Open Access

Biobanking across the phenome - at the center of chronic disease research

BMC Public HealthBMC series ¿ open, inclusive and trusted201313:1094

DOI: 10.1186/1471-2458-13-1094

Received: 27 July 2012

Accepted: 25 September 2013

Published: 25 November 2013

Abstract

Background

Recognized public health relevant risk factors such as obesity, physical inactivity, smoking or air pollution are common to many non-communicable diseases (NCDs). NCDs cluster and co-morbidities increase in parallel to age. Pleiotropic genes and genetic variants have been identified by genome-wide association studies (GWAS) linking NCD entities hitherto thought to be distant in etiology. These different lines of evidence suggest that NCD disease mechanisms are in part shared.

Discussion

Identification of common exogenous and endogenous risk patterns may promote efficient prevention, an urgent need in the light of the global NCD epidemic. The prerequisite to investigate causal risk patterns including biologic, genetic and environmental factors across different NCDs are well characterized cohorts with associated biobanks. Prospectively collected data and biospecimen from subjects of various age, sociodemographic, and cultural groups, both healthy and affected by one or more NCD, are essential for exploring biologic mechanisms and susceptibilities interlinking different environmental and lifestyle exposures, co-morbidities, as well as cellular senescence and aging. A paradigm shift in the research activities can currently be observed, moving from focused investigations on the effect of a single risk factor on an isolated health outcome to a more comprehensive assessment of risk patterns and a broader phenome approach. Though important methodological and analytical challenges need to be resolved, the ongoing international efforts to establish large-scale population-based biobank cohorts are a critical basis for moving NCD disease etiology forward.

Summary

Future epidemiologic and public health research should aim at sustaining a comprehensive systems view on health and disease. The political and public discussions about the utilitarian aspect of investing in and contributing to cohort and biobank research are essential and are indirectly linked to the achievement of public health programs effectively addressing the global NCD epidemic.

Keywords

Comorbidities Cohort Genome wide association study Non-communicable disease Phenome Public health Risk factors

Background

The aim of the present report is to address the importance of studying non-communicable diseases (NCDs) and their relationship to aging in a systems approach. Understanding the complexity and interrelation of risk factors and disease networks requires the biologic sample collection, detailed and comprehensive phenotyping, and broad risk factor data. We present the international progress made in establishing large population-based biobank cohorts with the explicit aim to investigate non-communicable disease (NCD) etiology longitudinally. We point to the current inadequacy and the critical need to invest substantial research funding into NCD research in low and middle income countries in which the rise of NCDs converges with the high prevalence of infectious diseases. We discuss the relevance of studying pathophysiologic mechanisms linking different age-related NCDs and the aging process. We also highlight recent examples of phenome approaches. Finally, we point out striking pleiotropic findings of NCD phenotypic traits and genome-wide associations (GWAS) which clearly signpost an on-going paradigm shift in NCD research and underscore the potential of agnostic, complex data, systemic and multi-levelled methodologies leading to new understanding of chronic disease etiology.

International trend for prospective large-sized biobank cohorts

NCDs convey more than 50% of the global burden of disease and are challenging the health of populations worldwide. In high income countries (HICs), the epidemic of NCDs has been recognizably the major public health challenge over the last decades [1]. For this reason several HICs have increased their research efforts and invested substantial funding in extremely large population-based prospective cohort studies (with samples sizes over 200′000). These mega-cohorts (Table 1; see also http://​www.​p3g.​org) apply detailed phenotype descriptions over time, exhaustive temporal assessment of personal and environmental information and include high quality biologic sample collections for future genetic and functional analyses [2]. Prospective biobanking represents a powerful tool for establishing causal relationships as the time-order of sampling and of phenotyping is generally clear. Both hypothesis-driven as well as agnostic research can be conducted. Biologic samples in research can be used to test genetic determinants (e.g. genetic variants of N-Acetyltransferases, NAT1 and NAT2) potentially mediating susceptibility (e.g. increased cancer risk), to discover or validate biomarkers as land marks of mechanisms (e.g. acetylation of aromatic and heterocyclic amines), or to sense and estimate individual environmental exposures (e.g. variable toxicity of carcinogens). Additional applications are expected to increase in the future. General good practices for biobanking in research have been defined (e.g. http://​www.​ieaweb.​org for epidemiologic settings). An increasing need for biologic samples has therefore been the driving force to establish biobank collections in various clinical and observational settings [3]. A well-known example is the UK biobank, collecting blood, saliva and urine of more than 500′000 participants. Questionnaire and measurement data were collected at baseline and follow-up examinations are performed in subsets of the cohort [4]. There are also efforts of similar dimension in low and middle income countries (LMIC) such as the Chinese Biobank Study [Kadoorie Study of Chronic Disease in China (KSCDC)]. This project is a blood-based health database aiming at collecting genetic, environmental and lifestyle data on 510′000 adults aged 30–79 years to understand the causes, risk factors, pathogenesis, prevalence patterns and trends of major infectious and NCDs [5]. The establishment, maintenance and repeated collection of participant data require a substantial long-term investment of research funds. Critical voices point to the tremendous costs and to the methodological challenges to keep bias low over a long follow-up time in a multi-centric study design. But the scientific utility of such large longitudinal datasets is undisputed [6, 7]. Understanding the genetic, molecular and mechanistic background of interdependence between NCDs, comorbidities and risk factors during the aging process is a research priority for public health. Sufficiently powered cohorts and biobanks with broad, yet refined characterization of participants for risk factors and health phenotypes are a conditio sine qua non to achieve this goal.
Table 1

Selection of ongoing mega-cohort studies in adults

Web site

Cohort study

Country

Country size

Focus

Sample size

Baseline

Biologic samples

--

CONOR/HUNT

Norway

4,9 Mio

Common disease etiology

185′000

1994-1995

Blood

http://​www.​millionwomenstud​y.​org/​

Million women study

United Kingdom

62,3 Mio

Women’s health

1′300′000

1996 - 2001

Blood, saliva, in a sub-sample

http://​epic.​iarc.​fr/​

EPIC

Europe

738,2 Mio

Nutrition, life style and cancer other diseases

520′000

1997

Blood

--

Mexico city prospective study

Mexico

117,4 Mio

Major determinants of morbidity and premature mortality

150′000

1998-2004

Blood

http://​www.​decode.​com/​research/​

deCODE

Iceland

0.4 Mio

Research company

200′000

2000

Various

http://​www.​milleniumcohort.​org/​

Millennium

USA

313,3 Mio

US military family cohort

150′000

2001

Not specified

http://​www.​geenivaramu.​ee/​en/​

Estonian biobank

Estonia

1.3 Mio

Biologic resource

50′000

2002

Blood

http://​www.​birmingham.​ac.​uk/​research/​activity/​mds/​projects/​HaPS/​PHEB/​Guangzhou/​index.​aspx

Guanghzou Biobank Cohort study

China

1339,7 Mio

Genetic, lifestyle, occupational and environmental factors, and life course Causes of the common chronic diseases

40′000

2003

Blood, urine

http://​www.​ckbiobank.​org/​

China Kandoorie Biobank

China

1339,7 Mio

Chronic disease etiology, complex interplay of lifestyle, environmental, and genetic susceptibility

500′000

2004-2008

Blood

http://​www.​phri.​ca/​pure/​index.​html

PURE

Several countries

3223,7 Mio

Maladaptation to urbanization and cardiovascular health

120′000

2006

Blood

http://​www.​ukbiobank.​ac.​uk/​

UK biobank

United Kingdom

62,3 Mio

Common disease etiology

500′000

2007 - 2010

Blood, saliva, urine

http://​www.​lifelines.​net/​

LifeLines

The Netherlands

16,8 Mio

causes and prognosis of burden of disease, co-determinants, rather than comorbidity, family study

165′000

2007

Blood, urine

https://​www.​etude-nutrinet-sante.​fr/​fr/​common/​login.​aspx

Nutrinet Santé

France

65,4 Mio

Nutrition and health

500′000

2009

Blood, urine

http://​www.​partnershipforto​morrow.​ca/​

The Canadian Partnership for Tomorrow Project (CPTP)

Canada

34,4 Mio

Cancer and chronic disease etiology

300′000

2009

Blood

http://​lifegene.​ki.​se/​

Life gene

Sweden

9,4 Mio

Nealth and lifestyle

500′000

2011

Blood, urine

http://​www.​constances.​fr

CONSTANCES

France

65,4 Mio

Biologic and research resource

500′000

2011

Blood

http://​www.​nationale-kohorte.​de/​index_​en.​html

German national cohort

Germany

81,8 Mio

Common disease etiology

200′000

2012

Blood

Listed by date of baseline examination start.

Discussion

Biobank cohorts and chronic disease research in low and middle income countries (LMIC)

Research on NCDs almost exclusively relies on cohort data and associated biological samples collected in HICs. The recent update of global burden of disease estimates marks a shift from communicable towards non-communicable diseases and from life years lost due to premature death to increased number of years lived with chronic diseases and disabilities in LMICs [1, 8]. Though regionally heterogeneous, the LMICs show a persistently high proportion of infectious diseases in addition to a recent increase in prevalence of NCDs such as ischemic heart disease, stroke and diabetes. This observed convergence of NCDs and communicable diseases causes a dual burden of disease [9] for which most LMICs not only lack adequate health system resources, but also research funds to address the regional and local public health challenges [10]. Though causal relationships of NCD etiology and preventive measures identified in population-based biobank cohort studies in HICs will most likely inform public health decisions in LMICs, it is obvious that repeating studies of established NCD risk factors in LMICs will be necessary for proper estimation of their contribution to the disease burden [11]. Much can be learned about effect modifiers and risk factors by paralleled establishment of biobank cohorts in different settings. From human genome variation studies we already know that many African populations harbour a larger degree of genetic variation [12]. Several examples of high quality cohort study efforts in LMIC have been undertaken [7] (Table 1). For example, the prevalence of healthy lifestyle in patients with cardiovascular disease (n = 7519) was investigated in the PURE study, a large-scale epidemiological study that recruited >140,000 individuals residing in in 17 low-, middle-, and high-income countries around the world, and revealed strong correlation of decreasing levels of healthy lifestyle with decreasing country income level [13]. The Guangzhou Biobank Cohort Study [14], combining the use of biomarkers and questionnaire data for investigation of NCDs health system use as well as NCDs etiology, is another excellent example of a regional population-based cohort study in a country transiting fast from low to high income settings, albeit with large social discrepancies. Such large scale biobank cohort studies in LMICs face numerous challenges including funding, political, cultural and religious issues, but they are imminently important to collect data and monitor the dynamics of changes in environmental, life style, societal and health parameters with the increasing trend of urbanization in these countries [7]. They also contribute importantly to increasing the global competitiveness of research in LMICs [15].

Phenome approach towards disease networks

In aiming to improve understanding of NCD etiology refined phenotyping of specific health outcomes is a necessity. Clinical disease diagnosis based research is known to be challenged by phenotypic heterogeneity. As an example, asthma, an intermittent chronic respiratory disease can be defined as a clinical diagnosis of asthma, but it is known that there are important differences in etiology and mechanisms depending on age of asthma onset or the presence of atopy and allergies. GWAS findings clearly revealed that the locus 17q21 determined childhood and not adult onset asthma [16, 17]. Statistical clustering approaches applied to the multilayer disease characteristics of a large group of asthmatic patients identified four distinct asthma phenotype groups: active treated allergic childhood-onset asthma; active treated adult-onset asthma; inactive or mild untreated asthma differing by atopy status and age of asthma onset [18]. In general up to recently, genetic investigations of NCD determinants, especially in large-scale GWAS meta-analyses, reduced the phenotype studied to a clinical diagnostic entity, a fact that may contribute to the disappointingly low predictive power of common genetic disease variants identified to date [1921]. The importance of precise phenotyping for identifying the genetic contribution to common disease has been stressed since the time point of completion of the human genome project [22]. Clearly this challenges meta-analyses of data from different medium-sized cohorts collected in non-harmonized ways. International efforts to develop harmonized phenotype definitions lead early on to the Human Phenome Project [22, 23]. Since the initiative call phenome based databases were established (e.g. bipolar disorder phenome [24]; epilepsy phenome/genome project [25]; mouse phenome [26]; human pathology centered phenomes on cardiomyopathy [27], deafness [28], cardiac conduction characteristics [29], human skeletal phenome [30]). Phenotypes forming the basis of the phenome approaches can refer to any characteristic or trait measureable in an organism. It can be as diverse as a morphologic, biochemical, physiological, electrical, behavioral, epigenetic trait and these measures show a large inter-individual variability. Recently phenome-based approaches proofed their usefulness in identifying context-dependent clinical reference values for white blood cell counts [31]. Other recent phenome approaches applied semantic web technologies to scan electronic health records comprising clinical and biologic medical data for identifying genotype-phenotype associations [32, 33]. The current applications of the phenome approaches illustrate well the broad definition of the “phenome” summarizing often a large collection of phenotypes. Refined phenome approaches must be expanded to the concept of disease networks [34, 35], the Diseasome. According to a European population-based survey 25% of the respondents of age older than 14 years reported the presence of more than one chronic condition [36]. A systematic evidence review reported prevalence ranges of multi-morbidity in elderly of 55% to 98% [37]. The identification and clustering of human disease etiologic factors was undertaken in a bioinformatic driven data-mining approach using MeSH annotation of MEDLINE-referenced articles and the authors produced the etiome profile for 863 diseases (available at http://​etiome.​stanford.​edu) [38]. New analytical approaches open novel exploratory avenues of investigation supporting the paradigm shift towards systematic, multi-layered and more exhaustive phenotypic catalogs. Patient records from a 1.5 million large patient population were used to establish correlation links of 161 disorders with disease phenotypes allowing to estimate the genetic overlap within the disease network [39]. A comorbidity database, the human disease network, was established from the analysis of 30 Mio Medicare patient data linking diseases and comorbidities (available at http://​hudine.​neu.​edu/​) [40]. More recently, to better understand disease similarities independent research groups have explored the clustering of genome-phenome correlations on a large number of published phenotype – gene associations [41], or the type 2 diabetes genetic loci [42] or the major histocompatibility complex class II surface receptor, HLA-DRB1 [43]. These reports clearly proof the huge potential of bioinformatics-driven data-mining methodologies to shape the diseasome by classification of disease phenotypes and molecular diseases pathways. Thus such public health relevant research will continue to steadily improve our understanding of the phenotypic overlap of different NCDs and their link to aging processes. These system approaches to disease must furthermore be paralleled by systems approaches to understand risk factors. The concept of the phenome has thus been supplemented by the concept of the Exposome which measures environmental exposure as internal intermediate phenotypes of exposed organisms [4446] using metabolomic and proteomic methods for quantification of molecular traits.

Accelerated aging processes as a link to NCD comorbidity

Given that NCDs are chronic the proportion of comorbidities or secondary NCDs increase with age. Beyond this play of chance, NCD risk factors are known to accelerate the aging process of various organs. Smoking and obesity are among the most consistent factors showing adverse effects on all features of aging. For example, smoking, a potent risk factor for cardiovascular and respiratory NCDs has been suggested to promote cellular senescence of the lung [47], to impair the immune response [48] and increase skin aging [49]. Likewise obesity, a major risk factor for cardiovascular NCDs has been associated with age-related disease of the CNS [50]. Telomere shortening, a marker of the aging process, is inversely associated with several risk factors of diabetes and mitochondrial function in diabetic patients compared to healthy controls [51]. Telomere length was positively correlated with good glycemic/lipid control and negatively correlated with adiposity and insulin resistance [51, 52]. Other NCD risk factors such as sun light or weight loss exhibit adverse effects on more restricted features of aging such as skin aging or osteoporosis (see Table 2 as illustrative example).
Table 2

Risk factors of NCDs and aging

Risk factor studied

Disease or trait

Acclerated aging and impaired function

Reference

Smoking

Humoral immunity

Immune system aging

[48]

 

Inflammatory response

Immune system aging

[53]

 

Heart rate variabiltiy

Autnomous nervous system aging

[54]

 

Alzheimer

Premature cognitive impairment, CNS aging

[55]

 

Atherosclerosis

Cardiovascular aging

[56]

 

Elastosis of the neck

Skin aging

[57]

 

Bone mineral density

Bone aging

[58]

Obesity, BMI, high calorie intake,

Impaired immune response

Immune system aging

[59]

Waist-hip ratio, skin-folds,

CD8 Tcell activation

Immune system aging

[59]

Body weight

Lipodystrophy

Adipocyte aging

[60]

 

Heart rate variabiltiy

Autnomous nervous system aging

[61]

 

Alzheimer

Premature cognitive impairment, CNS aging

[55]

 

Atherosclerosis

Cardiovascular aging

[56]

 

Alopecia

Hair aging

[62]

 

Bone mineral density

Bone aging

[58]

Dyslipidemia

Atherosclerosis

Cardiovascular aging

[56]

 

Alopecia

Hair aging

[62]

History of diabetes

Alzheimer

Premature cognitive impairment, CNS aging

[55]

 

Bone mineral density

Bone aging

[58]

Hypertension,

Alzheimer

Premature cognitive impairment, CNS aging

[55]

High resting pulse

Atherosclerosis

Cardiovascular aging

[56]

 

Osteoporosis

Bone aging

[63]

 

Bone mineral density

Bone aging

[58]

Other chronic diseases,

Immunosenescence

Immune system aging

[64]

Comorbidity

Lipodystrophy

Adipocyte aging

[65]

 

Atherosclerosis

Cardiovascular aging

[56]

 

Sacropenia

Muscle aging

[66]

 

Osteoporosis

Bone aging

[63]

Medication intake

Sacropenia

Muscle aging

[66]

 

Osteoporosis

Bone aging

[63]

UV light/sun exposure

Alopecia

Hair aging

[62]

Low sun exposure

Elastosis of the neck

Skin aging

[57]

 

Sacropenia

Muscle aging

[66]

Health behaviours

Alzheimer

Premature cognitive impairment, CNS aging

[55]

Low level of mental activity

Atherosclerosis

Cardiovascular aging

[56]

Physical inactivity

Sacropenia

Muscle aging

[66]

 

Osteoporosis

Bone aging

[63]

Depression

Atherosclerosis

Cardiovascular aging

[56]

Poor diet

Sacropenia

Muscle aging

[66]

Weight loss/no weight gain

Osteoporosis

Bone aging

[63]

Low education

Alzheimer

Premature cognitive impairment, CNS aging

[55]

 

Atherosclerosis

Cardiovascular aging

[56]

Psychosocial factors

Alzheimer

Premature cognitive impairment, CNS aging

[55]

Content of table is illustrative, not exhaustive.

The natural history of aging is characterized by a diminished self-renewal capacity of the organism resulting in sclerodermatous changes of the skin, alopecia, osteoporosis, sarcopenia, muscle atrophy, generalized lipodystrophy, atherosclerosis, decreased elasticity of the vascular system, immunologic senescent changes such as decline in humoral immunity, T-cell functional dysregulation, innate and adaptive immune functions [48, 59, 64, 67]. Characteristic land marks of aging are also neurologic senescent changes of the central, peripheral and autonomic nervous system including limited neuronal loss, glial proliferation in the cortex and an overall brain weight decrease, degradation of sensory performance, decline in proprioception and somatosensory information processing and also reduced reactivity of the sympathetic and the parasympathetic nervous activity [68, 69]. It is likely that systemic approaches combining the focus on accelerated aging, NCDs, environmental and genetic risk factors will point to the underlying disease biology. Understanding how shared risk factors affect mechanisms common to NCDs and aging processes is important from a public health perspective to meet effective prevention programs.

Lessons learned from genetics on NCD clustering: pleiotropic gene variants

Despite ongoing debates about the limitation of GWAS findings from the predictive personalized medicine perspective, GWAS studies do not announce the end of complex disease genetics, but rather a promising first step. Completely novel genes expand our understanding of NCD pathology. A large number of GWAS loci have been consistently associated with one or multiple NCDs in independent populations (see Additional file 1; http://​www.​genome.​gov/​gwastudies). Evidence for pleiotropy of loci, genes and even specific SNPs suggests important mechanistic links between diseases and is of potential relevance to advance understanding the biology of NCD clusters, co-morbidities and aging processes. A recent meta-analysis of 372 GWAS on 105 unique age-related diseases revealed the clustering of genetic variants in ten significantly enriched chromosomal locations which contain genes involved in inflammation and cellular senescence [70]. Pleiotropy is defined as a genetic variant or a gene having an effect on multiple phenotypes. In Table 3, we present an overview of specific SNPs likely to be pleiotropic. They were consistently associated with different forms of cancer (i.e. rs401681, TERT, CLPTM1L, 5p15.33 – associated with lung, bladder, pancreatic cancer, melanoma and prostate-specific antigen levels) and of chronic inflammatory diseases (i.e. rs11209026, IL23R, 1p31.3 – associated with Crohn’s disease, ulcerative colitis, ankylosing spondylitis and psoriasis; rs10488631, IRF5,TNPO3, 7q32.1 – associated with systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis and primary biliary cirrhosis; see Additional file 2 for detailed summary of pleiotropic SNPs). This observed non-random clustering of NCD-linked traits and specific pleiotropic SNPs can be used to identify biologic mechanisms shared by different NCDs. In a recent study a method was presented to evaluate the pleiotropy among GWAS-identified SNPs and genes for common complex disease and traits; it reported that 17% of the GWAS genes and 4% of the GWAS SNPs showed evidence of pleiotropy [71]. Although pleiotropy had been suggested to be common to the genetic architecture of complex disease [72], only isolated cases of pleiotropy had been reported previously such as the links between APOE genotypes and dyslipidemia, coronary heart disease and Alzheimer's disease [73], and type 2 diabetes and prostate cancer (TCF2 genotypes) [74]. The genetic overlap between psoriasis, diabetes type 2 and Crohn’s disease, three inflammatory diseases affecting distinct organs, was identified by combining evidence from linkage and GWAS data [75]. Recently antagonistic pleiotropic effects of genetic variants were evidenced conferring risk for one disease, diabetes type 1, and protection for another disease, inflammatory bowel disease [76].
Table 3

Pleiotropic GWAS loci of NCDs

Locus, gene

dbSNP ID

NCD entity associated with SNP

P-value

Risk allele frequency

PubMed ID

Cancer linked NCDs cluster

5p15.33, TERT

rs2736100

Glioma

2.00E-17

0.49

19578367

 

rs2736100

Glioma

1.00E-14

NR

21531791

 

rs2736100

Glioma

7.00E-09

NR

21827660

 

rs2736100

Hematological and biochemical traits

3.00E-08

0.4

20139978

 

rs2736100

Idiopathic pulmonary fibrosis

3.00E-08

0.41

18835860

 

rs2736100

Lung adenocarcinoma

2.00E-22

0.39

20700438

 

rs2736100

Lung adenocarcinoma

3.00E-11

0.39

20871597

 

rs2736100

Lung cancer

1.00E-27

0.41

21725308

 

rs2736100

Testicular germ cell cancer

8.00E-15

0.49

20543847

5p15.33, TERT, CLPTM1L

rs401681

Bladder cancer

5.00E-07

0.54

20972438

 

rs401681

Lung cancer

8.00E-09

NR

18978787

 

rs401681

Melanoma

3.00E-08

0.46

21983787

 

rs401681

Pancreatic cancer

7.00E-07

0.45

20101243

 

rs401681

Serum prostate-specific antigen levels

1.00E-10

0.55

21160077

8q24.21, Intergenic

rs6983267

Colorectal cancer

1.00E-14

0.49

17618284

 

rs6983267

Colorectal cancer

7.00E-11

0.48

18372905

 

rs6983267

Colorectal cancer

2.00E-08

0.34

21242260

 

rs6983267

Prostate cancer

9.00E-13

0.5

17401363

 

rs6983267

Prostate cancer

9.00E-13

0.49

18264097

 

rs6983267

Prostate cancer

7.00E-12

0.53

18264096

 

rs6983267

Prostate cancer

9.00E-06

NR

21743057

9p21.3, CDKN2A, CDKN2B

rs4977756

Glaucoma

1.00E-14

0.6

21532571

 

rs4977756

Glioma

7.00E-15

0.6

19578367

Inflammatory trait linked NCDs cluster

1p31.3, IL23R

rs11209026

Ankylosing spondylitis

2.00E-17

0.93

21743469

 

rs11209026

Ankylosing spondylitis

9.00E-14

0.94

20062062

 

rs11209026

Crohn’s disease

1.00E-64

0.93

21102463

 

rs11209026

Crohn’s disease

4.00E-21

NR

22293688

 

rs11209026

Crohn’s disease

2.00E-18

0.92

17447842

 

rs11209026

Inflammatory bowel disease

4.00E-11

0.93

17068223

 

rs11209026

Inflammatory bowel disease

7.00E-11

0.94

18758464

 

rs11209026

Psoriasis

7.00E-07

NR

20953190

 

rs11209026

Ulcerative colitis

5.00E-28

0.94

21297633

 

rs11209026

Ulcerative colitis

3.00E-10

NR

19915572

 

rs11209026

Ulcerative colitis

1.00E-08

0.93

19122664

1p13.2, PTPN22

rs2476601

Crohn’s disease

1.00E-08

0.9

18587394

 

rs2476601

Rheumatoid arthritis

9.00E-74

0.1

20453842

 

rs2476601

Rheumatoid arthritis

2.00E-21

NR

19503088

 

rs2476601

Rheumatoid arthritis

2.00E-11

0.1

17804836

 

rs2476601

Type 1 diabetes

9.00E-85

NR

19430480

 

rs2476601

Type 1 diabetes

2.00E-80

0.09

17554260

 

rs2476601

Type 1 diabetes

1.00E-07

0.09

17632545

 

rs2476601

Type 1 diabetes autoantibodies

2.00E-111

NR

21829393

 

rs2476601

Vitiligo

1.00E-07

0.1

20410501

7q32.1, IRF5,TNPO3

rs10488631

Primary biliary cirrhosis

3.00E-10

0.11

20639880

 

rs10488631

Primary biliary cirrhosis

2.00E-07

NR

19458352

 

rs10488631

Rheumatoid arthritis

4.00E-11

0.11

20453842

 

rs10488631

Systemic lupus erythematosus

7.00E-18

0.11

21408207

 

rs10488631

Systemic lupus erythematosus

2.00E-11

0.12

18204098

 

rs10488631

Systemic sclerosis

2.00E-13

NR

20383147

 

rs10488631

Systemic sclerosis

2.00E-10

NR

21779181

 

rs10488631

Systemic sclerosis

2.00E-07

NR

21779181

 

rs10488631

Systemic sclerosis

4.00E-07

0.09

21750679

18p11.21, PTPN2

rs2542151

Crohn’s disease

5.00E-17

0.15

18587394

 

rs2542151

Crohn’s disease

3.00E-08

0.18

17554261

 

rs2542151

Crohn’s disease

2.00E-07

0.16

17554300

 

rs2542151

Type 1 diabetes

1.00E-14

0.16

17554260

 

rs2542151

Type 1 diabetes

9.00E-08

NR

18978792

 

rs2542151

Type 1 diabetes autoantibodies

4.00E-13

NR

21829393

18p11.21, PTPN2

rs1893217

Celiac disease

3.00E-10

0.17

20190752

 

rs1893217

Celiac disease and Rheumatoid arthritis

5.00E-12

NR

21383967

 

rs1893217

Type 1 diabetes

4.00E-15

NR

19430480

Cardiovascular trait linked NCDs cluster

2p23.3, GCKR

rs1260326

Cardiovascular disease risk factors

2.00E-08

0.4

21943158

 

rs1260326

Cholesterol, total

7.00E-27

0.41

20686565

 

rs1260326

Chronic kidney disease

3.00E-14

0.41

20383146

 

rs1260326

C-reactive protein

5.00E-40

NR

21300955

 

rs1260326

Hematological and biochemical traits

4.00E-09

0.44

20139978

 

rs1260326

Hypertriglyceridemia

7.00E-09

0.41

20657596

 

rs1260326

Liver enzyme levels (gamma-glutamyl transferase)

4.00E-13

0.38

22001757

 

rs1260326

Metabolic traits

4.00E-10

0.35

19060910

 

rs1260326

Platelet counts

9.00E-10

NR

22139419

 

rs1260326

Serum metabolites

3.00E-18

NR

22286219

 

rs1260326

Triglycerides

6.00E-133

0.41

20686565

 

rs1260326

Triglycerides

2.00E-31

0.45

19060906

 

rs1260326

Two-hour glucose challenge

3.00E-10

NR

20081857

 

rs1260326

Waist circumference and related phenotypes

4.00E-08

NR

18454146

11q12.2, FADS1, FADS2

rs174547

HDL cholesterol

2.00E-12

0.33

19060906

 

rs174547

Lipid metabolism phenotypes

8.00E-262

NR

22286219

 

rs174547

Metabolic traits

9.00E-116

0.32

21886157

 

rs174547

Phospholipid levels (plasma)

4.00E-154

NR

21829377

 

rs174547

Phospholipid levels (plasma)

3.00E-64

NR

21829377

 

rs174547

Resting heart rate

2.00E-09

0.33

20639392

 

rs174547

Serum metabolites

7.00E-179

0.3

20037589

 

rs174547

Triglycerides

2.00E-14

0.33

19060906

11q14.3, MTNR1B

rs1387153

Fasting plasma glucose

2.00E-36

0.29

19060909

 

rs1387153

Glycated hemoglobin levels

4.00E-11

0.28

20858683

 

rs1387153

Metabolic syndrome (bivariate traits)

2.00E-09

NR

21386085

 

rs1387153

Metabolic syndrome (bivariate traits)

8.00E-09

NR

21386085

 

rs1387153

Type 2 diabetes

8.00E-15

NR

20581827

12q24.12, ALDH2, BRAP

rs671

Coronary heart disease

2.00E-34

0.23

21971053

 

rs671

Drinking behavior

4.00E-211

0.75

21372407

 

rs671

Esophageal cancer

3.00E-24

NR

19698717

 

rs671

Hematological and biochemical traits

7.00E-10

0.26

20139978

 

rs671

Hematological and biochemical traits

5.00E-09

0.26

20139978

 

rs671

Intracranial aneurysm

3.00E-06

0.75

22286173

 

rs671

Triglycerides

2.00E-06

NR

22171074

16q13, CETP

rs3764261

Age-related macular degeneration

7.00E-09

0.33

21665990

 

rs3764261

Age-related macular degeneration

7.00E-07

0.32

20385819

 

rs3764261

Cholesterol, total

7.00E-14

0.32

20686565

 

rs3764261

HDL cholesterol

2.00E-57

0.31

18193043

 

rs3764261

HDL cholesterol

7.00E-29

0.28

19060910

 

rs3764261

HDL cholesterol

3.00E-12

0.2

19359809

 

rs3764261

HDL cholesterol

7E-380

0.32

20686565

 

rs3764261

LDL cholesterol

9.00E-13

0.32

20686565

 

rs3764261

Lipid metabolism phenotypes

1.00E-36

NR

22286219

 

rs3764261

Metabolic syndrome

1.00E-48

0.36

20694148

 

rs3764261

Metabolic syndrome

3.00E-13

NR

21386085

 

rs3764261

Triglycerides

1.00E-12

0.45

20686565

 

rs3764261

Waist circumference

1.00E-27

NR

18454146

19p13.2, LDLR

rs6511720

Cardiovascular disease risk factors

5.00E-11

0.11

21943158

 

rs6511720

Carotid intima media thickness

1.00E-07

NR

21909108

 

rs6511720

Cholesterol, total

7.00E-97

0.11

20686565

 

rs6511720

LDL cholesterol

4.00E-117

0.11

20686565

 

rs6511720

LDL cholesterol

2.00E-51

0.1

18193044

 

rs6511720

LDL cholesterol

2.00E-26

0.1

19060906

 

rs6511720

LDL cholesterol

4.00E-26

0.9

18193043

 

rs6511720

Lp-PLA2 activity and mass

3.00E-11

0.1

22003152

19q13.32, APOE, APOC1

rs4420638

Alzheimer’s disease

2.00E-44

NR

17998437

 

rs4420638

Alzheimer’s disease

1.00E-39

NR

17975299

 

rs4420638

Alzheimer’s disease (age of onset)

1.00E-12

NR

22005931

 

rs4420638

Alzheimer’s disease (late onset)

1.00E-39

NR

17474819

 

rs4420638

Cholesterol, total

5.00E-111

0.17

20686565

 

rs4420638

Cognitive decline

4.00E-27

NR

22054870

 

rs4420638

C-reactive protein

9.00E-139

NR

21300955

 

rs4420638

C-reactive protein

5.00E-27

NR

19567438

 

rs4420638

C-reactive protein

3.00E-07

0.9

21196492

 

rs4420638

HDL cholesterol

4.00E-21

0.17

20686565

 

rs4420638

LDL cholesterol

9.00E-147

0.17

20686565

 

rs4420638

LDL cholesterol

1.00E-60

0.2

18193044

 

rs4420638

LDL cholesterol

3.00E-43

0.18

18193043

 

rs4420638

LDL cholesterol

2.00E-40

0.18

20864672

 

rs4420638

LDL cholesterol

4.00E-27

0.16

19060906

 

rs4420638

LDL cholesterol

1.00E-20

0.18

18262040

 

rs4420638

LDL cholesterol

2.00E-07

NR

18802019

 

rs4420638

Lp-PLA2 activity and mass

5.00E-30

0.84

22003152

 

rs4420638

Lp-PLA2 activity and mass

6.00E-24

0.16

20442857

 

rs4420638

Longevity

2.00E-16

0.81

21740922

 

rs4420638

Quantitative traits

3.00E-07

0.21

19197348

 

rs4420638

Triglycerides

3.00E-13

0.22

17463246

Cardiovascular & inflammatory trait linked NCDs cluster

12q24.12, SH2B3

rs3184504

Coronary heart disease

6.00E-06

0.44

21378990

 

rs3184504

Diastolic blood pressure

4.00E-25

0.47

21909115

 

rs3184504

Diastolic blood pressure

3.00E-14

0.48

19430479

 

rs3184504

Eosinophil counts

7.00E-19

0.38

19198610

 

rs3184504

Rheumatoid arthritis

6.00E-06

0.51

20453842

 

rs3184504

Systolic blood pressure

5.00E-09

0.48

19430479

 

rs3184504

Type 1 diabetes

3.00E-27

NR

19430480

 

rs3184504

Type 1 diabetes autoantibodies

2.00E-38

NR

21829393

12q24.12, SH2B3, ATXN2

rs653178

Blood pressure

7.00E-20

0.59

21909110

 

rs653178

Celiac disease

7.00E-21

0.5

20190752

 

rs653178

Celiac disease

8.00E-08

0.48

18311140

 

rs653178

Celiac disease and Rheumatoid arthritis

3.00E-19

NR

21383967

 

rs653178

Chronic kidney disease

4.00E-11

0.5

20383146

 

rs653178

Diastolic blood pressure

3.00E-18

0.53

19430483

Skin pigmentation linked NCDs cluster

11q14.3, TYR

rs1393350

Blue vs. green eyes

3.00E-12

0.23

17952075

 

rs1393350

Eye color

3.00E-09

0.27

20585627

 

rs1393350

Melanoma

2.00E-14

0.27

19578364

 

rs1393350

Melanoma

2.00E-13

0.28

21983787

 

rs1393350

Skin sensitivity to sun

2.00E-06

0.27

17952075

 

rs1393350

Tanning

2.00E-13

NR

19340012

 

rs1393350

Vitiligo

2.00E-18

0.73

20410501

16q24.3, MC1R

rs1805007

Basal cell carcinoma

4.00E-17

0.07

21700618

 

rs1805007

Blond vs. brown hair color

2.00E-13

0.08

17952075

 

rs1805007

Freckles

1.00E-96

0.05

17952075

 

rs1805007

Red vs non-red hair color

2.00E-142

NR

17952075

 

rs1805007

Skin sensitivity to sun

2.00E-55

0.06

17952075

A selection of GWAS identified pleiotropic SNPs implicated in more than one NCD entity are presented here. For a more complete list of pleiotropic loci see Additional file 1. Data has been downloaded (09 March 2012) from the online catalogue of published GWAS available at http://​www.​genome.​gov/​gwastudies.

NR, not reported.

Lp-PLA2, lipoprotein-associated Phospholipase A2.

LDLR, Low density lipoprotein receptor.

HDL, High density lipoprotein.

LDLR, Low density lipoprotein.

Methodological challenge of data mining and of complex systems analysis

The research community is facing unprecedented statistical, data mining and analytical challenges as the next steps ahead are complex interaction studies of genes, other –omics markers, lifestyle, and environment on the phenome. Standard statistical approaches using linear causal relationships have shown to be limited for reproducible association studies on complex phenotypes as well as for two-way interaction analyses. Researchers will need to adapt their current methods by implementing approaches that reflect more closely the dynamics of adaptive biologic systems by taking non-linear and non-proportional relationships into account. Methods of complex system science and chaos theory have been applied to various biologic systems [77] and have been proposed to be applied to human health behavioral changes for public health prevention aims [78]. Fractal dynamics in physiology have shown to be relevant to disease and aging [79], to biologic signals in general [80] and chaotic motifs have been investigated in dynamic behavior of gene regulatory networks [81]. To date we have only started to investigate disease clusters and pleiotropic risk effects in a systematic manner [82, 83]. Formal analytical concepts of disease similarities and shared gene networks have been proposed to guide future research for the identification of molecular evidence of comorbidities [84]. Recent novel data mining approaches to combine GWAS findings and phenome data have been proposed to achieve NCD disease gene discovery, phenotype classification [41] and phenome-wide association studies [85] or to improve disease diagnostic procedures [27, 86]. Other bioinfomatic approaches combining animal model data of human disease and mammalian phenotype ontologies databases seem to suggest that germline genetic variation might underlie the heterogeneity of comorbidities [87, 88].

Summary

In the present report, we covered a wide range of aspects of importance to NCD research, including establishments and maintenance of large and systematic biobank cohorts from all parts of the world; implementation of broad and detailed phenotyping, as well as broad and detailed risk factor assessment, including aging characteristics; development of novel analytical methods for systemic analysis, addressing networks of diseases, or of personal and environmental risk factors, as well application of agnostic genomic analysis methods. In fact, to meet current and future public health challenges and to improve efficacy of prevention at the individual as well as at the population level, we need answers to the following questions [8992]: Which are major pathophysiologic pathways mediating the clustering of NCDs? To what degree are biological mechanisms shared between NCDs and normal aging? Do modifiable NCD risk factors act through common mechanisms? Can persons susceptible to common NCD risk patterns and comorbidities be identified?

To address this type of questions with data providing adequate statistical power and using hypothesis driven and explorative as well as agnostic approaches, establishment and maintenance of carefully designed large and comprehensive population-based cohorts with prospective collection of biological samples are a key requirement. Efforts must be further intensified to collaborate across cohorts from different geographic regions in a harmonized fashion, a process already started with remarkable success in P3G [93]. Harmonized and exhaustive phenotype collection is a particular challenge and novel instruments as developed for standardized assessment of multiple chronic diseases etiology [94] must be implemented. The quality management of a sustainable long-term biobank importantly comprises next to legislative, ethical and financial aspects also guaranteed safety of samples, temperature monitoring, traceability and parsimonious use of sample aliquots. Quality management of biological sample collection is particularly important for cohort studies with multi-centric design.

Given that biobank cohorts serve to increase the wellbeing of future generations by indirectly promoting biomedical knowledge and public health, these activities require the development of normative procedures and defined governance [95, 96]. There are still issues left to be resolved, such as establishing large biobanks for investigation of future research questions conflicts with the well accepted and widely implemented personal informed consent [97]. In the light of biobanking’s interest for present and future society, it might be considered a great good [95] and according discussions for a possibility of general non-personalized consent in politics and public are needed. This debate paper aimed to highlight the potential of biobank cohort research for complex disease etiology, a field of research that will allow improving health of populations as well as informing individuals on quality-of life increasing health decisions.

Declarations

Authors’ Affiliations

(1)
Swiss Tropical and Public Health Institute
(2)
University of Basel

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  98. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2458/​13/​1094/​prepub

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