T1D susceptibility loci identified to date.
The prevalence of diabetes is increasing worldwide and to date it impacts the lives of approximately 200 million people (Steyn et al., 2009). It is estimated that by 2030, there will be 439 million adults affected by diabetes (International Diabetes Federation/diabetes prevalence: www.idf.org). Type 1 diabetes (T1D) represents approximately 10% of these patients and is most prevalent in populations of European ancestry, where there is ample evidence of increased annual incidence during the past five decades (Onkamo et al., 1999; EURODIAB ACE Study Group, 2000).
T1D is a complex trait that results from the interplay between environmental and genetic factors. Much evidence supports a strong genetic component associated with T1D. The epidemiological data showing differences in geographic prevalence is one clear indicator, with populations of European ancestry having the highest presentation rate. T1D has high concordance among monozygotic twins (33 to 42%) (Redondo et al., 2001) and runs strongly in families with sibling risk being approximately 10 times greater than in the general population (Clayton, 2009); this is in clear contrast to the “less genetic” type 2 diabetes, where the sibling risk ratio is relatively modest at 3.5 (Rich, 1990).
T1D develops at all ages and occurs through the autoimmune destruction of pancreatic β-cells with resulting lack of insulin production. The immune system participates in β-cell destruction through several of its components including natural killer (NK) cells, B lymphocytes, macrophages, dendritic cells (DC), and antigen-presenting cells (APCs). Studies in human and animal models have shown that both innate and adaptive immune responses participate in disease pathogenesis, possibly reflecting the multifactorial nature of this autoimmune disorder.
In this review, we provide an update on genome-wide association studies (GWAS) discoveries to date and discuss the latest associated regions added to the growing repertoire of gene networks predisposing to T1D.
Historically, prior to GWAS, only six loci had been fully established to be associated with T1D. The human leukocyte antigen (HLA) region on chromosome 6p21 was the first known candidate to be strongly associated with T1D in 1970s (Singal & Blajchman, 1973; Nerup et al., 1974; Cudworth & Woodrow, 1975). This cluster of homologous cell-surface proteins is divided into class I (A, B, C) and class II (DP, DQ, RD). The HLA genes encode highly polymorphic proteins, which are essential in self versus non-self immune recognition. The class I molecules are ubiquitously expressed and present intracellular antigen to CD8+ T cells. Class II molecules are expressed mainly on professional APCs: DCs, macrophages, B-lymphocytes and thymus epithelium. Class II molecules are composed of A and B chains, and present antigens to CD4+ T cells, which promote inflammation by secreting cytokines upon recognition of their specific targets. Approximately half of the genetic risk for T1D is conferred by the genomic region harboring the HLA class II genes primarily HLA-DRB1, -DQA1 and -DQB1 genes). In 1984, insulin (INS) gene encoded on chromosome 11p15 was identified as second loci linked with T1D (Bell et al., 1984). In 1996, the cytotoxic T-lymphocyte-associated protein 4 (CTLA4) gene encoded on chromosome 2q33 was recognized as third loci (Nistico et al., 1996). In 2004, a protein tyrosine phosphatase, non-receptor type 22 (PTPN22) gene encoded on chromosome 1p13, was found to be associated with susceptibility to T1D in another case-control study (Bottini et al., 2004). Vella et al., 2005 reported interleukin 2 receptor alpha (IL2RA) gene as fifth T1D loci on chromosome 10p15. In 2006, Smyth et al. identified the interferon-induced with helicase C domain 1 (IFIH1) gene on chromosome 2q24.3 as the sixth gene to be strongly associated with T1D.
The advent of GWAS in the mid-2000s has changed the situation dramatically, increasing the pace and efficiency of discovery for the T1D associated loci, by a factor of ten. The critical platform for this work was laid by the HapMap project (International HapMap Consortium, 2003, 2005). The GWAS approach has been made possible by the development of high-density genotyping arrays. The genome is laid out in discrete linkage disequilibrium (LD) blocks with limited haplotype diversity within each of these blocks. Therefore, a minimal set of single nucleotide polymorphisms (SNPs) can detect almost all common haplotypes present, thus improving genotyping accuracy and reducing cost.
The first full-scale GWAS for T1D were published in 2007 by our group (Hakonarson et al., 2007) and The Wellcome Trust Case-Control Consortium (WTCCC, 2007). We examined a large pediatric cohort of European descent using the Illumina HumanHap 550 BeadChip platform. The design involved 561 cases, 1,143 controls and 467 triads in the discovery stage, followed by a replication effort in 939 nuclear families. In addition to finding the “usual” suspects, including an impressive 392 SNPs capturing the very strong association across the major histocompatibility complex (MHC), we identified significant association with variation at the KIAA0350 gene, which we replicated in an additional cohort. The WTCCC study investigated seven common complex diseases including T1D by genotyping 2,000 cases and 3,000 controls with ~500,000 SNPs using the Affymetrix GeneChip, and reported a number of novel T1D loci, including the KIAA0350 genomic region (WTCCC, 2007). Todd et al., 2007 confirmed these findings, using 4,000 cases, 5,000 controls and 3,000 T1D families as well as association reported in the WTCCC study to the 12q13 region. In a separate effort we fast-tracked 24 SNPs at 23 distinct loci from our original study and established association to the 12q13 region with a combined P-value of 9.13x10-10 (Hakonarson et al., 2008); this was the same locus as reported by the WTCCC and Todd et al., 2007. The 12q13 region harbors several genes, including ERBB3, RAB5B, SUOX, RPS26 and CDK2. However, the causative variants at this locus remain unknown. Concannon et al., 2008 reported an association between SNP at the UBASH3A locus on 21q22.3 and T1D by using SNP genotyping data from a linkage study of affected sib pairs in nearly 2,500 multiplex families, a finding also corroborated by our efforts as well as association to the BACH2 gene (Grant et al., 2009).
In order to get the most from GWAS and to increase the statistical power, several independent research groups carried out meta-analyses using datasets from different investigative groups. Cooper et al., 2008 performed the first meta-analysis by using T1D datasets from the WTCCC, 2007 and the Genetics of Kidneys in Diabetes (GoKind) study (Mueller et al, 2006; Manolio et al., 2007), and confirmed associations for PTPN22, CTLA4, MHC, IL2RA, 12q13, 12q24, CLEC16A and PTPN2. The SNPs with lowest nominal P-values were taken forward for further genotyping in an additional British cohort of 6,000 cases, 7,000 controls and 2,800 families. As a result, the IL2-IL21 association strengthened further and they found strong evidence for four additional loci: BACH2; a 10p15 region harboring the protein kinase C, theta gene (PRKCQ); a 15q24 region harboring nine genes including cathepsin H (CTSH) and a 22q13 region harboring tumor necrosis factor related protein 6 (C1QTNF6). Additional studies are required to elucidate the culprit genes and their mechanism at the 15q24 and 22q13 loci.
Barrett et al., 2009 meta-analysis uncovered in excess of 40 loci, including 18 novel regions, plus they confirmed a number of previously reported (Smyth et al., 2008; Fung et al., 2009; Cooper et al., 2009). The study included samples from WTCCC, 20070, the GoKind study (Mueller et al., 2006) and controls and family sets from Type 1 Diabetes Genetics Consortium (T1DGC). The meta-analysis observed association to 1q32.1 (which harbors the immunoregulatory interleukin genes IL10, IL19 and IL20), 9p24.2 contains only Glis family zinc finger protein 3 (GLIS3; first suggested by us in Grant et al., 2009), 12p13.31 which harbors a number of immunoregulatory genes including CD69 and 16p11.2 harboring IL27. These findings were further supported by our in silico replication efforts (Qu et al., 2010).
To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ~2.54 million genotyped and imputed SNPs in a combined cohort of 9,934 cases and 16,956 controls (Bradfield et al., 2011). Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome wide significance. The most significantly associated SNP (rs539514, P = 5.66x10-11) resided in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50x10-9) resided in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however the region of linkage disequilibrium is approximately 800kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06x10-9) was in an intergenic region on 6q27, where the region of association is approximately 900kb and harbors additional genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP and PCD2. These latest associations add to the growing repertoire of gene networks predisposing to T1D. Table 1 summarizes all T1D associated loci reported to date.
\n\t\t\t\tReference\n\t\t\t | \n\t\t\t\n\t\t\t\tSample Size\n\t\t\t | \n\t\t\tReplication Sample Size | \n\t\t\t\n\t\t\t\tEthnic Group\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy Type\n\t\t\t | \n\t\t\t\n\t\t\t\tMain Findings\n\t\t\t | \n\t\t
Hakonarson et al., 2007 | \n\t\t\t467 trios, 561 cases, 1,143 controls | \n\t\t\t2,350 individuals in 549 families; 390 trios | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS | \n\t\t\tHLA-DRB1, HLA-DQA2, CLEC16A, INS, PTPN22 | \n\t\t
WTCCC 2007 | \n\t\t\t1,963 cases, 2,938 controls | \n\t\t\tsee Todd et al., 2007 | \n\t\t\tEuropean, British | \n\t\t\tGWAS | \n\t\t\tHLA-DRB1, INS, CTLA4, PTPN22, IL2RA, IFIH1, PPARG, KCNJ11, TCF7L2 | \n\t\t
Todd et al., 2007 | \n\t\t\tsee WTCCC 2007 | \n\t\t\t2997 trios, 4,000 cases, 5,000 controls | \n\t\t\tEuropean British | \n\t\t\tGWAS | \n\t\t\tPHTF1-PTPN22, ERBB3, CLEC16A, C12orf30 | \n\t\t
Hakonarson et al., 2008 | \n\t\t\t467 trios, 561 cases, 1,143 controls | \n\t\t\t549 families, 364 trios | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS | \n\t\t\tSUOX - IKZF4 | \n\t\t
Concannon et al., 2008 | \n\t\t\t2,496 families | \n\t\t\t2,214 trios, 7,721 cases, 9,679 controls | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS | \n\t\t\tINS, IFIH1, CLEC16A, UBASH3A | \n\t\t
Cooper et al., 2008 | \n\t\t\t3,561 cases, 4,646 controls | \n\t\t\t6,225 cases, 6,946 controls, 3,064 trios | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS meta-analysis | \n\t\t\tPTPN22, CTLA4, HLA, IL2RA, ERRB3, C12orf30, CLEC16A, PTPN2 | \n\t\t
Grant et al., 2009 | \n\t\t\t563 cases, 1,146 controls, 483 case-parents trios | \n\t\t\t636 families, 3,303 cases, 4,673 controls | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS | \n\t\t\tEDG7, BACH2, GLIS3, UBASH3A, RASGRP1 | \n\t\t
Awata et al., 2009 | \n\t\t\t735 cases, 621 controls | \n\t\t\t- | \n\t\t\tJapanese | \n\t\t\tTaqMan genotyping | \n\t\t\tERBB3, CLEC16A | \n\t\t
Zoledziewska et al., 2009 | \n\t\t\t1037 cases, 1706 controls | \n\t\t\t- | \n\t\t\tEuropean, Sardinian | \n\t\t\tTaqMan genotyping | \n\t\t\tCLEC16A | \n\t\t
Fung et al., 2009 | \n\t\t\t8010 cases, 9733 controls | \n\t\t\t- | \n\t\t\tEuropean, British | \n\t\t\tTaqMan genotyping | \n\t\t\tSTAT4, STAT3, ERAP1, TNFAIP3, KIF5A/PIP4K2C | \n\t\t
Wu et al., 2009 | \n\t\t\t205 cases, 422 controls | \n\t\t\t- | \n\t\t\tHan Chinese | \n\t\t\tTaqMan genotyping | \n\t\t\tCLEC16A | \n\t\t
Barrett et al., 2009 | \n\t\t\t7,514 cases, 9,045 controls | \n\t\t\t4,267 cases, 4,670 controls, 4,342 trios | \n\t\t\tEuropean | \n\t\t\tGWAS meta-analysis | \n\t\t\tMHC, PTPN22, INS, C10orf59, SH2B3, ERBB3, CLEC16A, CTLA4, PTPN2, IL2RA, IL27, C6orf173, IL2, ORMDL3, GLIS3, CD69, IL10, IFIH1, UBASH3A, COBL, BACH2, CTSH, PRKCQ, C1QTNF6, PGM1 | \n\t\t
Wallace et al., 2010 | \n\t\t\t7,514 cases, 9,045 controls | \n\t\t\t4,840 cases, 2,670 controls, 4,152 trios | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS meta-analysis | \n\t\t\tDLK1, TYK2 | \n\t\t
Wang et al., 2010 | \n\t\t\t989 cases, 6197 controls | \n\t\t\t- | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS | \n\t\t\tPTPN22, IL10, IFIH1, KIAA0746, BACH2, C6orf173, TAGAP, GLIS3, L2R, INS, ERBB3, C14orf181, IL27, PRKD2, HERC2, CLEC16A, IFNG, IL26, | \n\t\t
Reddy et al., 2011 | \n\t\t\t1434 cases, 1864 controls | \n\t\t\t- | \n\t\t\tEuropean ancestry, southeast USA | \n\t\t\tTaqMan genotyping | \n\t\t\tPTPN22, INS, IFIH1, SH2B3, ERBB3, CTLA4, C14orf181, CTSH, CLEC16A, CD69, ITPR3, C6orf173, SKAP2, PRKCQ, RNLS, IL27, SIRPG, CTRB2 | \n\t\t
Bradfield et al., 2011 | \n\t\t\t9,934 cases, 16,956 controls | \n\t\t\t1,120 trios | \n\t\t\tEuropean ancestry | \n\t\t\tGWAS meta-analysis | \n\t\t\tLMO7, EFR3B, 6q27, TNFRSF11B, LOC100128081, FOSL2 | \n\t\t
Asad et al., 2012 | \n\t\t\t424 families, 3078 cases, 1363 controls | \n\t\t\t- | \n\t\t\tEuropean, Scandinavians | \n\t\t\tGenotyping and sequencing | \n\t\t\tHTR1A, RFN180 | \n\t\t
Huang et al., 2012 | \n\t\t\t16,179 individuals | \n\t\t\t- | \n\t\t\tEuropean ancestry | \n\t\t\tGenomes-based imputation | \n\t\t\tCUX2, IL2RA | \n\t\t
T1D susceptibility loci identified to date.
The immune system is well organized and well regulated with a basic function of protecting the host against pathogens. This places the immune system in a vital position between healthy and diseased states of the host. Its protective task is regulated by a complex regulatory mechanism involving a diverse army of cells and molecules of humoral and cellular factors working in concert to protect the body against invaders. The human immune system has two components: innate and adaptive. Innate immunity is comprised of physical, chemical, and microbiological barriers to the entry of antigen, and the elements of immune system (DC, macrophages, mast cells, NK cells, neutrophils, monocytes, complements, cytokines and acute phase proteins), which provide immediate host defense. Adaptive immunity is the hallmark of the immune system of higher animals with T and B cells as the key cellular players that provide more specific life-long immunity.
In T1D this system breaks down: insulin-producing β-cells are subjected to specific attack by the host immune system. To better understand the etiology of T1D, a plethora of research has been done to link the systematic destruction of β-cells and the role of the immune system. Linkage studies in 1970s revealed MHC as the first key contributor to T1D susceptibility. Further linkage analysis and candidate gene association studies revealed additional loci associated with T1D. Starting in 2007, GWAS have increased the number of loci be associated with T1D to almost 60. In Figure 1 we present 59 T1D susceptibility loci as where we have classified them into loci harboring non-immune (14) vs. immune (45) genes. Functional aspects of some genes are discussed below.
Immune and Non-immune T1D genes are depicted in a concept map representing the components of the immune system. The discovery of T1D susceptibility genes started as early as 1974 with just six genes identified by 2006 shown in red. The advent of GWAS led to flurry of novel genes associated with T1D reaching the excess of 40 by 2009 and almost 60 by 2012.
The complex crosstalk between innate and adaptive immune cells has major impact on the pathogenesis and development of T1D as illustrated in Figure 2. The initiation phase (Phase I) of T1D development takes place in the pancreas where conventional dendritic cells (cDCs) capture and process β-cell antigens. Apoptosis (‘natural cell death’) or viral infection can lead to β-cell death. Antiviral responses are mediated by invariant natural killer T (iNKT) cells; crossplay between iNKT and plasmacytoid DCs (pDCs) controls viral replication thus prevents subsequent inflammation, tissue damage, and downregulating T1D pathogenesis. Migration of activated cDCs to the draining lymph node primes pathogenic islet antigen-specific T cells. This activation is promoted by macrophages through IL12 secretion. B cells present β-cell antigen to diabetogenic T cells and secrete autoantibodies in response. The activation of islet antigen-specific T cells can be inhibited by cDCs through engagement of programmed cell death ligand 1 (PDL1). The expansion phase (Phase II): iNKT cells can further promote the recruitment of tolerogenic cDCs and pDCs. These DCs promote expansion of regulatory T (TReg) cells through the production of indoleamine 2,3-dioxygenase (IDO), IL10, transforming growth factor-β (TGFβ) and inducible T cell co-stimulator ligand (ICOSL). Phase III: In the pancreas, β-cell can be killed by diabetogenic T cells and NK cells through the release of interferon-γ (IFNγ), granzymes and perforin, as well as by macrophages through the production of tumour necrosis factor (TNF), IL-1β and nitric oxide (NO). IL12 produced by cDCs sustains the effector functions of activated diabetogenic T cells and NK cells. TReg cells that inhibit diabetogenic T cells and innate immune cells through IL10 and TGFβ can prevent β-cell damage. Tolerogenic pDCs stimulated by iNKT cells could also control diabetogenic T cells through IDO production. Lastly, β-cells can inhibit diabetogenic T cells by expressing PDL1 and escape the cell death.
Pathogenesis model of T1D involves complex interactions between innate and adaptive immune cell types.
The C-type lectin domain family 16, member A (CLEC16A) gene encodes protein with C-type lectin domain structure, which makes it potentially related to the immune response (Robinson et al., 2006). It is established that C-type lectins function both as adhesion and pathogen recognition receptors (PPRs) (Cambi & Figdor, 2003). In addition, CLEC16A is almost exclusively expressed in immune cells including DCs, B-lymphocytes and NK cells. Our 2007 GWAS in a large pediatric cohort of European descent identified CLEC16A as a novel T1D susceptibility gene within a 233-kb linkage disequilibrium block on chromosome 16p13. Three common non-coding variants of the CLEC16A gene (rs2903692, rs725613 and rs17673553) reached genome-wide significance for association with T1D (Hakonarson et al., 2007). Subsequent replication studies in an independent cohort confirmed the association. Importantly, the allele of CLEC16A linked to protection from T1D was also associated with higher levels of CLEC16A expression in NK cells (Hakonarson et al., 2007).
The 2007 WTCCC study independently discovered CLEC16A (formally known as KIAA0350) as a T1D susceptibility locus associated with the non-coding variant rs12708716. This finding was confirmed immediately for T1D in populations of European descent (Todd et al., 2007, Cooper et al., 2008). To date, several SNPs (rs2903692, rs17673553, rs725613, rs12708716, rs12921922, rs12931878) within the CLEC16A gene have been reported to be associated with T1D in several populations: Sardinian (Zoledziewska et al., 2009), Spanish (Martinez et al., 2010), southeast USA (Reddy et al., 2011), Chinese (Wu et al., 2009; Sang et al., 2012), and Japanese (Yamashita et al., 2011). Recently CLEC16A was also associated with adult-onset of autoimmune diabetes (Howson et al., 2011).
Several GWAS in different autoimmune diseases such as multiple sclerosis (MS) (Zuvich, 2011; Nischwitz et al., 2011), Addison’s disease (Skinningsrud et al., 2008), systemic lupus erythematosus (SLE) (Gateva et al., 2009; Zhang et al., 2011), Celiac disease (Dubois et al., 2010), Crohn’s disease (Márquez et al., 2009), selective immunoglobulin A deficiency (Jagielska et al., 2012), alopecia areata (Jagielska et al., 2012), rheumatoid arthritis (Martinez et al., 2010) and primary biliary cirrhosis (Mells et al., 2011; Hirschfield et al., 2012) also demonstrated association of the 16p13 loci with disease risk, implying that the 16p13 region contains a key regulator of the self-reactive immune response.
Recently, Davison et al., 2012 reported intron 19 of the CLEC16A gene behaves as a regulatory sequence, which affects the expression of a neighboring gene dexamethasone-induced (DEXI). While it is clear that intron 19 of CLEC16A is highly enriched for transcription-factor-binding events, more functional studies are needed to advance from GWAS to candidate causal genes and their biological functions.
To find causal variant of CLEC16A gene we sequenced the 16p13 region in 96 T1D patients and found 10 new non-synonymous SNPs resulting in one stop-codon, two splice site mutations, and 7 amino acid changes (unpublished data). The studies are under way to examine if these changes are correlated with CLEC16A expression and if these SPNs are present in control group.
Little is yet proven about CLEC16A functions. Kim et al., 2010 characterized ema as an endosomal membrane protein is required for endosomal trafficking and promotes endosomal maturation. Expression of human orthologue of ema ‘CLEC16A’ rescued the Drosophila mutant demonstrating conserved function of the protein. A recent study by the same group also reported its requirement for the growth of autophagosomes and proposed that the Golgi is a membrane source for autophagosomal growth, and that ema facilitates this process (Kim et al., 2012). Expression of CLEC16A rescued the autophagosome size defect in the ema mutant, suggesting that regulation of autophagosome morphogenesis may be one of the fundamental functions of CLEC16A. Another recent study elucidated the dynamic expression changes and localization of CLEC16A in lipopolysaccharide (LPS) induced neuroinflammatory processes in adult rats. CLEC16A expression was strongly induced in active astrocytes in inflamed cerebral cortex. In vitro studies indicated that the up-regulation of CLEC16A might be involved in astrocyte activation following LPS challenge (Wu et al., 2012).
In our latest effort to identify additional genetic loci for T1D, we examined associations in the largest meta-analysis to date between T1D and ~2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three novel loci associated with T1D that reached genome-wide significance (Bradfield et al., 2011).
LMO7 (13q22): The most significantly associated SNP (rs539514, P = 5.66x10-11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22 (Bradfield et al., 2011). LMO7 is a multi-domain mammalian protein with a calponin homology (CH) domain, a discs-large homologous regions (DHR) domain, and a LIM domain. Proteins of this family are involved in protein-protein interactions, regulation of cell adhesion and signaling (Ooshio et al., 2004; Yamada et al., 2004). The expression of LMO7 is cell type specific (Furuya et al., 2002; Kang et al., 2000; Lindvall et al., 2005; Bradfield et al., 2011; Rozenblum et al., 2002; Sasaki et al., 2003) and is essential for the development of muscle and heart tissues. Mice with homozygous deletions of LMO7 display retinal, muscular, and growth retardation (Semenova et al., 2003). LMO7 is upregulated in multiple cancers, especially at the metastatic stage; however under normal conditions its expression is low and limited to very few tissues (Furuya et al., 2002; Kang et al., 2000; Sasaki et al., 2003; Perou et al., 2000). In cultured rat ascites hepatoma cells, the upregulation of LMO7 correlates with the ability of transforming growth factor β (TGFβ) to enhance the invasiveness of these cells (Nakamura et al., 2005). Recent GWAS meta-analysis by Bradfield et al., 2011 identified LMO7 association with T1D. Although the function of LMO7 does not clearly relate to the etiology of T1D, LMO7 is expressed in pancreatic islets and thus is a possible biological candidate at this locus (Kutlu et al., 2009).
EFR3B (2q23): The second most significantly associated SNP among the new loci (rs478222, P=3.50x10-9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. EFR3B is an 817 amino acid protein that exists as three alternatively spliced isoforms and belongs to the EFR3 family. The gene encoding EFR3B maps to human chromosome 2p23.3. A number of genetic diseases have been linked to genes on chromosome 2 including Harlequin icthyosis, lipid metabolic disorder sitosterolemia, and Alstrom syndrome. Our recent study shows novel association of 2q23 locus with T1D risk. Location of SNP rs478222 in the intronic region of EFR3B gene makes it a good candidate, however the 2q23 region harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A.
Nuclear receptor coactivator 1 (NCOA1) protein is a member of the p160/steroid receptor coactivator (SRC) family. The product of this gene binds to a variety of nuclear hormone receptors in a ligand-dependent manner, suggesting that NCOA1 may play a role as a bridging molecule between nuclear hormone receptors and general transcription factors (Onate et al., 1995; Torchia et al., 1997).
C2orf79 is peptidyl-tRNA hydrolase domain containing 1 (PTRHD1) predicted protein with unknown function.
Centromere protein O (CENPO) gene encodes a component of the interphase centromere complex. The protein is localized to the centromere throughout cell division and is required for bipolar spindle assembly, chromosome segregation and checkpoint signaling during mitosis (Okada et al., 2006).
Adenylate cyclase 3 (ADCY3) gene encodes a membrane-associated enzyme. This protein catalyzes the formation of the secondary messenger cyclic adenosine monophosphate (cAMP) and is highly expressed in human placenta, testis, ovary, and colon (Ludwig & Seuwen, 2002). Wong et al., 2000 reported the presence of adenylyl cyclase 2, 3, and 4 in olfactory cilia. ADCY3 mutants failed olfaction-based behavioral tests indicating that ADCY3 and cAMP signaling are critical for olfactory-dependent behavior.
DnaJ/Hsp40 homolog, subfamily C, member 27 (DNAJC27) gene encodes 273 amino acid protein with RAB-like GTPase and DNAJ domains. EST database suggests high expression in nervous system and reproductive organs (Nepomuceno-Silva et al., 2004).
Pro-opiomelanocortin (POMC) gene encodes a polypeptide hormone precursor protein synthesized mainly in corticotroph cells of the anterior pituitary. POMC is essential for normal steroidogenesis and maintenance of adrenal weight. Mutations in this gene have been associated with early onset obesity, adrenal insufficiency, and red hair pigmentation (Krude et al., 1998; Hung et al., 2012).
DNA (cytosine-5)-methyltransferase 3 alpha (DNMT3A) gene encodes a protein that functions as a de-novo methyltransferase that can methylate unmethylated and hemimethylated DNA with equal efficiencies (Yanagisawa et al., 2002).
Additional fine gene mapping and functional studies are needed to determine causal variants for 2q23 region and their role in T1D.
Intergenic region 6q27: Intergenic region on 6q27 contained the third most significantly associated SNP (rs924043, P=8.06x10-9) in our recent study (Bradfield et al., 2011). The region of association is approximately 900kb and harbors multiple genes including PHF10, TCTE3, DLL1, FAM120B, PSMB1, TBP, and PDCD2. The 6q27 region also includes several genes of unknown function: C6orf208/LINC00574 (long intergenic non-protein coding RNA 574), T-complex-associated-testis-expressed 3 (TCTE3), LOC154449, WD repeat domain 27 (WDR27) and chromosome 6 open reading frame 120 (C6orf120).
Plant Homeo Domain (PHD) finger protein 10 (PHF10) encodes a subunit of an ATP-dependent chromatin-remodeling complex that functions in neural precursor cells (Yoo et al., 2009).
Delta-like 1-Drosophila (DLL1) is a human homolog of the Notch Delta ligand and a member of the delta/serrate/jagged family. It plays a role in mediating cell fate decisions during hematopoiesis and cell communication (Santos et al., 2007; Dontje et al., 2006). The protein is expressed in heart, pancreas and brain. Su et al., 2006 reported pancreatic regeneration in chronic pancreatitis requires activation of the notch-signaling pathway.
Family with sequence similarity 120B (FAM120B) gene encodes protein belonging to the constitutive coactivator of peroxisome proliferator-activated receptor gamma (PPARG) family. FAM120B functions in adipogenesis through PPARG activation in a ligand-independent manner (Li et al., 2007).
Proteasome (prosome, macropain) subunit, beta type, 1 (PSMB1) gene encodes a member of the proteasome B-type family, also known as the T1B family, that is a 20S core beta subunit (Trachtulec et al., 1997). This gene encodes TBP, the TATA-binding protein a transcription factor that functions at the core of the DNA-binding multiprotein transcription factor IID (TFIID). Binding of TFIID to TBP is the initial transcriptional step of the pre-initiation complex (PIC) and plays a role in the activation of eukaryotic genes transcribed by RNA polymerase II (Keutgens et al., 2010).
Programmed cell death 2 (PDCD2) gene encodes a nuclear protein highly expressed in placenta, heart, pancreas, lung, and liver, and lowly expressed in spleen, lymph nodes, and thymus. Expression of this gene is known to be repressed by B-cell CLL/lymphoma 6 (BCL6); a transcriptional repressor (Agata et al., 1996).
In addition, despite not reaching the genome wide significance, our study observed evidence for association at three additional loci containing the candidate genes LOC100128081, TNFRSF11B and FOSL2 (Bradfield et al., 2011). Of these, it is notable that the tumor necrosis factor receptor superfamily, member 11B (TNFRSF11B) is a strongly associated locus with bone mineral density, also discovered in GWAS, and the locus harboring LOC100128081 has also been reported in the context of a GWAS of SLE. FOS-like antigen 2 (FOSL2) gene encodes a leucine zipper protein that dimerizes with the JUN family proteins and forms the transcription factor complex activator protein 1 (AP-1). The FOS proteins have been implicated as regulators of cell proliferation, differentiation, and transformation (Cohen et al., 1989).
CUX2 (12q24):\n\t\t\t\t\tHuang et al., 2012 re-analyzed the original 2007 WTCCC study by using the 1000 Genomes imputation and reported refined variant rs1265564 in Cut-like homeobox 2 (CUX2) region for association with T1D. CUX2 is expressed exclusively in neural tissues. The protein belongs to the CUT homeobox family and contains three CUT domains and a homeodomain; both domains are DNA-binding motifs (Gingras et al., 2005). CUX2 gene has been shown to directly regulate the expression of NeuroD (Iulianella et al., 2008). NeuroD/BETA2, a transcription factor of the insulin gene, is reported to be associated with T1D in Asian descent (Iwata et al., 1999; Kavvoura & Ioannidis, 2005). Thus CUX2 is a plausible candidate for exploration in T1D pathogenesis.
HTR1A (5p13-q13):\n\t\t\t\t\tAsad et al., 2012 confirmed the previously suggested association between the chromosome 5p13-q13 regions and T1D in Scandinavian families (Nerup et al., 2001). None of the previous GWAS have reported any association of 5p13-q13 with T1D. This recent study identified the 5-hydroxytryptamine receptor 1A (HTR1A) and the ring finger protein 180 (RFN180) genes to be associated with T1D in multiplex (Swedish and Danish) families. However, the conditional analysis indicated HTR1A has as a primary association with T1D. Both quantitative PCR and immunohistochemical analysis confirmed the presence of the HTR1A in human pancreas (Asad et al., 2012). The study suggests that HTR1A may affect T1D susceptibility by modulating the initial autoimmune attack or either islet regeneration, insulin release, or both. The HTR1A gene is known to encode for a G-protein coupled receptor specific for serotonin, which mediates cellular signaling via the amine serotonin (Barnes & Sharp, 1999). The HTR1A receptor is mainly known to mediate signal transduction in neurons in the central nervous system (Lesurtel et al., 2008). However, serotonin is also produced in pancreatic islets of several different species (Sundler et al., 1980). Studies in rodent islets show inhibition of insulin secretion by serotonin (Zawalich et al., 2004). Sumatriptan (serotonin agonist) has an inhibitory effect on insulin secretion in humans (Coulie et al., 1998). Mohanan et al., 2006 reported a decrease in expression of HTR1A with increased insulin release during pancreatic regeneration. HTR1A also plays a role in the immune system. High level of protein expression has been reported in activated T-cells and low in resting T-cells; down regulates adenylate cyclase, which in turn regulates T-cell cytokine production and cytotoxicity (Aune et al., 1993). Hence polymorphisms in the HTR1A gene may affect insulin release and T-cell activity, thereby increases the risk of developing T1D.
This chapter provides a summary of recent advances in the identification of multiple variants associated with T1D. Genome wide association studies have revolutionized the field of autoimmune mediated disorders. In T1D only six genetic factors were well established before GWAS. GWAS has contributed greatly by expanding the number of established genetic variants to 57 genes. Most of these genes are novel and were not in any investigator’s favorite list. For the first time there is real consensus on the role of specific genetic factors underpinning T1D pathogenesis.
The discoveries of genetic factors involved in the pathogenesis of T1D through GWAS present the first step in a much longer process leading to cure. Genes uncovered using this approach are indeed fundamental to disease biology and will define the key molecular pathways leading to cure of T1D. However, such genome wide scans can lack coverage in certain regions where it is difficult to genotype so it is possible that other loci with reasonable effect sizes remain to be uncovered.
To date most of T1D-associated variants have been discovered utilizing cohorts of European ancestry because the SNP arrays were designed to optimally capture the haplotype diversity in this ethnicity. Novel SNP arrays are needed with the same degree of capture in diverse populations to elucidate the full role of each locus in a worldwide context.
The next challenge is to resolve the specific causal variants and determine how they affect the expression and function of these gene products. The Next-Generation Sequencing (NGS) technology has opened new avenues to elucidate the role of coding and noncoding RNAs in health and disease and would speed up the identification of causative gene variants in T1D.
No doubt, the in vitro and in vivo biology of these genes will be fascinating areas of exploration for many scientists. Only after fully uncovering the functional context of T1D associated genes; these findings will show promise of use for preventive strategies.
This research was financially supported by grant from National Institute of Health (DP3 DK085708-01) and an Institute Development Award to the Center for Applied Genomics from the Children’s Hospital of Philadelphia.
‘Emotional Finance’, as inaugurated by Richard Taffler and David Tuckett, received a warm reception in the early 2010s from regulators, the financial press, and investment management industry’s main professional body, the CFA Institute. Yet outside their immediate social and professional circles, the wider academic community largely ignored both Emotional Finance’s challenge to Behavioural Finance and its theoretical and methodological approach, which is, at its core, an application of Kleinian psychoanalysis to interpreting the individual psychology of traders and to describing the group psychology of financial markets. A decade after the financial crisis, industry professionals rarely talk about ‘Emotional Finance’ at all, except insofar as they see ‘emotional biases’ as a sub-species of behavioural biases, which hardly amounts to a successful challenge to Behavioural Finance [1]. Because Taffler and Tuckett’s approach is inherently interdisciplinary, few practicing psychoanalysts have felt equipped to comment upon their characterisation of financial markets, and yet fewer finance academics have the training or inclination to reckon with Taffler and Tuckett’s idiosyncratic handling of psychoanalytic theory.
Emotional Finance, as an intellectual project, rests on four related claims: first, that financial assets are categorically different from other kinds of commodities; second, that financial innovation peculiarly lends itself to being experienced as a ‘phantastic object’; third, that regulators can and should design institutional mechanisms that acknowledge the psychodynamics of the dealing room; and fourth, that the recent financial crisis ought to be handled in a manner similar to the post-Apartheid South African regime’s Truth and Reconciliation Commission. While agreeing that financial bubbles are banal and that emotional responses to the vicissitudes of the market are nothing remarkable, this chapter argues that financial assets are not categorically distinct; that the term ‘phantastic object’ is superfluous and its application is a variant of the ‘sharpshooter’ fallacy; that narrative causation is not formally equivalent to causality; that the relationship between group psychology and individual psychodynamics is under-theorised; and that financial instability is not the same as financial bubbles. Finally, there are other bigger threats to financial stability than those identified by the techniques employed by advocates of Emotional Finance, especially given the periods of relative calm in financial markets between the Great Financial Crisis (2007-2008) and its sequelae in the Eurozone Debt Crisis (2009-2012) and the recent crisis caused by the global coronavirus pandemic in Q2 2020.
By far the most problematic claim of Emotional Finance is that financial assets are somehow a special category. In Minding the Markets, Tuckett claims there are three characteristics of financial assets that make them unusually amenable to obtaining the status of a ‘phantastic object’: their volatility, their abstract quality, and the difficulty in determining whether or not a manager’s success was attributable to skill or luck ([2], p. xvii). Of these, the volatility argument is the most straightforward and also most peculiar. While it is certainly true that individual listed securities, futures contracts, or financial derivatives contracts may be highly volatile, financial markets themselves are notable for their lack of volatility. This is straightforward to demonstrate empirically because the volatility of the ‘S&P 500’ (as measured by the implied volatility of index options for the following 30 days) is itself a tradable index called the VIX or CBOE Volatility Index. Its performance since its inception in 1985 can be seen below in Figure 1.
CBOE Volatility Index (VIX) from December 1985 to October 2020 (daily closings). http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
For most of the last thirty-five years, the VIX has hovered around 15 to 25, with a high of 150 during the October 1987 ‘flash crash’. In the fourth quarter of 2008, after the Lehman collapse, it peaked at around 80. What does this mean? The VIX is a measure of volatility over a 30-day period, annualised, such that a VIX of 20 means that the market is expected to move up or down by 5.8% (or 20/√12) over the next 30 days. Even a VIX of 80 in means that the market is expected to move 23.12% up or down over the next 30 days, which most recently occurred in Q2 2020 with the outbreak of the COVID-19 pandemic. The daily volatility, by contrast, is calculated by dividing the number by √256, or the number of trading days in an average year, which for a VIX of 20, means that the market is expected to move up or down by 1.25% daily.
How does this compare to other commodities, say crude oil, or even to gold, that supposedly safe haven? Figure 2 answers this question. In the case of crude oil, the CBOE also publishes an Oil Volatility Index, which is markedly higher than the S&P 500, which compares favourably to a volatility index for gold over the thirty-year period from 1990-2020.
Comparison of Oil, Stocks, and Gold Volatility Indices 1990-2020. http://www.cboe.com/products/vix-index-volatility/volatility-on-etfs/cboe-crude-oil-etf-volatility-index-ovx (CBOE - Chicago Board Options Exchange); http://www.cboe.com/products/vix-index-volatility/volatility-on-etfs/cboe-gold-etf-volatility-index-gvz (CBOE - Chicago Board Options Exchange); http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
Some might complain that this is because oil and gold are exchange-traded and thus subject to added volatility, but, in fact, there is good evidence that exchange-based futures trading lowers volatility rather than amplifies it. We know this because of a natural experiment afforded by the Onion Futures Act of 1958, which banned futures trading in onions [3]. The price volatility of onions is considerably greater than that of either the S&P 500 or of crude oil. The charts in Figure 3 would not surprise economists or finance academics.
Onions vs Crude Oil, Onions vs S&P 500. (a and b) https://www.investing.com/indices/us-spx-500-historical-data (Investing.com Historical Data); https://www.indexmundi.com/commodities/?commodity=crude-oil&months=360 (Index Mundi data archive); https://usda.library.cornell.edu/concern/publications/k643b116n?locale=en (USDA Data Library); https://bsic.it/the-onion-paradox-or-why-futures-are-good-for-the-economy/ (Bocconi Student Investors Club Blog).
As acknowledged above, individual stocks may move more than the market as a whole, but most investment managers hedge this risk by holding a portfolio composed of a variety of different asset classes, let alone constituents among them. Retail investors, rather than professional money managers, may find themselves credit-constrained and forced to sell if a security falls quickly in value, but most fund managers have either automated stop-losses or the discretion with which to cut their losses. Moreover, over longer time horizons, financial assets are not particularly volatile compared to house prices in major markets in last fifteen years as shown in Figure 4.
Annual House Price Inflation Rates, 2006-2020. https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.do (Eurostat Data Archive); https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=1&1921=survey&1903=11#reqid=19&step=3&isuri=1&1921=survey&1903=11 (BEA, National Data).
The volatility of the stock market against the risk-free rate is not especially significant even during the Dotcom Crashes. As many commentators noticed at the time of the Lehman collapse, the problem was that most fund managers had spent the majority of their career chasing returns and had little experience of market snaps of any kind. Additionally, market snaps of the kind experienced during the 2008 crash or even the Eurobond crisis of 2011-12 pale in comparison to economic shocks such as that experienced during the coronavirus pandemic in Q1-Q2 2020 as illustrated in Figure 5.
VIX to 10 yr. Treasury Note Yield Ratio, 1990-2020. https://www.macrotrends.net/2016/10-year-treasury-bond-rate-yield-chart (Macrotrends Data Archive); http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
This is why financial engineers worked so hard in the period from 2003 to 2006 to create instruments, like mortgage-backed securities and collateralised debt obligations, that gave investors leveraged access to a much more volatile housing market.
There is no reason to contest much of Tuckett’s characterisation of the problems with the ‘efficient-markets hypothesis’ except to say that it is was originally developed as a simplifying assumption that made certain classes of models tractable. The slippage that allowed it to become a (flawed) description of reality, let alone an operative ideal and normative regulatory goal, is ideological and not the consequence of the internal logic of the idea. Some markets are efficient and do not forecast prices reliably, as Holbrook Working discovered when he explored North American grain markets in the interwar period or securities in the postwar moment [4, 5].
The second quality of financial assets, identified by Tuckett, which makes them amenable to the psychodynamic processes that he describes is their putative ‘abstractness’. This is surely in the eyes of the beholder. As early as Adam Smith [6], economic writers identified two (or three) distinct forms of value: value-in-use and value-in-exchange. Value-in-use can be further distinguished as value-in-consumption or in the income generated by a particular asset, i.e. you could live in a house or rent it out, you could eat the produce of your garden or sell it, etc. Value-in-exchange is what it commands either on an open market or in a barter transaction. For financial assets, there is often little consumable ‘value-in-use’ except at the margins, insofar as some investors may buy Class A or B shares in Berkshire Hathaway in order to meet Warren Buffett at his annual junket in Omaha and other investors (especially aggressive hedge funds) may purchase shares in order to control a company, oust its leadership, merge it with another, or even liquidate it. Most often, however, financial assets either generate income (which Tuckett suggests is usually calculated through a capital asset pricing model) or are sold on for speculative gains or losses. Modern finance theory holds that efficient markets should arbitrage value-in-use (income) and value-in-exchange, so that the dividend is ‘priced into’ the share, but in practice many investors are driven by a combination of dividend income and capital gains, and the protection of the latter in the U.S. and UK tax codes has been distorting investment behaviour these last thirty years or so.
Yet, in practice, these instruments are not abstract to those who trade them. Bonds have par values, generate coupon payments, have interest rates and calculable yields based on their prices. Equities may or may not pay dividends, but can be valued on that basis or on the book value of the firm. A variety of options, including the right to buy or sell a security at a particular price, have been well known for over 500 years ([7], p. 2-3). More complex financial derivatives were not abstract so much as they were opaque, in that they were tied to underlying assets that were, themselves, difficult to value. But even if we were to call that ‘abstraction’, it is by no means obvious that this is the sort of abstraction that lends itself to phantasy. It is equally plausible that relatively simple, graspable items in everyday life are the stuff of phantasies. The very complexity of some of the more recent species of financial assets (especially collateralised debt obligations that were tied to securitised mortgages) made them difficult to value, but the problem was not that investment managers fantasised about them, but rather that rating agencies were put under pressure to score them more highly than they deserved.
Elsewhere Tuckett contrasts financial assets with a television set, where ‘a “rational” consumer can consult a range of information about the price and quality and on that basis make a decision’ ([2], p. 21). He goes on to argue that the buyer might notice he got a bad deal, might observe the prices of televisions fluctuates as models sell out quickly or not at all, or as new models appear, and he might have buyer’s remorse, and even sell it on the second-hand market. Yet Tuckett maintains that ‘with financial assets the situation is very different [from television sets] as they have no intrinsic value but one determined by ambiguous information and varying expectations about an uncertain future that plays out in time’ ([2], p. 21). This is simply untrue. Bonds represent claims, either preferential or subordinated, on the business revenue or tax revenue of the firm or sovereign that issued them, equities represent residual claims against the book value of a firm, whereas financial derivatives (swaps, options, etc) represent contractual arrangements that can, and have, been litigated. The fortunes made by vulture funds that purchased collateralised debt obligations composed of subprime mortgages or of junior Greek debt is an indication that these financial assets do have values that are calculable.
What is more difficult to calculate, and is indeed often uncertain, is the depth of the secondary market at a given time, and hence the liquidity risk. Tuckett commits the same error as proponents of the efficient-markets hypothesis do when he ignores what is known in the trade as “the limits of arbitrage”, in that he assumes that buyers are not credit constrained and only buy or sell because of their sense of the direction of the market. The reality is very different, in that people and institutions can be forced to sell for a range of reasons (to raise money to meet current obligations) and institutions, like pension funds, can be forced to purchase risky assets because they have to match their assets with their liabilities to generate the returns needed to meet obligations that are years away. Liquidity risk is particularly acute in a financial crisis where people are not buying or selling anything at any price, but anyone who has tried to sell a television set near the end of the month can tell you that the used market also depends on the proximity of the average consumer to a weekly or monthly pay day, depending on the price level. Sensitivity to liquidity risk is difficult to know ex-ante, but it is not analytically difficult to grasp.
Moreover, the distinction between risk and uncertainty in Tuckett’s account is problematic. Risk is calculable based on an ergodic assumption that the future will be like the past. To a surprising degree, this assumption holds in financial markets, as the ‘equity risk premium’ (the premium paid to investors for buying equities over government debt securities) has not changed much in 150 years, and, as the finance literature has decisively shown, has made owners of shares better off than those who eschew the risks attendant to them [8, 9].
Moreover, as Figure 6 illustrates, the majority of the ‘total return’ from stocks comes from dividends not from price appreciation, which belies the idea that shares do not have a ‘value-in-use’ or income component that is tangible and real.
Nominal and Real Returns from Stocks and the Stock Index, 1927-2020. (a) https://www.statista.com/statistics/1032048/value-us-dollar-since-1640/ (Statista archive); https://www.macrotrends.net/1333/historical-gold-prices-100-year-charthttps://onlygold.com/gold-prices/historical-gold-prices/ (Macrotrends); https://fred.stlouisfed.org/series/DGS20#0https://www.multpl.com/s-p-500-historical-prices/table/by-year (St Louis Federal Reserve Archive); https://seekingalpha.com/article/4311451-stocks-bonds-bills-and-inflation-returns-for-94-years-ending-december-2019 (Seeking Alpha Blog). (b) https://finance.yahoo.com/quote/%5EGSPC/history?period1=-1325635200&period2=1603238400&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true; https://www.officialdata.org/us/stocks/s-p-500/1927?amount=100&endYear=2020 (Yahoo Finance); https://www.in2013dollars.com/us/inflation/1927#:~:text=In%20other%20words%2C%20%24100%20in,inflation%20rate%20was%20%2D1.69%25. (CPI Inflation Calculator).
The returns above are for U.S. equities as an asset class (i.e. they represent ‘beta’), and say nothing about particular securities or vintages. Yet it is precisely because retail investors and professional money managers can buy (and sell) index-funds that active managers have to try to ‘beat’ the market. That is where the pressure comes, to generate ‘alpha’, which Tuckett correctly notices is ephemeral, and, according to adherents of the efficient-markets hypothesis, at best idiosyncratic and at worst a statistical mirage. The money managers that Tuckett identified have an incentive to depict their performance as a result of their skill, but given the survivorship bias (firms that get unlucky fail and disappear), the charge that even the big winners are probably ‘lucky monkeys’ is not without some real plausibility.
So, yes, returns are ‘uncertain,’ but what does this mean? ‘Uncertainty’ refers to the ‘unknown unknowns’ of Knight and Keynes, but hardly matters much to the everyday operation of markets, which have remained remarkably continuous and well-funded in all but a handful of cases (some of the more exotic CDOs still do not trade at any price) through the last crisis. The reason that discussions of ‘uncertainty’ were back in vogue in the 2010s is that markets in 2003-2007 under-priced risk, because the models could not account for uncertainty. Once-in-a-lifetime events (the so-called Black Swans) are important to risk managers, but for Tuckett’s argument to work they have to be an everyday feature of markets, which they, by definition, are not. Tuckett is describing the life of an onion trader not a financial asset manager. This is precisely why more recent work on emotions in financial markets has eschewed the problematic articulated by Taffler and Tuckett in favour of understanding how emotions affect participants in markets under ordinary trading conditions [10].
While a middle class, middle-aged Londoner might indeed see a television as ordinary and mundane and a share as ‘exotic’ and ‘abstract’, but as seen in the London riots of 2011, ordinary people did risk their livelihoods, reputation (and a possible criminal record) and even lives to snatch televisions and trainers from shops. Before globalisation, not so long ago, consumers in emerging markets invested television sets with almost magical properties. If television sets are in the eye of the beholder, it is equally plausible that various financial instruments are mundane, familiar and more or less rationally estimable by people who trade them every day. Is financial innovation, in its first incarnation then, any different?
Tuckett thinks so. He cites tulips, subscription shares (in the South Sea Bubble), and a host of other items ([2], p. 18) as inherently generating outsized excitement because they represented ‘financial innovation’, but these assertions are not proven. In some cases, as with tulips and subscription shares, the characterisation of them as novel financial innovations is just wrong ([7], pp. 2-4), as futures trading in Baltic grain had preceded that in tulips by over half a century, and the Bank of England offered subscription shares to English investors decades before the South Sea Company directors did. In other cases, it is easier to explain the erratic valuations in terms of Akerlof’s lemon problem, which can be summarised as those who cannot tell good wine from bad will overpay for the latter and undervalue the former [11]. This is not to deny that bubbles form in financial markets or that they are further fuelled by fantastic narratives about the value of the assets, which are their focal point. But let us not forget that the ‘bubble’ in the early noughties was not in collateralised debt obligations, but rather in housing market where price rises were fuelled by the advent of subprime mortgages, which are neither especially abstract nor backed by something intangible. The trouble is not with the role of fantasies in bubbles, but rather with the theoretical formulation of these ‘phantastic objects.’ In short, there is nothing unique about financial assets.
The theoretical edifice of Emotional Finance equally depends on the usefulness of the term, ‘phantastic object’, as a plausible unit of analysis. Tuckett [2] gives his most recent definition of a phantastic object as ‘subjectively very attractive “objects” (people, ideas or things) which we find highly exciting and idealise, imagining (feeling rather than thinking) they can satisfy our deepest desires, the meaning of which we are only partially aware’ ([2], p. xi). According to Tuckett, he had ‘coined the term’ as an attempt to explain a situation where ‘a story gets told about an object of apparent desire (such as a dotcom share, a tulip bulb, or a complex financial derivative), which becomes capable of generating excitement in a situation where outcomes are inherently uncertain’ ([2], p. xiv). He reports that ‘the term conjoins “phantasy” as in unconscious phantasy and “object” as in representation.’ What does this mean? Tuckett further explains: ‘the phantasy stimulated is about more than just a story of getting rich. Rather it is a story about participation in an imagined object relationship in which the possessor of the desired object plays with the omnipotent phantasy of having permanent and exclusive access to it and all good things’ ([2], p. xiv). Although Freud and Klein both long ago recognised that all object relationships are ambivalent, Tuckett sees ambivalence (and with it a degree of abstraction, ambiguity and uncertainty) as necessary ingredients of this heady emotional stew [12, 13]. Yet the insistence on ambivalence, for emphasis, is hardly a cardinal sin.
The more serious problem arises, however, when we pick apart this notion of ‘phantasy.’ For Tuckett and Taffler, the usual citation is Freud’s meditation on creative writing and daydreaming [14]. Here Freud develops a theory, no longer accepted even in literary theory, that a child’s fantastical play is very similar to the creative writer, because both the child and the writer are able to distinguish their intensely rich libido-cathected worlds and the characters they create for them from external reality ([14], p. 142-3). Phantasies, both conscious and unconscious, come to replace play, for Freud, as ‘the growing child when he stops playing, gives up nothing but the link with real objects; instead of playing, he now phantasises’ ([14], p. 144). Adults populate their phantasies with their internal objects both in manifest and disguised forms. In that limited sense, Tuckett’s ‘phantastic object’ is simply any object that has found a place in an adult’s unconscious phantasy, or what Freud constructed as the ‘psychical reality’, which is unique to each individual. Exactly how ‘financial assets’ become the paradigmatic ‘phantastic object’ remains to be shown, unless what Tuckett really means is that financial assets evoke some memory of a part-object or maternal breast.
To complicate matters, Freud recognises that children rarely conceal their fantastical play, whereas ‘the adult, on the contrary, is ashamed of his phantasies and hides them from other people. He cherishes them as his most intimate possessions, and as a rule he would rather confess his misdeeds than tell anyone his phantasies ([14], p. 144). If so, it is all the more remarkable that the fund managers whom Tuckett interviewed were willing to tell their ‘phantasies’ to him, over the course of one meeting of 70 minutes or so, unless, if by analogy to Freud’s neurotic patient who hopes for a cure, they confess their phantasies to Tuckett in hopes of absolution for speculative excess ([14], p. 145).
Freud’s explanation of what causes adults to hide their phantasies in shame arises from the fact that they are ‘either ambitious wishes, which serve to elevate the subject’s personality; or they are exotic (sic) [erotic] ones’ ([14], p. 146). But he cautions, ‘we will not lay stress on the opposition between the two trends; we would rather emphasise the fact that they are often united’ and just as often reparative ([14], p. 146-7). Even the most overtly ‘ambitious, egotist wishes’ have some element of sexual gratification involved, if merely auto-erotic in the most narcissistic of states. Freud finishes by comparing ‘phantasies’ to ‘dreams’ and noting their quality of wish fulfilment ([14], p. 148). All of this is very familiar to psychoanalysts, but Tuckett’s neologism has lost the crucial sense, found in Freud, of erotic wish fulfilment, presumably because draining it of the erotic makes the concept more palatable to Tuckett’s audience. The Strachey translation’s tendency to render ‘fantasy’ as ‘phantasy’ (which is now the conventional usage in Britain) further reinforces the impression that it has little to do with sex, however alien the spelling may seem to American readers.
If we allow that conscious and unconscious narratives which adults weave about their internal objects are invested with libido and contain sexual gratification and conquest as elements of their function as wish fulfilment, then there is nothing unusual, let alone alarming, about a particular investment or set of investments acting as the vehicle, in such a fantasy, to unlimited wealth and with those resources the means to sexual conquest. Buyers of lottery tickets do this every day. To the extent that a particular object occupies a stereotyped place in such narratives, such that it becomes ‘very attractive’ and ‘idealised’ to an individual, let alone a group, then we have something closer to a ‘fetish’ or ‘an inanimate object worshipped … for its magical powers or as being inhabited by a spirit’ ([15], p. 57). Fetish objects also provoke the ‘divided states’ that Tuckett describes ([2], p. xi), possess ‘magical powers’ and lead to ‘potency the [fetishist may] otherwise lack’ ([15], p. 57). Whereas many sexual fetishes function by synecdoche (feet, hair, clothes, footwear, etc), others do so by metonymy, offering a substitute object ([16], p. 132). There are two further features that sharpen the similarities between ‘fetish object’ and ‘phantastic object’, namely ‘(a) the fetish has multiple meanings derived by condensation, displacement and symbolisation from other objects, and (b) the fetishist behaves as though [the fetish object] actually were these other objects and is no more disturbed by incongruity or absurdity than a dreamer is while dreaming’ ([15], p. 57). Ironically, Tuckett’s example of Aladdin’s lamp is usually explained as a fetish object rather than a phantastic one. The use of ‘pseudo-psychoanalytic’ language (‘phantastic object’ in place of ‘fetish object’) may be more acceptable to the audience, but it has the consequence of dislocating the concept within a wider psychoanalytic discourse.
Although Tuckett does not acknowledge this in his own discussion, the Emotional Finance presentation of ‘phantastic objects’ also depends on these meanings derived from ‘condensation, displacement and symbolisation’ ([15], p. 57). Before exploring that in detail, it is first worth considering the alternative source of ‘phantastic object’ as offered by Tuckett, namely in the definition of ‘phantasy’ offered by Laplanche and Pontalis in their rival to the Rycroft volume ([17], pp. 317-321), which invokes the principle that ‘the use of the term “phantasy” cannot fail to evoke the distinction between imagination and reality (perception). If this distinction is made into a major psycho-analytic axis of reference, we are brought to define phantasy as a purely illusory production which cannot be sustained when confronted with a correct apprehension of reality’ ([17], p. 315). As they note, ‘certain of Freud’s writings appear to back up this type of approach. Thus in “Formulations on the Two Principles of Mental Functioning” (1911b), Freud sets the internal world, tending towards satisfaction by means of illusion, against an outside world which gradually imposes the reality principle upon the subject through the mediation of the perceptual system’ ([17], p. 315).
For Taffler and Tuckett, the ‘reality’ of financial markets ultimately strips the ‘phantastic objects’ of their value if not their meaning, as the inevitable crash and de-idealisation leads to anger and revulsion [18]. As Laplanche and Pontalis also notice, modern psychoanalytic usage extends ‘phantasy’ to a range of conscious, preconscious and unconscious fantasies, thereby muddling the extent to which repression plays a role ([17], p. 315). They suggest, instead, distinguishing between day-dreams that serve as compromise-formations, common ‘unconscious phantasies’ that appear as precursors to neurotic symptoms, and unconscious fantasies that offer the seeds of wish fulfilment in dreams. With Tuckett’s formulation, the narratives about ‘phantastic objects’ appear to be mostly preconscious, in that the fund managers are not necessarily aware of them until prompted by their interlocutor, but then venture them freely. Whether or not this is plausible in a psychoanalytic sense remains debatable, as Tuckett’s formulation appears to ignore both the roles of secondary revision and of repression.
As with Freud, Laplanche and Pontalis also link phantasies to desire, which does not even merit an index entry in Tuckett [2]. In Laplanche and Portalis, they emphasise the extent to which wish fulfilment evokes the ‘hallucinatory memory of satisfaction’ (1973, p. 318), or the maternal breast, which is a kind of primordial ‘phantasy-object’. Yet they also acknowledge, ‘the relationship between phantasy and desire seems to us to be more complicated than that. Even in their least elaborate forms, phantasies do not appear to be reducible to an intentional aim on the part of the desiring subject …’, and crucially ‘it is not an object that the subject imagines and aims at, so to speak, but rather a sequence in which the subject has his own part to play and which the permutations of roles and attributions are possible’ ([17], p. 318). Read that way, Tuckett’s ‘phantastic object’ is, in effect, a contradiction in terms, in that it is not the object itself that the subject desires, but rather the outcome of the script, i.e. unbridled wealth, beautiful women (or men), and universal gratification. In other words, we’re back to ordinary explanations that turn on greed, lust, and gluttony.
As to the relationship between the internalisation of the so-called ‘phantastic object’ and the processes Rycroft alludes to of ‘condensation, displacement and symbolisation’, these are, in effect, what Tuckett evokes when he describes the ‘divided states’ of idealisation and de-idealisation that he postulates occur in the minds of traders. There may well be a value in thinking about how financial assets relate to Marxian and Freudian notions of ‘fetishism,’ but Tuckett forecloses this possibility with his neologism.
To summarise, there are two separate etymologies of ‘phantastic object’ in Tuckett’s writings with Taffler on the subject. The strain that depends most heavily on Freud is very hard to distinguish from more conventional uses of ‘fetish object’ while the version that depends strictly on Laplanche and Pontalis is oxymoronic. Either ‘phantastic objects’ are essentially fetish objects, denuded of the explicit eroticism, or they are an inherently self-contradictory attempt to bridge the gap between part-objects expressed in paranoid-schizoid states (where what the infant desires is the maternal breast) and vehicles for the realisation of erotic phantasies in less regressed states of mind. The love affair, in short, is not with the financial asset or the car or the suit, but rather still with idea of ‘getting the girl’ or ‘winning the game.’
The latter is simply an instance of superfluity and proliferation of neologisms, which in turn muddles the waters, whereas the former suggests something of the very problematic hermeneutic strategy employed by those who advocate for Emotional Finance.
Interpretation is not explanation; causation is not causality. In the social sciences, this is almost a cliché, but they are important caveats. Causality rests on the identification of a specific mechanism by which X has an effect on Y. Explanations can be realistic in the sense that they try to account for external reality, or epistemic (anti-realist) in the sense that they strive for the internal consistency of the empirical model. Much of Tuckett’s complaint with modern economics is that it strives for the latter, whereas the natural sciences present themselves as interested in the former, except perhaps in cosmology.
In finance, the movement of prices is easy to explain: they rise when there are more buyers than sellers, they fall when there are more sellers than buyers. The willingness to buy or sell is, indeed, partly influenced by individual expectations of future prices, such that for markets to function there has to be heterogeneity of belief. There is nothing at all surprising about that. Predicting the movement of prices is an occult science, whether practiced by ‘chartists’ who do ‘technical analysis’ or by punters who pontificate on the market outlook for a particular stock. Interpreting price behaviour (explaining why markets rose or fell) lies somewhere in between, though much of it depends on normative judgments about ‘value’. To imagine that you are in an asset-price bubble is to imagine that the current prices of an asset have diverged from some ‘rational’ judgement of fundamental value.
Tuckett retains that notion of ‘rationality’, though he attributes it to an uncertain, yet-to-be-experienced ‘objective reality’ rather than to the price discovery mechanisms of the market. Now he is by no means alone in that, as Behavioural Economics speaks of ‘bounded rationality,’ but the problem is whether or not any of this can be apprehended ex-ante. Tuckett identifies the ‘drowning out’ of naysayers as a feature of the euphoria he describes, yet some of these naysayers, like Nouriel Roubini, have successfully predicted ten out of the last three crises, whereas Warren Buffett made an even greater fortune on Berkshire Hathaway’s derivatives book, even as he preached about ‘weapons of mass destruction’. Some people are hypocrites, others are stereotyped market commentators, and even stopped clocks are right twice a day. It should not be forgotten that the people who made the most money in the Great Financial Crisis where those who shorted subprime mortgages, often against the interests of their own clients. The most successful currency trade in modern times, the Black Wednesday bear raid organised by Soros, was, in fact, a mean-reversion trade designed to force sterling out of the European Exchange Rate Mechanism. It just took two billion pounds to do it. In other words, the trick about shorting anything is timing it. To echo the line often mis-ascribed to Keynes, ‘markets can remain irrational longer than you can remain solvent.’
Tuckett [2] variously declaims any attempts at quantification, though his most recent work aims in that direction by attempting to exploit insights from Big Data [19, 20, 21, 22, 23]. Instead, what Tuckett’s approach is offering is an interpretative strategy, which serves mostly as an elaboration of the latter stages of the Minsky-Kindleberger model of an asset-price bubble [24], which identifies states of ‘displacement, new opportunities, boom, euphoria, dismissal, unease, panic, revulsion’ ([2], p. 16). What the Minsky-Kindleberger model describes is not a mechanism of causality but a causal chain. Psychoanalysis, with its roots in Aristotelian casuistry (with the assumption of a relationship, albeit a complex one, between infantile conflicts and adult neuroses), is especially well-suited to such an exercise. Moreover, psychoanalytic theories of causation are also multi-valent. Aristotle identified four cases: material, formal, efficient and final. Freudian psychoanalysis, on the other hand, tends to consider symptoms simultaneously in terms of ‘origins, genesis, function, meaning and expectation’ ([25], pp. 22-36). For example, your euphoria might well have its genesis in the rising price of a stock (or falling if you shorted it), might have its origins in an outpouring of enthusiasm for a new technology in which the particular firm has an advantage, might be a function of the fact that institutional investors have decided they need exposure to that sector, may mean that firms that rely on older technologies will experience hard times, might have been expected given the success of a similar technology in a more advanced country. None of these interpretations of your euphoria have anything to do with causality in any formal sense (that the number of buyers in the market started to outnumber the number of sellers); but rather this strategy reflects a mode of analysis of narrative causation that is liberating because it disrupts established narratives and opens up the possibilities of new ones. This is why Tuckett eventually became interested in ‘conviction narratives’, because that seemed to present a means of moving from causation to casuality [20, 22]. Unfortunately, this strategy creates a hermeneutic circle.
As post-structuralist literary critics subsequently noticed, these narratives generally are organised around one of four tropes: metaphor, metonymy, synecdoche, and irony, which correspond to the emplotments of romance, tragedy, comedy/farce and satire [26]. Tuckett’s case studies of investment managers do all follow similar trajectories, but figural causation is an artefact of the mimetic function of narrative not a feature of reality [27]. The reason that people have not learned from previous crises is because there is less to learn than some might imagine. To suggest otherwise is to suggest that there is some path dependent group psychological structuring of financial bubbles, such that they all have the same denouement, regardless of the particular asset at their core. To the extent that this is true, it is obvious (and just an elaboration of Minksy-Kindleberger), and to the extent that it is not obvious, it is wrong (in that the focal point of bubbles does matter) for reasons that should become clear in the next two sections.
As Tuckett explains, ‘groupfeel’ has replaced his earlier usage of ‘groupthink’ as a way of aggregating the individual emotion states of participants in a market. He is surely correct that groups display elements of ‘consensus seeking, group polarisation, out-group stereotyping, and the suppression of dissent’ ([2], p. 66-67), but what becomes harder to understand is why he sees financial markets as ‘groups’ in the sense that a notion of ‘groupfeel’ would apply. Financial markets are nothing if not competitive arenas, and despite the existence of social spaces in which collusion might occur (c.f. LIBOR-fixing), the notion that the market is subject to these dynamics is implausible. Individual firms may be, which is significant only insofar as some bulge-bracket firms become the dominant dealers in particular financial assets. Tuckett is likewise correct that the structure of the industry means individual managers may be more concerned with short-term performance than with longer-term results, but that has nothing to do with phantastic objects and everything to do with how compensation is structured. This is also one of the few areas in which the market does ‘zero-sum,’ as those managers who outperform the market benchmark are richly rewarded in fees, whereas those who underperform benchmarks get sacked. That said, zero-sum games are not especially known for displaying the sorts of group psychologies found elsewhere.
Behavioural Finance, instead, offers ‘herding’ as a heuristic that mangers use to main-chase the perceived ‘market leaders’. This is not necessarily an emotional response (as one might prefer to think oneself smarter than other traders), but rather a rational one of achieving safe but possibly sub-optimal returns. Even so, it is a simplification to suggest that risk-on/risk-off maps to divided states (paranoid-schizoid and depressive positions), unless one assumes that the ‘market norm’ is one of stagnation, which, at least in equities, is discredited by the data presented in Figure 6. What Tuckett has, in fact, done is taken a typical risk management heuristic of the directors of trading desks on dealing floors, which is to remove from the floor traders who are losing on a given day and to cap the winnings of those who appear to be ‘streaking’, and used it as a synecdoche for the market as a whole. That interpretative strategy makes very little sense, however, when you consider that the reason that the risk manager is pulling the trader is that the frustration and disappointment causes him to exaggerate risk, whereas the success encourages him to underrate risk vis-à-vis the market as a whole. Crucially, in Tuckett’s model, the problem is not the distance between the judgements of individuals and the group, but rather the distance between the phantasies of market participants and his notion of ‘reality’, which can only be apprehended ex-post, but seems in fact to be based upon some notion of equilibrium and rationality that hovers behind the precise notions that he and Taffler try to critique [28], hence the hermeneutic circle.
The final problem with Emotional Finance is the specification of the problem. Financial bubbles are nothing new. One recent work of economic theory written by a former practitioner called them ‘banal’ and ubiquitous [29]. Janeway suggests that problem is not with asset-price bubbles, per se, but rather that some of them are productive whereas others destructive. Asset-price bubbles that focus on ‘general purpose technologies’ or infrastructure (canals, steamships, railways, electrification, information computing technology, etc) tend to be socially beneficial. The rush of speculation generates a tolerance for Schumpterian waste. Once the music stops, individual firms may go bankrupt, but the roads, canals, bridges, and rail lines remain.
Debt-leveraged bubbles, particularly in real estate, can wipe out private wealth and cause contagion to other aspects of the economy, generating great hardship, but those are usually generated by central banks that use household balance sheets to smooth aggregate demand, as happened in the late 1990s and early 2000s. Tuckett’s analysis makes no distinction on the basis of the focal point of the bubble. Bubbles are all equally suspect, in that they are formed around ‘phantastic objects’ that promise fool’s gold. Underlying Tuckett’s work is the peculiar fantasy that it is possible to train regulators to identify asset-price bubbles based on their recognition of a kind of prodromal euphoric state as evidenced in the chatter of traders, particularly on the Bloomberg platform. The idea is then to install circuit-breakers, as a kind of market nanny decides ‘enough is enough.’ The problem with this is that it begs the regulatory equivalent of the Texas sharpshooter fallacy, whereby a gunman sprays the side of a barn with a shotgun and only then steps up to draw the bulls-eye. We can only guess at the number of ‘dangerous’ bubbles that will be so averted!
One final point remains to be made. Tuckett calls for a ‘Truth and Reconciliation’ Commission to investigate the financial crisis in the same manner as the Apartheid-era crimes in South Africa. Leaving aside the question of how successful the latter was, the former is hardly worthy of such an intellectual and moral project. The Great Financial Crisis was not the end of the world as we know it, particularly from the vantage point of 2020 when the effects of COVID-19 lockdowns are so much greater. If the Great Financial Crisis was a searing experience for the Millenial generation, it was not because of economic realities, but because of the political responses to the crisis. Austerity policies which produced widening inequality in the developed world are neither ‘necessary evils’ nor the product of the ‘inner logic of capitalism,’ but rather are ideological and political projects pursued with the blessings of the median-voter.
The most dangerous thing about Tuckett’s proposals for ‘minding markets’ is that they de-politicise the regulatory process, putting it in the realm of regulating human emotion, rather than in the sphere of political economy. For Tuckett, based in the United Kingdom, the irony of the financial crisis is that the neo-liberal experiment in ‘light touch’ regulation and low levels of taxation (especially on capital gains) happened under New Labour. The Tories came to power on the back of New Labour’s mistakes, and have followed the ‘tried and true’ strategy of austerity while blaming the ‘pain’ on their predecessors and on the European Union. The electoral calculus of such a strategy produced Brexit. The glee with which the political elite have pursued these aims and the docility of the electorate in the face them is a much worthier target of study via notions like ‘groupfeel’ and ‘phantasies’ that produce master narratives. Rather than minding impersonal markets, we’d do better to mind our own tendency to deny the damage done by those who used the financial crisis to justify policies that they fully intended to pursue anyway. If 9/11 was George Bush’s excuse for invading Iraq, the collapse of Lehman Brothers in its vicissitudes offered the Tories a pretext for dismantling the welfare state. In the era of COVID-19, the main challenge for regulators and central banks alike will be resisting pressure from politicians the world over to encourage bull markets deliberately in order to maintain consumer and investment confidence in the face of damage to the real economy and especially the attendant job losses. In addressing such questions, ‘Emotional Finance’ has little to offer, despite the sense of Déjà vu involved.
The author would like to acknowledge a number of people with whom she has discussed this subject over the years, including Professor Michelle Baddeley, Dr Stanley H. Shapiro, Dr Frederick Fisher, Dr Margot Waddell, and Dr David Bell, and the members of the London Consortium’s ‘Psychoanalytic Thought, History and Political Life Forum,’ which was where she gave a version of this paper as a seminar in June 2014. Special thanks to the organisers, Dr Shaul Bar-Haim, Dr Benjamin Poore, and Dr Helen Tyson, and particularly to Professor Daniel Pick, Professor Jacqueline Rose, Dr Matt ffytche, Dr Manuel Batsch, and Dr Akshi Singh for their comments. A version was also circulated in September 2015 at the New Imago Forum at Jesus College, Oxford where useful suggestions were made. The author would also like to thank her research assistant, Mr Eskil Välilä, for redrawing the graphs with updated data, which required considerable research, and for rendering them to suit the formatting guidelines for this publication.
The author declares no conflict of interest.
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