Confounding, ascertainment bias, and the blind quest for a genetic 'fountain of youth'

Design Considerations for Genetic Linkage and Association Studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees, or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, the affected sibling pair design that is of more relevance for common, complex diseases. Power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree and genotyping errors and the effect of the type and density of genetic markers. For association studies, we consider the popular case-control design for dichotomous phenotypes and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritization of genetic variants, and for genome-wide association studies (GWAS) the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined.

Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes

Insights into the genetic basis of type 2 diabetes mellitus (T2DM) have been difficult to discern, despite substantial research. More is known about rare forms of diabetes mellitus, several of which share clinical and genetic features with the common form of T2DM. In this Review, we discuss the extent to which the study of rare and low-frequency mutations in large populations has begun to bridge the gap between rare and common forms of diabetes mellitus. We hypothesize that the perceived division between these diseases might be due, in part, to the historical ascertainment bias of genetic studies, rather than a clear distinction between disease pathophysiologies. We also discuss possible implications of a new model for the genetic basis of diabetes mellitus subtypes, where the boundary between subtypes becomes blurred.

The mathematical limits of genetic prediction for complex chronic disease

BACKGROUND

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases.

METHODS

Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease.

RESULTS

The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses.

CONCLUSIONS

Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease.

Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes

Genome sequencing can identify individuals in the general population who harbor rare coding variants in genes for Mendelian disorders and who may consequently have increased disease risk. Previous studies of rare variants in phenotypically extreme individuals display ascertainment bias and may demonstrate inflated effect-size estimates. We sequenced seven genes for maturity-onset diabetes of the young (MODY) in well-phenotyped population samples (n = 4,003). We filtered rare variants according to two prediction criteria for disease-causing mutations: reported previously in MODY or satisfying stringent de novo thresholds (rare, conserved and protein damaging). Approximately 1.5% and 0.5% of randomly selected individuals from the Framingham and Jackson Heart Studies, respectively, carry variants from these two classes. However, the vast majority of carriers remain euglycemic through middle age. Accurate estimates of variant effect sizes from population-based sequencing are needed to avoid falsely predicting a substantial fraction of individuals as being at risk for MODY or other Mendelian diseases.

The 'missing heritability' of common disorders: should health researchers care?

This article critiques the "missing heritability" position, which calls for greater efforts and funding to identify the genetic architecture of common disorders, even if this endeavor has yet to translate into tangible prevention, diagnosis, or treatment interventions. Supporters of the position contend that genetic variants "for" common disorders, which they argue must exist based on heritability estimates (hence their "missing heritability" position), have not been found because the current state of science and technology is not adequate to the task, yet they insist that this search warrants significant societal investments. We argue, instead, that these variants have not been found because they do not exist. The thrust of the problem with the "missing heritability" position, we propose, lies in its proponents' use of faulty concepts and research methods, including reliance on twin studies, plagued with environmental confounds; on the concept of heritability, a breeding statistic and not a measure of the importance of genetic influences on phenotypes; and on the belief that genetic variations are relevant to understanding, preventing, or treating common disorders, a belief that we argue is false. We elaborate on these problems, discuss their public health implications, and suggest future directions for a critical analysis of human genetics.

'Sifting the significance from the data' - the impact of high-throughput genomic technologies on human genetics and health care

This report is of a round-table discussion held in Cardiff in September 2009 for Cesagen, a research centre within the Genomics Network of the UK’s Economic and Social Research Council. The meeting was arranged to explore ideas as to the likely future course of human genomics. The achievements of genomics research were reviewed, and the likely constraints on the pace of future progress were explored. New knowledge is transforming biology and our understanding of evolution and human disease. The difficulties we face now concern the interpretation rather than the generation of new sequence data. Our understanding of gene-environment interaction is held back by our current primitive tools for measuring environmental factors, and in addition, there may be fundamental constraints on what can be known about these complex interactions.

Design considerations for genetic linkage and association studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare, highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, affected sibling pairs, of more relevance for common, complex diseases. Theoretical and more practical power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree, and genotyping errors, as well as the effect of the type and density of genetic markers. Linkage studies should be as large as possible to have sufficient power in relation to the expected genetic effect size. Segregation analysis, a formal statistical technique to describe the underlying genetic susceptibility, may assist in the estimation of the relevant parameters to apply, for instance. However, segregation analyses estimate the total genetic component rather than a single-locus effect. Locus heterogeneity should be considered when power is estimated and at the analysis stage, i.e. assuming smaller locus effect than the total the genetic component from segregation studies. Disease heterogeneity should be minimised by considering subtypes if they are well defined or by otherwise collecting known sources of heterogeneity and adjusting for them as covariates; the power will depend upon the relationship between the disease subtype and the underlying genotypes. Ultimately, identifying susceptibility alleles of modest effects (e.g. RR≤1.5) requires a number of families that seem unfeasible in a single study. Meta-analysis and data pooling between different research groups can provide a sizeable study, but both approaches require even a higher level of vigilance about locus and disease heterogeneity when data come from different populations. All necessary steps should be taken to minimise pedigree and genotyping errors at the study design stage as they are, for the most part, due to human factors. A two-stage design is more cost-effective than one stage when using short tandem repeats (STRs). However, dense single-nucleotide polymorphism (SNP) arrays offer a more robust alternative, and due to their lower cost per unit, the total cost of studies using SNPs may in the future become comparable to that of studies using STRs in one or two stages. For association studies, we consider the popular case-control design for dichotomous phenotypes, and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritisation of genetic variants, and for genome-wide association studies (GWAS), the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined. GWAS have a high power to detect common variants of high or moderate effect. For weaker effects (e.g. relative risk<1.2), the power is greatly reduced, particularly for recessive loci. While sample sizes of 10,000 or 20,000 cases are not beyond reach for most common diseases, only meta-analyses and data pooling can allow attaining a study size of this magnitude for many other diseases. It is acknowledged that detecting the effects from rare alleles (i.e. frequency<5%) is not feasible in GWAS, and it is expected that novel methods and technology, such as next-generation resequencing, will fill this gap. At the current stage, the choice of which GWAS SNP array to use does not influence the power in populations of European ancestry. A multistage design reduces the study cost but has less power than the standard one-stage design. If one opts for a multistage design, the power can be improved by jointly analysing the data from different stages for the SNPs they share. The estimates of locus contribution to disease risk from genome-wide scans are often biased, and relying on them might result in an underpowered replication study. Population structure has so far caused less spurious associations than initially feared, thanks to systematic ethnicity matching and application of standard quality control measures. Differential bias could be a more serious threat and must be minimised by strictly controlling all the aspects of DNA acquisition, storage, and processing.

On the validity of the likelihood ratio test and consistency of resulting parameter estimates in joint linkage and linkage disequilibrium analysis under improperly specified parametric models

It has been shown that parametric analysis of linkage disequilibrium conditional on linkage using an overly deterministic model can be optimal for family-based association analysis. However, if one applies this strategy carelessly, there is a risk of false inference. We analyse properties of such likelihood ratio tests when the assumed disease mode of inheritance is inaccurate. Under some conditions, problems result if one is not careful to consider what null hypothesis is being tested. We show that: (a) tests for which the null hypothesis assumes the absence of both linkage and association are independent of the true mode of inheritance; (b) likelihood ratio tests assuming either linkage or association under the null hypothesis may depend on the true mode of inheritance, leading to inconsistent parameter estimates, in particular under extremely deterministic models; (c) this problem cannot be eliminated by increasing sample size or adding population controls--as sample size increases, the chance of false positive inference goes to 100%; (d) this issue can lead to systematic false positive inference of association in regions of linkage. This is important because highly deterministic models are often used intentionally in model-based analyses because they can have more power than the true model, and are implicit in many model-free analysis methods.

Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions

Identification of environmentally modifiable factors causally influencing disease risk is fundamental to public-health improvement strategies. Unfortunately, observational epidemiological studies are limited in their ability to reliably identify such causal associations, reflected in the many cases in which conventional epidemiological studies have apparently identified associations that randomized controlled trials have failed to verify. The use of genetic variants as proxy measures of exposure -an application of the Mendelian randomization principle-can contribute to strengthening causal inference. Genetic variants are not subject to bias due to reverse causation (disease processes influencing exposure, rather than vice versa) or recall bias, and if simple precautions are applied, they are not influenced by confounding or attenuation by errors. The principles of Mendelian randomization are illustrated with specific reference to studies of the effects of alcohol intake on various health-related outcomes through the utilization of genetic variants related to alcohol metabolism (in ALDH2 and ADH1B). Ways of incorporating Gene × Environment interactions into the Mendelian randomization framework are developed, and the strengths and limitations of the approach discussed.

An utter refutation of the "fundamental theorem of the HapMap"

The International HapMap Project was proposed in order to quantify linkage disequilibrium (LD) relationships among human DNA polymorphisms in an assortment of populations, in order to facilitate the process of selecting a minimal set of markers that could capture most of the signal from the untyped markers in a genome-wide association study. The central dogma can be summarized by the argument that if a marker is in tight LD with a polymorphism that directly impacts disease risk, as measured by the metric r(2), then one would be able to detect an association between the marker and disease with sample size that was increased by a factor of 1/r(2) over that needed to detect the effect of the functional variant directly. This "fundamental theorem" holds, however, only if one assumes that the LD between loci and the etiological effect of the functional variant are independent of each other, that they are statistically independent of all other etiological factors (in exposure and action), that sampling is prospective, and that the estimates of r(2) are accurate. None of these are standard operating assumptions, however. We describe the ramifications of these implicit assumptions, and provide simple examples in which the effects of a functional variant could be unequivocally detected if it were directly genotyped, even as markers in high LD with the functional variant would never show association with disease, even in infinite sample sizes. Both theoretical and empirical refutation of the central dogma of genome-wide association studies is thus presented.

What are genes "for" or where are traits "from"? What is the question?

For at least a century it has been known that multiple factors play a role in the development of complex traits, and yet the notion that there are genes "for" such traits, which traces back to Mendel, is still widespread. In this paper, we illustrate how the Mendelian model has tacitly encouraged the idea that we can explain complexity by reducing it to enumerable genes. By this approach many genes associated with simple as well as complex traits have been identified. But the genetic architecture of biological traits, or how they are made, remains largely unknown. In essence, this reflects the tension between reductionism as the current "modus operandi" of science, and the emerging knowledge of the nature of complex traits. Recent interest in systems biology as a unifying approach indicates a reawakened acceptance of the complexity of complex traits, though the temptation is to replace "gene for" thinking by comparably reductionistic "network for" concepts. Both approaches implicitly mix concepts of variants and invariants in genetics. Even the basic question is unclear: what does one need to know to "understand" the genetic basis of complex traits? New operational ideas about how to deal with biological complexity are needed.

Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology

BACKGROUND

Despite earlier doubts, a string of recent successes indicates that if sample sizes are large enough, it is possible-both in theory and in practice-to identify and replicate genetic associations with common complex diseases. But human genome epidemiology is expensive and, from a strategic perspective, it is still unclear what 'large enough' really means. This question has critical implications for governments, funding agencies, bioscientists and the tax-paying public. Difficult strategic decisions with imposing price tags and important opportunity costs must be taken.

METHODS

Conventional power calculations for case-control studies disregard many basic elements of analytic complexity-e.g. errors in clinical assessment, and the impact of unmeasured aetiological determinants-and can seriously underestimate true sample size requirements. This article describes, and applies, a rigorous simulation-based approach to power calculation that deals more comprehensively with analytic complexity and has been implemented on the web as ESPRESSO: (www.p3gobservatory.org/powercalculator.htm).

RESULTS

Using this approach, the article explores the realistic power profile of stand-alone and nested case-control studies in a variety of settings and provides a robust quantitative foundation for determining the required sample size both of individual biobanks and of large disease-based consortia. Despite universal acknowledgment of the importance of large sample sizes, our results suggest that contemporary initiatives are still, at best, at the lower end of the range of desirable sample size. Insufficient power remains particularly problematic for studies exploring gene-gene or gene-environment interactions. Discussion Sample size calculation must be both accurate and realistic, and we must continue to strengthen national and international cooperation in the design, conduct, harmonization and integration of studies in human genome epidemiology.

Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models

BACKGROUND/AIMS

In genetic studies of complex disease a consideration for the investigator is detection of joint effects. The Multifactor Dimensionality Reduction (MDR) algorithm searches for these effects with an exhaustive approach. Previously unknown aspects of MDR performance were the power to detect interactive effects given large numbers of non-model loci or varying degrees of heterogeneity among multiple epistatic disease models.

METHODS

To address the performance with many non-model loci, datasets of 500 cases and 500 controls with 100 to 10,000 SNPs were simulated for two-locus models, and one hundred 500-case/500-control datasets with 100 and 500 SNPs were simulated for three-locus models. Multiple levels of locus heterogeneity were simulated in several sample sizes.

RESULTS

These results show MDR is robust to locus heterogeneity when the definition of power is not as conservative as in previous simulation studies where all model loci were required to be found by the method. The results also indicate that MDR performance is related more strongly to broad-sense heritability than sample size and is not greatly affected by non-model loci.

CONCLUSIONS

A study in which a population with high heritability estimates is sampled predisposes the MDR study to success more than a larger ascertainment in a population with smaller estimates.

Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity.

SNP-specific extraction of haplotype-resolved targeted genomic regions

The availability of genotyping platforms for comprehensive genetic analysis of complex traits has resulted in a plethora of studies reporting the association of specific single-nucleotide polymorphisms (SNPs) with common diseases or drug responses. However, detailed genetic analysis of these associated regions that would correlate particular polymorphisms to phenotypes has lagged. This is primarily due to the lack of technologies that provide additional sequence information about genomic regions surrounding specific SNPs, preferably in haploid form. Enrichment methods for resequencing should have the specificity to provide DNA linked to SNPs of interest with sufficient quality to be used in a cost-effective and high-throughput manner. We describe a simple, automated method of targeting specific sequences of genomic DNA that can directly be used in downstream applications. The method isolates haploid chromosomal regions flanking targeted SNPs by hybridizing and enzymatically elongating oligonucleotides with biotinylated nucleotides based on their selective binding to unique sequence elements that differentiate one allele from any other differing sequence. The targeted genomic region is captured by streptavidin-coated magnetic particles and analyzed by standard genotyping, sequencing or microarray analysis. We applied this technology to determine contiguous molecular haplotypes across a ∼150 kb genomic region of the major histocompatibility complex.

Familial risks for common diseases: etiologic clues and guidance to gene identification

Familial clustering of a disease is a direct indicator of a possible heritable cause, provided that environmental sharing can be excluded. If the familial clustering is lacking, the likelihood of a heritable influence is also small. In the era of genome scans, the consideration of data on heritability should be important in the assessment of the likely success of the genome scan. The availability of a Multigeneration Register in Sweden provides a reliable access to families throughout the last century. This Register has been extensively used to study a number of different diseases through linkage to the Hospital Discharge Register. In the present article we review the obtained and some unpublished results for nine main disease classes. For each of these, familial risks are given for four disease subtypes. As measures of familial clustering we use risks between siblings, twins and spouses. Disease correlation between spouses suggests environmental sharing and a higher correlation between siblings and particularly twins shows heritable effects. We will also comment on the established susceptibility genes and the risks conferred by them. The data suggest high heritabilities for chronic obstructive pulmonary disease, asthma, noninfective enteritis and colitis, cerebral palsy and endocrine and metabolic diseases. Among the performed first-generation genome scans on various diseases, the success appears to be related to the a priori heritability estimates. To our knowledge this is a first attempt to summarize familial risks for a large number of diseases using data from a single population on which reasonable uniform diagnostic criteria have been applied.

Genetic polymorphisms of the RAS-cytokine pathway and chronic kidney disease

Chronic kidney disease (CKD) in children is irreversible. It is associated with renal failure progression and atherosclerotic cardiovascular (CV) abnormalities. Nearly 60% of children with CKD are affected since birth with congenital or inherited kidney disorders. Preliminary evidence primarily from adult CKD studies indicates common genetic risk factors for CKD and atherosclerotic CV disease. Although multiple physiologic pathways share common genes for CKD and CV disease, substantial evidence supports our attention to the renin angiotensin system (RAS) and the interlinked inflammatory cascade because they modulate the progressions of renal and CV disease. Gene polymorphisms in the RAS-cytokine pathway, through altered gene expression of inflammatory cytokines, are potential factors that modulate the rate of CKD progression and CV abnormalities in patients with CKD. For studying such hypotheses, the cooperative efforts among scientific groups and the availability of robust and affordable technologies to genotype thousands of single nucleotide polymorphisms (SNPs) across the genome make genome-wide association studies an attractive paradigm for studying polygenic diseases such as CKD. Although attractive, such studies should be interpreted carefully, with a fundamental understanding of their potential weaknesses. Nevertheless, whole-genome association studies for diabetic nephropathy and future studies pertaining to other types of CKD will offer further insight for the development of targeted interventions to treat CKD and associated atherosclerotic CV abnormalities in the pediatric CKD population.

Analysis of quantitative trait loci

Diseases with complex inheritance are characterized by multiple genetic and environmental factors that often interact to produce clinical symptoms. In addition, etiological heterogeneity (different risk factors causing similar phenotypes) obscure the inheritance pattern among affected relatives and hamper the feasibility of gene-mapping studies. For such diseases, the careful selection of quantitative phenotypes that may represent intermediary risk factors for disease development (intermediate phenotypes) is etiologically more homogeneous than the disease per se. Over the last 15 years quantitative trait locus mapping has become a popular method for understanding the genetic basis for intermediate phenotypes. This chapter provides an introduction to classical and recent strategies for mapping quantitative trait loci in humans.

Heritability and segregation analysis of osteosarcoma in the Scottish deerhound

Osteosarcoma is the most common malignant bone tumor in dogs and, like its human orthologue, is characterized by aggressive local behavior and high metastatic rates. The Scottish deerhound is a breed of dog with a >15% incidence of osteosarcoma and represents an excellent spontaneously occurring large-animal model of the human disease. We modeled the transmission of the osteosarcoma phenotype in a population of over 1000 related deerhounds ascertained as part of a prospective health study. Variance component analysis, segregation analysis, and linear modeling were performed to evaluate heritability, to infer the presumptive transmission model, and to identify covariate effects for this phenotype within the breed, respectively. Based on variance component analysis, heritability (h2) was estimated to be 0.69. Six transmission models were analyzed by segregation analysis; based on Akaike's information criteria, the most parsimonious model was the Mendelian major gene model with dominant expression. Linear modeling identified gender and genotype as significant predictors of disease outcome. Importantly, duration of gonadal hormone exposure, weight, and height at maturity were not significant predictors of outcome. Inheritance of the putative high-risk allele was thus associated with >75% risk of disease occurrence compared to the <5% baseline risk. These results support the hypothesis that a major gene with a dominant effect explains most of the osteosarcoma phenotype within the Scottish deerhound.

Gene-environment studies: any advantage over environmental studies?

Gene-environment studies have been motivated by the likely existence of prevalent low-risk genes that interact with common environmental exposures. The present study assessed the statistical advantage of the simultaneous consideration of genes and environment to investigate the effect of environmental risk factors on disease. In particular, we contemplated the possibility that several genes modulate the environmental effect. Environmental exposures, genotypes and phenotypes were simulated according to a wide range of parameter settings. Different models of gene-gene-environment interaction were considered. For each parameter combination, we estimated the probability of detecting the main environmental effect, the power to identify the gene-environment interaction and the frequency of environmentally affected individuals at which environmental and gene-environment studies show the same statistical power. The proportion of cases in the population attributable to the modeled risk factors was also calculated. Our data indicate that environmental exposures with weak effects may account for a significant proportion of the population prevalence of the disease. A general result was that, if the environmental effect was restricted to rare genotypes, the power to detect the gene-environment interaction was higher than the power to identify the main environmental effect. In other words, when few individuals contribute to the overall environmental effect, individual contributions are large and result in easily identifiable gene-environment interactions. Moreover, when multiple genes interacted with the environment, the statistical benefit of gene-environment studies was limited to those studies that included major contributors to the gene-environment interaction. The advantage of gene-environment over plain environmental studies also depends on the inheritance mode of the involved genes, on the study design and, to some extend, on the disease prevalence.

Mapping translational research in personalized therapeutics: from molecular markers to health policy

Translational research is frequently used in the bioscience literature to refer to the translation of basic science into practical applications at the point of patient care. With the introduction of theragnostics, a new medical subspecialty that fuses therapeutics and diagnostic medicine with the goal of providing individualized pharmacotherapy, we suggest that the focus of translational research is shifting. We identify two bottlenecks or gaps in translational research for theragnostics: GAP1 translation from basic science to first-in-human proof-of-concept; and GAP2 translation from clinical proof-of-concept to development of evidence-based personalized treatment guidelines. GAP1 translational research in theragnostics is usually performed in traditional craft-based studies with small sample sizes and led by independent academic or industry researchers. In contrast, GAP2 translational investigations typically rely on large research consortiums and population-based biobanks that couple biomarker information with longitudinal ‘real-life’ observational data on a broad range of pharmacological phenotypes. Despite an abundance of research on the use of biobanks in disease gene discovery, there has been little conceptual work on whether and to what extent population biobanks can be utilized for translating genomics discoveries to practical treatment guidelines for theragnostic tests.

Statistical tools for linkage analysis and genetic association studies

Genetic mapping by linkage analysis has been an invaluable tool in the positional strategy to identify the molecular basis of many rare Mendelian disorders. With the attention of the scientific and medical community shifting towards the analysis of more common, complex traits, it has become necessary to develop new approaches that take into account the complexity of the genetic basis of these disorders and their possible interaction with other, nongenetic factors. Linkage disequilibrium studies are now becoming increasingly popular thanks to the advent of genotyping platforms that allow genome-wide searching for association between hundreds of thousands of random polymorphisms and disease phenotypes in large samples of unrelated individuals. Moreover, the definition of the disease phenotype itself is being reconsidered to include quantitative traits that may better define the underlying biologic mechanisms for many pathologic conditions. This article will review classic and new approaches to genetic mapping by linkage and association analysis and discuss the directions this field is likely to take in the near future.

The balance between heritable and environmental aetiology of human disease

The Human Genome Project and the ensuing International HapMap Project were largely motivated by human health issues. But the distance from a DNA sequence variation to a novel disease gene is considerable; for complex diseases, closing this gap hinges on the premise that they arise mainly from heritable causes. Using cancer as an example of complex disease, we examine the scientific evidence for the hypothesis that human diseases result from interactions between genetic variants and the environment.

Strategy Considerations in Genome Cohort Construction in Korea

Focusing on complex diseases of public health significance, strategic issues regarding the on-going Korean Genome Cohort were reviewed: target size and diseases, measurements, study design issues, and followup strategy of the cohort. Considering the epidemiologic characteristics of Korean population as well as strengths and drawbacks of current research environment, we tried to tailor the experience of other existing cohorts into proposals for this Korean study. Currently 100,000 individuals have been participating the new Genome Cohort in Korea. Target size of de novo collection is recommended to be set as between 300,000 to 500,000. This target size would allow acceptable power to detect genetic and environmental factors of moderate effect size and possible interactions between them. Family units and/or special subgroups are recommended to parallel main body of adult individuals to increase the overall efficiency of the study. Given that response rate to the conventional re-contact method may not be satisfactory, successful follow-up is the main key to the achievement of the Korean Genome Cohort. Access to the central database such as National Health Insurance data can provide enormous potential for near-complete case detection. Efforts to build consensus amongst scientists from broad fields and stakeholders are crucial to unleash the centralized database as well as to refine the commitment of this national project.

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

Background

The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis), gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins.

Method

A new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918) assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction.

Conclusion

The results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis, a largely environmental explanation for shared sibling risk is plausible, even if the classical heritability is high. The results therefore highlight the possibility – previously rejected on the basis of twin study results – that inherited genetic variants are important in determining risk only for the relatively rare familial forms of diseases such as breast cancer. If so, genetic models of familial aggregation may be incorrect and the hunt for additional susceptibility genes could be largely fruitless.

Mapping genes and pathways in autoimmune disease

Identifying novel genes and pathways controlling T-cell activation holds the promise of developing novel therapies for autoimmune disease and cancer. Recent advances in the human genome project have shown that it is timely for small groups searching for this Holy Grail to rethink their options. In this review, some alternative strategies employed in pursuing novel disease pathways in rodents and humans, including recent results, are presented. Examples include the murine Roquin and Ncf1 genes, and the PTPN22 gene identified in humans. The potential benefit of reducing the heterogeneity of clinically defined diseases by the careful phenotyping of patients, cells and lesions using advanced molecular biology and imaging techniques is highlighted.

Translational research in central nervous system drug discovery

Of all the therapeutic areas, diseases of the CNS provide the biggest challenges to translational research in this era of increased productivity and novel targets. Risk reduction by translational research incorporates the "learn" phase of the "learn and confirm" paradigm proposed over a decade ago. Like traditional drug discovery in vitro and in laboratory animals, it precedes the traditional phase 1-3 studies of drug development. The focus is on ameliorating the current failure rate in phase 2 and the delays resulting from suboptimal choices in four key areas: initial test subjects, dosing, sensitive and early detection of therapeutic effect, and recognition of differences between animal models and human disease. Implementation of new technologies is the key to success in this emerging endeavor.

Dissecting complex disease: the quest for the Philosopher's Stone?

Is the search for the causes of complex disease akin to the alchemist's vain quest for the Philosopher's Stone? Complex chronic diseases have tremendous public health impact in the industrialized world. Much effort has been expended on research into their causes, with the aim of predicting who will be affected or preventing effects before they arise, but progress has been halting at best. In this paper, we discuss possible reasons including the use of models and methods that fit point-source and Mendelian diseases but may not be as appropriate for complex diseases, reliance on causal criteria that may not be as relevant as they are for communicable diseases, and the biology of complex disease itself. Finally, we ask whether most complex diseases are even good candidates for the kind of prediction and prevention that we have come to expect based on experience with infectious and Mendelian disease.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

How many genes underlie the occurrence of common complex diseases in the population?

BACKGROUND

Most common human diseases are due to complex interactions among multiple genetic variants and environmental risk factors. There is debate over whether variants of a relatively small number of genes, each with weak or modest individual effects, account for a large proportion of common diseases in the population, or whether a large number of rare variants with large effects underlie genetic susceptibility to these diseases. It is not clear how many genes are necessary to account for an appreciable population-attributable fraction of these diseases.

METHODS

In this analysis, we estimated the number of disease susceptibility genes needed to account for varying population attributable fractions of a common complex disease, taking into account the genotype prevalence, risk ratios for individual genes, and the model of gene-gene interactions (additive or multiplicative).

RESULTS

Very large numbers of rare genotypes (e.g. those with frequencies of 1 per 5000 or less) are needed to explain 50% of a common disease in the population, even if the individual risk ratios are large (RR = 10-20). On the other hand, only approximately 20 genes are usually needed to explain 50% of the burden of a disease in the population if the predisposing genotypes are common (> or = 25%), even if the individual risk ratios are relatively small (RR = 1.2-1.5).

CONCLUSIONS

Our results suggest that a limited number of disease susceptibility genes with common variants can explain a major proportion of common complex diseases in the population. Our findings should help focus the search for common genetic variants that provide the most important predispositions to complex human diseases.

Variation in recombination rate may bias human genetic disease mapping studies

The availability of the human genome sequence and variability information (as from the International HapMap project) will enhance our ability to map genetic disorders and choose targets for therapeutic intervention. However, several factors, such as regional variation in recombination rate, can bias conclusions from genetic mapping studies. Here, we examine the impact of regional variation in recombination rate across the human genome. Through computer simulations and literature surveys, we conclude that genetic disorders have been mapped to regions of low recombination more often than expected if such diseases were randomly distributed across the genome. This concentration in low recombination regions may be an artifact, and disorders appearing to be caused by a few genes of large effect may be polygenic. Future genetic mapping studies should be conscious of this potential complication by noting the regional recombination rate of regions implicated in diseases.

Genome-wide association studies: theoretical and practical concerns

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

Nonreplication in genetic studies of complex diseases--lessons learned from studies of osteoporosis and tentative remedies

Inconsistent results have accumulated in genetic studies of complex diseases/traits over the past decade. Using osteoporosis as an example, we address major potential factors for the nonreplication results and propose some potential remedies. Over the past decade, numerous linkage and association studies have been performed to search for genes predisposing to complex human diseases. However, relatively little success has been achieved, and inconsistent results have accumulated. We argue that those nonreplication results are not unexpected, given the complicated nature of complex diseases and a number of confounding factors. In this article, based on our experience in genetic studies of osteoporosis, we discuss major potential factors for the inconsistent results and propose some potential remedies. We believe that one of the main reasons for this lack of reproducibility is overinterpretation of nominally significant results from studies with insufficient statistical power. We indicate that the power of a study is not only influenced by the sample size, but also by genetic heterogeneity, the extent and degree of linkage disequilibrium (LD) between the markers tested and the causal variants, and the allele frequency differences between them. We also discuss the effects of other confounding factors, including population stratification, phenotype difference, genotype and phenotype quality control, multiple testing, and genuine biological differences. In addition, we note that with low statistical power, even a "replicated" finding is still likely to be a false positive. We believe that with rigorous control of study design and interpretation of different outcomes, inconsistency will be largely reduced, and the chances of successfully revealing genetic components of complex diseases will be greatly improved.

Haplotype analysis and identification of genes for a complex trait: examples from schizophrenia

For more than a decade there has been intensive research into the genetic etiology of schizophrenia, yet it is only recently that the first findings of promising genes associating with the disorder have been reported. Linkage analyses in families collected from different populations have provided relatively well defined genomic loci. These have been typically followed by fine mapping studies using single nucleotide polymorphisms (SNPs). A number of analysis programs have been produced to test SNPs and their haplotypes for association. Typically association has been established to specific haplotypes representing an allelic variant of the corresponding gene. The inherent problem of multiple testing in the analysis of haplotypes needs to be addressed fully, to determine if any of these recent findings can be considered as confirmed susceptibility genes for schizophrenia. However, informative haplotypes have provided a way to define allelic variants of genes associated with schizophrenia in numerous study samples, and are a useful tool in characterizing the extent of allelic diversity of putative schizophrenia susceptibility genes within different populations.