multiple disease hypothesis of common complex genetic disorders

Medical Hypotheses (2004) 62, 309–317

http://intl.elsevierhealth.com/journals/mehy

The common variants/multiple disease hypothesis of common complex genetic disorders Kevin G. Becker* Gene Expression and Genomics Unit, TRIAD Technology Center, National Institute on Aging, National Institutes of Health, Room 208, 333 Cassell Drive, Baltimore, MD 21224, USA Received 6 January 2003; accepted 6 October 2003

Summary Unlike simple rare Mendelian disorders, the genetic basis for common disorders is unclear. A general model of the genetics of common complex disorders is proposed which emphasizes the shared nature of common alleles in related common disorders, such as schizophrenia and bipolar disorder, Type II diabetes and obesity, and among autoimmune diseases. This model, the common variants/multiple disease hypothesis, emphasizes that many disease genes may not be disease specific. Common deleterious alleles, found at a relatively high frequency in the population may play a role in related clinical phenotypes in the context of different genetic backgrounds and under different environmental conditions. Published by Elsevier Ltd.

Introduction The genetic basis of common complex disorders is the subject of great scientific and clinical interest, yet remains unclear. Applying the experience of classical single gene Mendelian genetics to nonMendelian complex disorders is not straightforward. Moreover, the use of statistical approaches in modeling complex disease is imperfect due to often-unknown underlying biological complexity and as such there is controversy over the use of different statistical strategies [35,57,78]. Common disorders represent a large majority of human disease having a genetic component and pose both a conceptual and technical challenge. Psychiatric diseases, metabolic disorders, autoimmune disease, and complex degenerative processes are general classes of common complex disorders. *

Present address: Gene Expression and Genomics Unit, Gerontology Research Center, National Institute on Aging, National Institutes of Health, 5600 Nathan Shock Drive, Baltimore, MD 21224-6825, USA. Tel.: +1-410-558-8360; fax: +1-410-558-8326. E-mail address: [email protected] (K.G. Becker). 0306-9877/$ - see front matter. Published by Elsevier Ltd. doi:10.1016/S0306-9877(03)00332-3

Examples of individual diseases within each class include: psychiatric diseases, schizophrenia, unipolar, and bi-polar disorder; metabolic disorders, Type 2 diabetes and obesity; autoimmune and inflammatory disorders, thyroiditis, systemic lupus erythematosus, multiple sclerosis, and Type 1 diabetes, as well as asthma and atopic disorders. Heritability, linkage, association, and twin studies of many common disorders have suggested complex multifactorial genetic contributions to these diseases. Unlike conditions involving single gene defects, which are inherited in a classical Mendelian fashion and where mutations are considered to be causative, the genetic contributions to common complex disorders are generally considered to be susceptibility loci, influencing but not determining overall disease risk. As such, genetic components of an individual complex disease are often thought to involve multiple interacting genetic loci, each of which may contribute small effects in an additive manner in the context of epigenetic factors, to overall disease susceptibility. Epigenetic or environmental factors may include infection, diet, environmental insult, level of

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exercise, stress, and importantly, timing and developmental stage of these epigenetic events. This complexity has made the task of identifying disease genes quite difficult. It has been suggested that, due to the complexity of these disorders, we carefully consider the populations, technical approaches to analysis, and clinical classifications in evaluating the genetics of common disorders [70,76]. Similarly, the theoretical basis for these diseases should be considered carefully as well. In this report, I propose a general molecular model that considers the shared nature of common alleles, how they may involve common pathways, and how this may play a role in clinically related disorders.

Overlap of clinical characteristics among related common disorders While it may be logical and useful to categorize disorders into discreet classifications, a striking aspect of a number of common disorders is that they tend to overlap in many ways with closely related diseases. Within each class of disease, related diseases may share similar or overlapping clinical features, therapeutic strategies, and as described here, may have genetic elements in common. This overlap may manifest itself as specific shared clinical characteristics of disease [30,63,69,81], a spectrum of symptoms within a given disease classification [48,56,64], or as comorbidity of related disorders and phenotypes within individual patients, all suggesting that etiological factors in a given disorder are shared between related disorders. For example, comorbidity has been described in schizophrenia and bi-polar disorder [19,58], with intermediate phenotypes such as schizoaffective disorders found in families with both schizophrenia and affective disorder. Similarly, autism, ADHD, and Tourette syndrome overlap clinically [4,17,38,42,43,51]; as well as coronary heart disease, stroke, and other cardiovascular disorders. Multiple autoimmune disorders, both systemic and organ specific, are relatively common within individual patients and within families of patients with a given autoimmune disorder, suggesting a common underlying etiology [16,24,41,55,65].

The common disease/common variant hypothesis A straightforward hypothesis has been proposed, referred to as the common disease/common vari-

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ant (CD/CV) hypothesis [22,46] which questions why these diseases are so common and why are they maintained in the population at such high frequency. This hypothesis states, “the genetic risk for common diseases will often be due to diseaseproducing alleles found at relatively high frequencies (>1%)”. Although weak on empirical evidence [60], the CD/CV hypothesis is logical and may be true given that the alternative of common diseases being determined by rare alleles is statistically less likely. The CD/CV hypothesis addresses the question of why these disorders are so common; because the underlying disease influencing alleles are common. Moreover, an underlying assumption is that the deleterious effect of each disease producing allele is, on average, relatively low. Polymorphisms in the APOE gene in the context of late-onset Alzheimer’s disease have been suggested as a prototype for this hypothesis [20] due to the high population frequency of the APOE4 allele. Late-onset Alzheimer’s is a common disorder affecting up to 60% of the population after age 75. APOE4 is present in 15% of the population and is found in up to 50% of individuals affected with lateonset Alzheimer’s [20]. In a similar fashion, other disorders with high incidence in the population such as asthma, Type 2 diabetes, and schizophrenia have been suggested to involve common alleles as well [22,46]. Questions arising from the CD/CV hypothesis are: Are these common genetic factors specific to a particular disease? What are the effects of these individual disease-influencing alleles when they are in other genotypic backgrounds, in other genetic combinations, influenced by other epigenetic or environmental factors? If susceptibility genes are not causative and impart disease risk only in certain contexts, what are they doing in the meantime? Are they neutral? Are they having deleterious effects? Do they have subtle effects in other epigenetic or environmental contexts? In particular, are these deleterious alleles influencing the clinical course of related diseases that have overlapping or similar clinical characteristics?

Overlap in the genetics of related disorders Over the past decade, the genetics of common complex disorders have been studied in different ways, including linkage analysis using whole genome scanning methods and through genetic association studies. Studies of this type are difficult, significance values are generally suggestive [45],

The common variants/multiple disease hypothesis

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and at times results may not be confirmed in independent studies [26,37].

clude linkage for multiple sclerosis, asthma, Crohn’s disease, systemic lupus erythematosus, and celiac disease. Similarly, at chromosome 6q27 a locus previously defined as IDDM8, has been linked to Type 1 diabetes, as well as to SLE, and ankylosing spondylitis. This clustering or co-localization of autoimmune linkages is not uncommon and is found in at least 30 regions in the human genome including; 1p21–22, 1q24–25, 1q42, 2q22, 2q32–36, 3q21, 4q28, 5p15, 5p11, 5q31–33, 6p12–q11, 6q27, 7p15–21, 7q21–22,7q31, 8q22, 9p22, 10p12, 11p15, 11p13–14, 12p12–13, 14q31–32, 15q11, 15q26, 16q12–21, 17p13, 17q22, 19p13, 19q13, 20p11, 20q13, 21q22, 22q12–13, and Xp11. In many cases, identical polymorphic markers were used at these loci. A composite map of autoimmune/inflammatory disorder linkage can be found at the following address: http:// www.grc.nia.nih.gov/branches/rrb/dna/comparativegenomics.htm. Similarly, co-localization of linkage has been demonstrated between schizophrenia and bi-polar disorder. Both disorders have been linked to multiple loci [79] with overlap occurring at 3q [1], 10p13–12 (D10S1423) [31,61], 13q32 (D13S174) [12,28], 18p, and 22q11–13 (D22S278) [44,47]. As in autoimmune disease, this has also led to a shared

Linkage Through genome scan-based linkage studies, it has been observed that in some cases genetic loci overlap or co-localize between related disorders. This has been shown between schizophrenia and bipolar disorder [9–11,13,79]; ADHD and Tourette syndrome [33]; and Type 2 diabetes and obesity [5]. Recently, coincidence of linkage and association has been described in the context of genetic vulnerability to substance abuse [73]. In linkage analysis of autoimmune disorders, loci overlap has been shown in multiple autoimmune animal models [29,52,71,74,80] and in multiple human autoimmune disorders [6,3,39,53], leading to a shared loci hypothesis in the etiology of autoimmune disease [7,49]. Fig. 1 shows selected overlapping linkage results in related autoimmune disorders. For example, at chromosome 7p22.1–15.1, within a 20-cM region multiple autoimmune disorders have been linked, often using the identical polymorphic marker, all having a similar significance score (Lod 1.0–2.0). These in-

Figure 1 Chromosomal position of overlapping linkage from autoimmune studies. All markers are peak significance scores (LOD, Z-values, or P-values). Each locus is arbitrarily defined as a 10 cM confidence interval. The width of the line is proportional to the significance scores. All chromosomal positions are obtained from LDB; http://cedar. genetics.soton.ac.uk/public_html/ldb.html. All supporting data can be found at this address http://www.grc. nia.nih.gov/branches/rrb/dna/comparativegcnomics.htm.

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loci hypothesis between schizophrenia and bi-polar disorder, as well as a re-evaluation of clinical classifications in both disorders [9–11,13,79]. Overlap of linkage results between related disorders could be explained at different loci a number of ways including coincidental overlap, hotspots of recombination in the genome [67], or functionally related gene clusters [34,72], among others. However, in some cases common alleles or common haplotypes may be involved.

subarachnoid hemorrhage, and age-associated memory impairment. Like overlapping linkage, genetic association of a single allele to multiple disorders is not uncommon and may become more apparent with increasing emphasis on genetic association studies. A comprehensive listing of genetic association studies of many disorders, including studies which association could not be demonstrated, can be found at the following address: http://geneticassociationdb.nih.gov.

Association

The combinatorial nature of common disorders

Genetic association of the same gene or the same allele of the same gene to multiple related disorders is common. Although statistically significant association does not prove disease causation, in some cases, the same allele may have the same or similar functional effect (depending upon genetic background) at a given locus in multiple disorders. Table 1 highlights selected association studies in which the same gene or the same allele has been associated to multiple disorders. PPARa and PPARc have fundamental effects in lipid metabolism [75]. Allelic variants of PPARa and PPARc are associated with altered lipid metabolism [68] that may manifest itself in traits such as higher body mass, levels of serum leptin, LDL cholesterol levels, inflammation, and atherosclerosis. Moreover, in the context of obesity and Type 2 diabetes, genes involved in these disorders have been referred to as “diabesity genes” [27] highlighting the overlapping functional influence of these alleles in these related disorders. Similarly, the A/G +49 polymorphism of the CTLA gene has been associated with multiple immune disorders including Type 1 diabetes, multiple sclerosis, Grave’s disease, Hashimoto’s thyroiditis, and rheumatoid arthritis. This CTLA polymorphism has been shown to have functional consequences resulting in altered proliferation of T cells in B7 costimulatory pathways [50]. Examples of other immune genes associated with multiple common immune disorders include TNFa, CD14, IL4, IL4R, IL6, IL10, IL13, VDR, CCR5, FCER1B, and IFNc, possibly, reflecting the importance of fundamental immune pathways in these disorders. Finally, APOE4, the prototypic common allele from the CD/CV hypothesis, has been associated with at least nine disorders suggesting influence of a single allele on related clinical outcomes [8,66]. These disorders include Alzheimer’s disease, multiple sclerosis, traumatic brain injury, sleepdisordered breathing, cardiovascular disease,

By definition, common disease susceptibility alleles should not be considered disease genes because although necessary, they are not sufficient to cause disease. These alleles are quite often more common in normal than in affected individuals in the general population. However, they should be considered independently assorting disease traits, and when analyzed at the molecular level they can be thought of as molecular variants of biochemical pathways. These molecular variants are components in complex multi-component networks that contribute in additive ways to the ultimate disease phenotype. Individually, they may have little or no disease effect. Two examples of association studies with an emphasis on clinically relevant quantitative molecular traits, as opposed to overt disease, are studies in asthma and Type 2 diabetes. In these disorders, there are relatively well-defined genetically determined clinical parameters influencing disease severity (e.g., total serum IgE levels, lipid levels, glucose metabolism). Variability in these parameters is most likely determined by variants in molecular pathways important in fundamental homeostatic or metabolic processes. Individually, they may not be sufficient to cause clinical symptoms, but through combinatorial interaction and in different environmental settings, they may lead to disease. In disorders where molecular parameters or surrogate markers have not been well identified such as Type 1 diabetes, inflammatory bowel disease, schizophrenia, or multiple sclerosis, loci are quite often referred to by disease designations (IDDM1, IBD1, etc.), giving the impression that they are more disease specific than they may be. When searching at the molecular level, the specific allelic phenotype may not be overt disease, but may more likely be subtle quantitative molecular variation.

Chrom.

Gene

LocusLink Disease/Phenotvpe

Allele

P-value

Ref.

PubMed ID

2 2 2 2 2 2 2

2q33.1 2q33.1 2q33.1 2q33.1 2q33.1 2q33.1 2q33.1

CTLA4 CTLA4 CTLA4 CTLA4 CTLA4 CTLA4 CTLA4

1493 1493 1493 1493 1493 1493 1493

Rheumatoid arthritis Grave’s disease Multiple sclerosis Hashimoto’s thyroiditis Type 1 diabetes mellitus Type 1 diabetes mellitus Type 1 diabetes mellitus

A/G49 A/G49 A/G49 A/G49 A/G49

Gonzalez MF 99 Yanagawa T 97 Harbo HF 99 Donner H 97 Takahiro A 99 Yanagawa T 99 Marron MP 97

10203024 9459626 10082437 9398726

A/G49

P ¼ 0:009 P ¼< 0:01 P ¼ 0:006 P ¼< 0:03 P ¼ 0:004 na P ¼ 0:00002

10052685 9259273

3 3 3 3 3 3 3 3

3p25 3p25 3p25 3p25 3p25 3p25 3p25 3p25

PPARc PPARc PPARc PPARc PPARc PPARc PPARc PPARc

5468 5468 5468 5468 5468 5468 5468 5468

Type 2 diabetes mellitus Higher body mass index Waist circumference Higher levels of serum leptin Plasma total cholesterol LDL-cholesterol HDL cholesterol/BMI Obesity

Genotype A12A/c1431c Pro12Ala variant Pro12Ala variant Pro12Ala variant Pro12Ala variant Pro12Ala variant TT Pro12Ala variant

P ¼ 0:038 P ¼ 0:015 P ¼ 0:028 P ¼ 0:022 P ¼ 0:01 P ¼ 0:004 P < 0:05 P ¼< 0:001

Evans D 01 Cole SA 00 Cole SA 00 Cole SA 00 Meirhaeghe A 00 Meirhaeghe A 00 Knoblauch H 99 Beamer BA 98

11409297 10805513 10805513 10805513 10702770 10702770 10591673 9792554

5 5 5 5 5 5 5 5 5

5q31.2 5q31.2 5q31.2 5q31.2 5q31.1 5q31.1 5q31.1 5q31.1 5q31.1

CD14 CD14 CD14 CD14 CD14 CD14 CD14 CD14 CD14

929 929 929 929 929 929 929 929 929

Atopy SPT Total IgE Total IgE Crohn’s disease IgE levels Expired myocardial infarction Alcoholic liver disease Atopy

C-159T C-159T C-159T C-159T C-159T C-159T C-159T C-159T C-159T

P ¼< 0:05 P ¼ 0:00091 P ¼ 0:018 P ¼ 0:01 P ¼ 0:005 P ¼ 0:004 P ¼< 0:01 P ¼ 0:005 P ¼< 0:01

Koppelman GH 01 Ober C 00 Gao PS 99 Baldini M 99 Klein W 02 Baldini M 99 Unkelbach K 99 Jarvelainen HA 01 Koppleman GH 01

11282774 11022011 10517256 10226067 11843056 10226067 10195920 11343243 11282774

6 6 6 6 6 6 6 6 6 6 6

6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31 6p21.31

TNFa TNFa TNFa TNFa TNFa TNFa TNFa TNFa TNFa TNFa TNFa

7124 7124 7124 7124 7124 7124 7124 7124 7124 7124 7124

Asthma PrimBilCirr Sepsis Psoriasis lep. Leprosy GVHD Silicosis SLE Celiac Disease Chronic Bronchitis Psoriasis

G/A )308 TNF2 G/A )308 TNF1 G/A )308 TNF2 G/A )308 TNF1 G/A )308 TNFd G/A )308 TNF1 G/A )308 TNF1 G/A )308 TNF1 G/A )308 TNF1 )238 TNF1

P ¼ 0:003 P ¼ 0:02 P ¼ 0:007 P ¼ 2:74
108 P ¼ 0:02 P ¼ 0:006 P ¼< 0:05 na P ¼< 0:001 P ¼< 0:01 P ¼ 1:64
107

Albuquerque R 98 Gordon M 99 Majetschak M 99 Arias A 97 Roy S 97 Middleton PG 98 Yucesoy B 01 Sullivan KE 97 McManus R 96 Huang S 97 Arias A 97

9645594 10453936 10450735 9395887 9237725 9808588 11264025 9416858 8655356 9372657 9395887

12q12

VDR

7421

Grave’s disease

Exon 2 initiation codon (VDR-FOK:1)

P ¼ 0.023

Ban Y 00

11134121

12

313

Ch band

The common variants/multiple disease hypothesis

Table 1 Selected genes associated with three or more diseases.

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Table 1 (continued) Chrom.

Ch band

Gene

LocusLink Disease/Phenotvpe

Allele

P-value

Ref.

PubMed ID

12 12 12 12 12

12q12 12q12 12q12 12q12 12q12

VDR VDR VDR VDR VDR

7421 7421 7421 7421 7421

Rheumatoid arthritis Multiple sclerosis Crohn’s disease Type 1 diabetes mellitus Calcific aortic valve stenosis

na P ¼ 0:0263 P ¼ 0:017 P ¼ 0:015 P ¼ 0:001

Garcia-Lozano JR 0 Fukazawa T 00 Simmons JD 00 Chang TJ 00 Ortlepp JR 01

11251690 10465499 10896912 10792336 11359741

12

12q12

VDR

7421

Prostate cancer

P ¼ 0:001

Correa-Cerro L 99

10987658

12

12q12

VDR

7421

Tuberculosis

BB/tt genotype bb tt Bsml Vitamin D receptor (Bsml B/b) Tt genotype of the Taql RFLP genotype TT/Tt and Vit D deficiency

P ¼ 0:008

Wilkinson RJ 00

10696983

17 17 17 17 17 17

17q23 17q23 17q23 17q23 17q23 17q23

ACE ACE ACE ACE ACE ACE

1636 1636 1636 1636 1636 1636

Insulin sensitivity Age-assoc. memory impairment Elite swimming Raynaud’s phenomenon/SLE Systemic sclerosis Coronary artery disease

II genotype D allele D allele DD 0 allele

P ¼ 0:01 P ¼ 0:25 P ¼ 0:004 P ¼ 0:002

Ryan AS 01 Bartres-Faz D 00 Woods D 01 Uhm WS 02 Fatini C 02 Alvarez R 01

11522714 10963892 11354635 12043886 12015245 11485372

19 19 19 19 19 19 19 19

19q13.2 19q13.2 19q13.2 19q13.2 19q13.2 19q13.2 19q13.2 19q13.2

APOE APOE APOE APOE APOE APOE APOE APOE

Multiple sclerosis Alzheimer’s disease Traumatic brain injury Temporal lobe epilepsy Subarachnoid hemorrhage Sleep-disordered breathing Cardiovascular disease Age-assoc. memory

APOE4 APOE4 APOE4 APOE4 APOE4 APOE4 APOE4 APOE4

P ¼ 0:0002 P ¼ 0:0001 P ¼ 0:026 P ¼ 0:004 P ¼ 0:0035 P ¼ 0:003 P ¼ 0:0086 na

Chapman 01 Zubenko GS 98 Lichtman SW 00 Briellmann RS Niskakangas T 01 Kadotani H 01 Lahoz C 01 Bartres-Faz D 99

11171894 9653640 11094110 10932283 11340230 11401610 11257253 10549798

22 22 22

22q13.31 PPARa 22q13.31 PPARa 22q13.31 PPARa

Coronary heart disease LDL-apolipoprotein B Coronary atherosclerosis

VaM 62 allele V162 allele L162V

P ¼ 0:005 P <¼ 0:02 P ¼ 0:0006

Lacquemant C 00 Vohl MC 00 Flavell DM 02

11119019 10828087 11914252

348 348 348 348 348 348 348 348 5465 5465 5465

Columns. Chromosome, Chromosomal band, Gene, LocusLink#, Disease or phenotype, Allele, P-value, Reference, PubMed ID#. na, not available.

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The common variants/multiple disease hypothesis

The common variant/multiple disease hypothesis As described above, overlapping linkage results and multiple genetic associations to the same gene or allele within clinically related disorders is not a rare finding. This suggests an extension of the CD/ CV hypothesis, namely, the common variant/multiple disease (CV/MD) hypothesis of common complex disorders. This states that the common alleles which contribute to a given disease under a certain combination of interacting genes and environmental conditions, may act in other genetic backgrounds influenced by other environmental factors resulting in different, possibly related clinical outcomes. Fig. 2 outlines a simple general model where genetic and environmental factors are shared between related disorders. Within a related class of disorder such as autoimmune or metabolic disorders there are loci and environmental factors unique to an individual disease as

Figure 2 Molecular Model of Genetic and Environmental Factor Overlap in common disorders. s, gene 1, 2, 3, 4, 5, etc.; }, environmental factor 1, 2, 3, 4, 5, etc.; , behavioral trait 1, 2, 3, 4, 5, etc.

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well as loci and factors common to both. For example, inactivity is common to both Type 2 diabetes and obesity. Both disorders are influenced by various diets, both may share loci determining glucose and lipid metabolism, ultimately contributing to disease. Similarly, in autoimmune disorders, non-disease specific allelic variation, regulating quantitative levels of a given cellular or molecular immune parameter such as IFNc transcription [14], soluble CD14 levels [2], or CD4:CD8 subsets [54] in combination with more disease specific alleles (e.g., MHC) or environmental triggers, may act cooperatively, contributing to disease processes. Inherent in this model is the notion that there are general loci or alleles that may not determine the incidence of disease but the level of response, influencing clinical severity [18,36]. In this way, independently assorting allelic variants exert their functional effects in the environmental context and the genetic background in which they are found. Unlike a simple genes plus environment genetic model, the CV/MD model emphasizes the overlap in etiological factors among related disorders. Common alleles having multiple disease outcomes has been described in various ways [7,21, 51,71] for individual diseases. It is essentially a restatement of genetic pleiotropy [32] in the context of common complex human disease. This notion is not surprising, given the fewer number of predicted genes in the human genome than previously thought [59]. A multifactorial allelic overlap genetic model between related disorders has a number of conceptual and clinical implications including broad ranges of disease severity in a given disorder, the clinical expression of related spectrum disorders, as well as parallel “epidemics” of related disorders in the developed world, such as asthma and atopic disease [23,25,62,77,82]. Additionally, it suggests broad based cooperative efforts [40] at regions of clinical interest throughout the genome, such as 5q31-33, 11p25, or 12p13. Under the CV/MD model, complex phenotypes and common complex diseases are not unique entities but are mosaics [15] of common disease specific alleles and non-disease specific modifying alleles in the population influenced by a vast array of environmental factors.

Acknowledgements The author thank Drs. David Schlessinger and John Hardy for critical reading of the manuscript.

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The common variants/multiple disease hypothesis

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