest RUP GWAS meta-analysis to date:
807,553 individuals (246,363 case subjects and 561,190 control subjects) from the three
largest GWAS reports of RUP. They described 102 independent variants, 269 genes,
and 15 gene sets associated with depression, including genes and gene pathways involving synaptic structure and neurotransmission. In an independent replication
sample of 1,306,354 individuals (414,055 case subjects and 892,299 control subjects),
87 of the 102 associated variants
ure 12–2). An enrichment analysis provided specific evidence of the importance of
prefrontal brain regions (Howard et al. 2018b).
–12
2
estimate of approximately 35% from several million
2
is ~10%, or one-third of RUP h2 (Petters-
–10
) and the other in an intron of the
).
were significant after multiple
testing correction
(Fig
-
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FIGURE 12–2. Manhattan plot of 87 loci for major depressive disorder in a genome-wide
association study (GWAS) meta-analysis.
To view this figure in color, see Plate 5 in Color Gallery in middle of book.
Manhattan plot of the observed –log
meta-analysis (see text description). Variants are positioned according to the Genome Reference Consortium Human Build 37 (GRCh37) assembly.
Source. Reprinted from Figure 1 in Howard DM, Adams MJ, Clarke TK, et al.: “Genome-Wide MetaAnalysis of Depression Identifies 102 Independent Variants and Highlights the Importance of the Prefron
tal Brain Regions.” Nature Neuroscience 22(3):343–352, 2019. PMCID: PMC6522363.
P values of each variant for an association with depression in the
10
Copy Number Variants in Recurrent Unipolar
Disorder and Bipolar Disorder
CNVs are often defined as deletions, duplications, or insertions larger than 1,000 base
pairs. They may be inherited or arise de novo in the germ cell, in the zygote, or relatively early in neuronal development. In the latter case, the CNV may be somatic and
not represented in the germline. Early studies of CNVs were focused on relatively
large deletions, insertions, and duplications involving more than 100,000 DNA base
pairs. This was partially due to the detection limit in first-generation “tiling arrays”
and the relative paucity of genotyped SNPs (~100,000) in these early studies, because
the ability to call relatively smaller CNVs with high accuracy was limited. Genomewide CNV reports have revealed that rare (frequencies <1%) inherited or de novo
large CNVs occur in people with idiopathic developmental delay/intellectual disability, autism spectrum disorder, and schizophrenia at a twofold higher rate than in
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