How many diseases does it take to map a gene with SNPs?

Rare diseases: human genome research is coming home

After a long and largely disappointing detour, Genome Research has reidentified Rare Diseases as a major opportunity for improving health care and a clue to understanding gene and genome function. In this Special Issue of CSH Molecular Case Studies on Rare Diseases, several invited Perspectives, numerous Case Reports, and this Editorial itself address recent breakthroughs as well as unsolved problems in this wide field. These range from exciting prospects for gap-free diagnostic whole-genome sequencing to persisting problems related to identifying and distinguishing pathogenic and benign variants; and from the good news that soon, the United Kingdom will no longer be the only country to have introduced whole-genome sequencing into health care to the sobering conclusion that in many countries the clinical infrastructure for bringing Genome Medicine to the patient is still lacking. With less than 5000 genes firmly implicated in disease, the identification of at least twice as many disease genes is a major challenge, and the elucidation of their function is an even larger task. But given the renewed interest in rare diseases, their importance for health care, and the vast and growing spectrum of concepts and methods for studying them, the future of Human Genome Research is bright.

Targeted acetylcholinesterase-responsive drug carriers with long duration of drug action and reduced hepatotoxicity

PURPOSE

Acetylcholinesterase (AChE) plays a critical role in the transmission of nerve impulse at the cholinergic synapses. Design and synthesis of AChE inhibitors that increase the cholinergic transmission by blocking the degradation of acetylcholine can serve as a strategy for the treatment of AChE-associated disease. Herein, an operational targeted drug delivery platform based on AChE-responsive system has been presented by combining the unique properties of enzyme-controlled mesoporous silica nanoparticles (MSN) with clinical-used AChE inhibitor.

METHODS

Functionalized MSNs were synthesized by liquid phase method and characterized by using different analytical methods. The biocompatibility and cytotoxicity of MSNs were determined by hemolysis experiment and MTT assay, respectively. Comparison of AChE activity between drug-loading system and inhibitor was developed with kits and by ELISA method. The efficacy of drug-loaded nanocarriers was investigated in a mouse model.

RESULTS

Compared with AChE inhibitor itself, the inhibition efficiency of this drug delivery system was strongly dependent on the concentration of AChE. Only AChE with high concentration could cause the opening of pores in the MSN, leading to the controlled release of AChE inhibitor in disease condition. Critically, the drug delivery system can not only exhibit long duration of drug action on AChE inhibition but also reduce the hepatotoxicity in vivo.

CONCLUSION

In summary, AChE-responsive drug release systems have been far less explored. Our results would shed lights on the design of enzyme controlled-release multifunctional system for enzyme-associated disease treatment.

The Four Horsemen of the 'Omicsalypse': ontology, replicability, probability and epistemology

Much of modern genomics and the other 'omics' that tag along, assert that the causal bases of biomedical outcomes are genomically enumerable lists whose effects are predictable with 'precision', extensible from samples to all, and enabled by ever-greater hypothesis-free data accumulation. The assertion rests on fundamental, if often implicit assumptions, that (1) the phenomena are based on underlying law-like biological causation, and, therefore, are (2) replicable and (3) even if not deterministic, have specifiable, stable, essentially parametric, probabilities, all of which (4) essentially equates induction with deduction, enabling asymptotically accurate prediction based on past observation. These glowing promises are the four horsemen of a genocentric 'Omicsalypse'. But what if the assumptions are wrong or appropriate only to an extent that is unknowable, even in principle? Might there be better ways to understand complex traits?

Shortcut to success? Negotiating genetic uniqueness in global biomedicine

Since the sequencing of the human genome, as well as the completion of the first Human Genome Diversity Project, the benefits of studying one human population over another has been an ongoing debate relating to the replicability of findings in other populations. The leveraging of specific populations into research markets has made headlines in cases such as deCode in Iceland, Quebec Founder Population, and Generation Scotland. In such cases, researchers and policy makers have used the genetic and historical uniqueness of their populations to attract scientific, commercial and political interest. In this article, we explore how in countries with population isolates, such as Finland, the researchers balance considerations relating to the generalization and replicability of findings in small yet unique research populations to global biomedical research interests. This highlights challenges related to forms of competition associated with genetics research markets, as well as what counts as the 'right' population for genetic research.

Polymorphisms in the Chicken Growth Differentiation Factor 9 Gene Associated with Reproductive Traits

The aim of the study was to investigate GDF9 gene polymorphisms and their association with reproductive traits in chicken using DNA sequencing. A total of 279 Dongxiang blue-shelled (DX) chickens and 232 Luhua (LH) chickens were used for validation. We detected 15 single nucleotide polymorphisms (SNPs): nine SNPs were previously unreported in chicken, two were missense mutations, and only three exhibited significant associations with reproductive traits. G.17156387C>T was significantly associated with age at first egg (AFE) and weight of first egg (WFE) in both breeds. Birds carrying the CC genotype exhibited higher AFE and WFE values than those with the TT genotype. The SNP g.17156427A>G exhibited an association with egg weight at 300 days of age (EWTA) in DX but not in LH chickens. The SNP g.17156703A>C affected the AFE and EN (total number of eggs at 300 days of age) in DX chickens. In addition, certain diplotypes significantly affected AFE, BWTA (body weight at 300 days of age), and EN in both breeds. RT-PCR results showed that the GDF9 gene was highly expressed in stroma with cortical follicles (STR) and prehierarchal follicles. These results provided further evidence that the GDF9 gene is involved in determining reproductive traits in chicken.

Replicability and Prediction: Lessons and Challenges from GWAS

Since the publication of the Wellcome Trust Case Control Consortium (WTCCC) landmark study a decade ago, genome-wide association studies (GWAS) have led to the discovery of thousands of risk variants involved in disease etiology. This success story has two angles that are often overlooked. First, GWAS findings are highly replicable. This is an unprecedented phenomenon in complex trait genetics, and indeed in many areas of science, which in past decades have been plagued by false positives. At a time of increasing concerns about the lack of reproducibility, we examine the biological and methodological reasons that account for the replicability of GWAS and identify the challenges ahead. In contrast to the exemplary success of disease gene discovery, at present GWAS findings are not useful for predicting phenotypes. We close with an overview of the prospects for individualized prediction of disease risk and its foreseeable impact in clinical practice.

Yeast-based assays for characterization of the functional effects of single nucleotide polymorphisms in human DNA repair genes

DNA repair mechanisms maintain genomic integrity upon exposure to various types of DNA damage, which cause either single- or double-strand breaks in the DNA. Here, we propose a strategy for the functional study of single nucleotide polymorphisms (SNPs) in the human DNA repair genes XPD/ERCC2, RAD18, and KU70/XRCC6 and the checkpoint activation gene ATR that are essentially involved in the cell cycle and DNA damage repair. We analyzed the mutational effects of the DNA repair genes under DNA-damaging conditions, including ultraviolet irradiation and treatment with genotoxic reagents, using a Saccharomyces cerevisiae system to overcome the limitations of the human cell-based assay. We identified causal variants from selected SNPs in the present analyses. (i) R594C SNP in RAD3 (human XPD/ERCC2) caused severe reductions in the growth rate of mutant cells upon short-wavelength UV irradiation or chemical reagent treatment. (ii) The growth rates of the selected variants in RAD18, YKU70, and MEC1 were similar to those of wild-type cells on methyl methanesulfonate and hydroxyurea treated media. (iii) We also assessed the structural impact of the SNPs by analyzing differences in the structural conformation and calculating the root mean square deviation, which is a measure of the discordance of the Cα atoms between protein structures. Based on the above results, we propose that these analytical approaches serve as efficient methods for the identification of causal variants of human disease-causing genes and elucidation of yeast-cell based molecular mechanisms.

The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.

Genetics of primary sclerosing cholangitis and pathophysiological implications

Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.

Immunogenetics in primary sclerosing cholangitis

PURPOSE OF REVIEW

Primary sclerosing cholangitis (PSC) is a progressive biliary liver disorder strongly associated with inflammatory bowel disease (PSC-IBD). We summarize the genetics of PSC-IBD and highlight recent findings that further differentiate PSC-IBD as a unique disease.

RECENT FINDINGS

To date, genome-wide studies have uncovered 23 susceptibility loci for PSC-IBD, the majority of which have been previously reported as risk factors in other immune-mediated disorders. For most candidates, the pathological relationship to PSC-IBD remains largely unknown. Several candidate genes appear to be liver related but the large majority relate to immunity and reaffirm that alterations to immune function, trafficking, and tolerance are likely to influence susceptibility and presentation of PSC-IBD. Similar to most immune-mediated diseases, the strongest association in PSC-IBD resides within the human leukocyte antigen complex and suggests that disease-specific antigens drive pathogenic immune responses. Although genetic predisposition influences disease, genetic determinants account for less than 10% of total disease liability in PSC-IBD, clearly emphasizing the predominant role of environmental factors on disease susceptibility.

SUMMARY

Genetic studies define PSC-IBD as a unique disease to IBD mirroring clinical observations. Most risk loci harbour immune-related genes and disease variants are likely to perturb immune function, tolerance, and/or trafficking. Additional studies in patients and novel experimental systems are needed to identify the origin and impact of environmental factors in relation to genetic predisposition in PSC-IBD.

Transforming growth factor-β1 (C509T, G800A, and T869C) gene polymorphisms and risk of ischemic stroke in North Indian population: A hospital-based case-control study

Background:

Transforming growth factor-beta 1 (TGF-β1) is a multifunctional pleiotropic cytokine involved in inflammation and pathogenesis of cerebrovascular diseases. There is limited information on the association between variations within the TGF-β1 gene polymorphisms and risk of ischemic stroke (IS). The aim of this study was to investigate the association of the TGF-β1 gene (C509T, G800A, and T869C) polymorphisms, and their haplotypes with the risk of IS in North Indian population.

Methods:

A total of 250 IS patients and 250 age- and sex-matched controls were studied. IS was classified using the Trial of Org 10172 in Acute Stroke Treatment classification. Conditional logistic regression analysis was used to calculate the strength of association between TGF-β1 gene polymorphisms and risk of IS. Genotyping was performed using SNaPshot method.

Results:

Hypertension, diabetes, dyslipidemia, alcohol, smoking, family history of stroke, sedentary lifestyle, and low socioeconomic status were found to be associated with the risk of IS. The distribution of C509T, G800A and T869C genotypes was consistent with Hardy-Weinberg Equilibrium in the IS and control groups. Adjusted conditional logistic regression analysis showed a significant association of TGF-β1 C509T (odds ratio [OR], 2.1; 95% CI; 1.2–3.8; P = 0.006), G800A (OR, 4.4; 95% CI; 2.1–9.3; P < 0.001) and T869C (OR, 2.6; 95% CI; 1.5–4.5; P = 0.001) with the risk of IS under dominant model. Haplotype analysis showed that C509-A800-T869 and T509-G800-C869 haplotypes were significantly associated with the increased risk of IS. C509T and T869C were in strong linkage disequilibrium (D' =0.51, r² = 0.23).

Conclusion:

Our results suggest that TGF-β1 polymorphisms and their haplotypes are significantly associated with the risk of IS in North Indian population.

Genetics of Type 2 Diabetes: the Power of Isolated Populations

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

Genetic polymorphisms associated with overweight and obesity in uncontrolled Type 2 diabetes mellitus

Generally, obese and overweight individuals display higher free fatty acid levels, which stimulate insulin resistance. The combination of overweight or obesity with insulin resistance can trigger Type 2 diabetes mellitus (T2DM) and are primary contributing factors to the development of uncontrolled T2DM. Genetic polymorphisms also play an important role as they can impact a population's susceptibility to becoming overweight or obese and developing related chronic complications, such as uncontrolled T2DM. This review specifically examines the genetic polymorphisms associated with overweight and obesity in patients with uncontrolled T2DM. Particularly, gene polymorphisms in ADIPOQ (rs1501299 and rs17300539), LepR (rs1137101 and rs1045895), IRS2 (rs1805092), GRB14 (rs10195252 and rs3923113) and PPARG (rs1801282) have been associated with overweight and obesity in uncontrolled T2DM.

Penetrance of pathogenic mutations in haploinsufficient genes for intellectual disability and related disorders

De novo loss of function (LOF) mutations in the ASXL3 gene cause Bainbridge-Ropers syndrome, a severe form of intellectual disability (ID) and developmental delay, but there is evidence that they also occur in healthy individuals. This has prompted us to look for non-pathogenic LOF variants in other ID genes. Heterozygous LOF mutations in ASXL1, a paralog of ASXL3, are known to cause Bohring-Opitz syndrome (BOS), and benign LOF mutations in this gene have not been published to date. Therefore, we were surprised to find 56 ASXL1 LOF variants in the ExAC database (http://exac.broadinstitute.org), comprising exomes from 60,706 individuals who had been selected to exclude severe genetic childhood disorders. 4 of these variants have been described as disease-causing in patients with BOS, which rules out the possibility that pathogenic and clinically neutral LOF variants in this gene are functionally distinct. Apparently benign LOF variants were also detected in several other genes for ID and related disorders, including CDH15, KATNAL2, DEPDC5, ARID1B and AUTS2, both in the ExAC database and in the 6,500 exomes of the Exome Variant Server (http://evs.gs.washington.edu/EVS/). These observations argue for low penetrance of LOF mutations in ASXL1 and other genes for ID and related disorders, which could have far-reaching implications for genetic counseling and research.

Reducing INDEL calling errors in whole genome and exome sequencing data

Background

INDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.

Methods

We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).

Results

Simulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.

Conclusions

Overall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0089-z) contains supplementary material, which is available to authorized users.

Analysis of interleukin-10 promoter single nucleotide polymorphisms and risk of non-Hodgkin lymphoma in a Malaysian population

We evaluated the association of two IL10 single nucleotide polymorphisms (SNPs) (rs1800896 and rs1800871) with non-Hodgkin lymphoma (NHL) risk in the three major races of the Malaysian population (Malay, Chinese and Indian; 317 cases and 330 controls). Our initial screening demonstrated that rs1800871 but not rs1800896 was significantly associated with increased NHL risk in Malays (pMalay-Rec = 0.007) and Chinese only (pChinese-Rec = 0.039). Subsequent combined analysis of the Malay and Chinese revealed significant association of rs1800871 with all (ALL) NHL subtypes (pMeta-ALL-NHL-Rec = 0.001), ALL B-cell subtypes (pMeta-ALL-B-cell-Rec = 0.003), diffuse large B-cell lymphoma (DLBCL) subtype (pMeta-DLBCL-Rec = 0.002) and ALL T-cell subtypes (pMeta-ALL-T-cell-Rec = 0.031). SNP rs1800896 showed increased risk only in follicular lymphoma (FL) (pMeta-FL-Dom = 0.0004). We also detected a male-specific association of rs1800871 with increased NHL risk (pMeta-Male-ALL-NHL-Rec = 0.006) in the combined analysis. To our knowledge, this is the first report on the association of IL10 promoter SNPs with NHL susceptibility in the three major races of Malaysia.

Genetic Susceptibility to Multiple Sclerosis: The Role of FOXP3 Gene Polymorphism

INTRODUCTION

It is well recognized that both genetic and environmental factors play an important role in the pathogenesis of multiple sclerosis (MS). Immune pathogenesis of MS focuses on pathogenic CD4+ T lymphocytes. CD4+CD25+ regulatory T cells have suppressive function in this cell group. FOXP3 (forkhead boxP3) transcription factor is a key structure in the development and function of regulatory cells. Functional alterations in FOXP3 gene expression have been observed in various autoimmune diseases.

METHODS

We screened a non-synonymous coding single nucleotide polymorphism (exon +2710 C/T) (rs2232369) of human FOXP3 gene in 148 MS patients (118 with Relapsing Remitting MS, 30 with Secondary Progressive MS) and 102 age- and sex-matched healthy controls. The association of polymorphisms with susceptibility, and course of the disease was evaluated.

RESULTS

We could not detect any single nucleotide polymorphism in MS patients, however, polymorphic allele was detected in 3% of the control group. Consequently, a genetic association between the FOXP3 gene polymorphism and MS was not revealed.

CONCLUSION

The distribution of this polymorphism has not been screened in any other MS populations before. Although we could not succeed to find any association between susceptibility to MS and screened FOXP3 gene polymorphisms, we suggest that this particular polymorphism is not appropriate for these kind of studies in the future.

Osteoarthritis year 2013 in review: genetics and genomics

Progress in genetic research has delivered important highlights in the last year. One of the widest impact is the publication of the Encyclopedia of DNA Elements (ENCODE) project showing the impressive complexity of the human genome and providing information useful for all areas of genetics. More specific of osteoarthritis (OA) has been the incorporation of DOT1-like, histone H3 methyltransferase (DOT1L) to the list of 11 OA loci with genome-wide significant association, the demonstration of significant overlap between OA genetics and height or body mass index (BMI) genetics, and the tentative prioritization of HMG-box transcription factor 1 (HBP1) in the 7q22 locus based on functional analysis. In addition, the first large scale analysis of DNA methylation has found modest differences between OA and normal cartilage, but has identified a subgroup of OA patients with a very differentiated phenotype. The role of DNA methylation in regulation of NOS2, SOX9, MMP13 and IL1B has been further clarified. MicroRNA expression studies in turn have shown some replication of differences between OA and control cartilage from previous profiling studies and have identified potential regulators of TGFβ signaling and of IL1β effects. In addition, non-coding RNAs showed promising results as serum biomarkers of cartilage damage. Gene expression microarray studies have found important differences between studies of hip or knee OA that reinforce the idea of joint specificity in OA. Expression differences between articular cartilage and other types of cartilage highlighted the WNT pathway whose regulation is proposed as critical for maintaining the articular cartilage phenotype. Many of these results need confirmation but they signal the exciting progress that is taking place in all areas of OA genetics, indicate questions requiring more study and augur further interesting discoveries.

BMP2/BMP4 colorectal cancer susceptibility loci in northern and southern European populations

Genome-wide association studies have successfully identified 20 colorectal cancer susceptibility loci. Amongst these, four of the signals are defined by tagging single nucleotide polymorphisms (SNPs) on regions 14q22.2 (rs4444235 and rs1957636) and 20p12.3 (rs961253 and rs4813802). These markers are located close to two of the genes involved in bone morphogenetic protein (BMP) signaling (BMP4 and BMP2, respectively). By investigating these four SNPs in an initial cohort of Spanish origin, we found substantial evidence that minor allele frequencies (MAFs) may be different in northern and southern European populations. Therefore, we genotyped three additional southern European cohorts comprising a total of 2028 cases and 4273 controls. The meta-analysis results show that only one of the association signals (rs961253) is effectively replicated in the southern European populations, despite adequate power to detect all four. The other three SNPs (rs4444235, rs1957636 and rs4813802) presented discordant results in MAFs and linkage disequilibrium patterns between northern and southern European cohorts. We hypothesize that this lack of replication could be the result of differential tagging of the functional variant in both sets of populations. Were this true, it would have complex consequences in both our ability to understand the nature of the real causative variants, as well as for further study designs.

From markers to molecular mechanisms: type 1 diabetes in the post-GWAS era

By the year 2000, a draft of the human genome sequence was completed. Millions of single-nucleotide polymorphisms (SNPs) had been deposited into public databases, and high throughput technologies were under development for SNP genotyping. At that time, it was predicted that large case control association studies would provide far better resolution and power than genome-wide linkage studies. Type 1 diabetes was one of the first phenotypes to be examined by genome-wide association studies (GWAS), and to date over 50 genomic regions have been associated with the disease. In general, the great majority of these loci individually contribute a relatively small degree of risk, and most loci lie outside of coding sequences. The identification of molecular mechanisms from these genomic data therefore remains a significant challenge. Here, we summarize genetic candidate, linkage, and association studies of type 1 diabetes and discuss a potential strategy to identify mechanisms of disease from genomic data.

The haplotype of the TGFβ1 gene associated with cerebral infarction in Chinese

BACKGROUND

Transforming growth factor beta1 (TGFβ1) is a multifunctional cytokine involved in inflammation and pathogenesis of atherosclerosis. The aim of the present study was to investigate the relationship between human TGFβ1 gene +869T>C (rs1800470), -509C>T (rs1800469) single nucleotide polymorphisms (SNPs) and haplotypes and cerebral infarction (CI) in a Chinese population.

METHODS

The genetic association study was performed in 450 Chinese patients (306 male and 144 female) with CI and 450 control subjects (326 male and 124 female). TGFβ1 gene +869T>C and -509C>T polymorphisms were identified with amplification refractory mutation system polymerase chain reaction and DNA sequencing method.

RESULTS

The individual SNPs analysis showed the +869T and -509C in an additive model (+869T vs +869C; -509 C vs T), +869TT genotype in a recessive model (TT vs TC+CC) and 509CC genotype in a dominant model (CC+ CT vs TT) were identified to be related to CI (P<0.05). +869T>C and -509C>T SNPs were in strong linkage disequilibrium (d'=0.87, R2=0.75). Haplotype analysis showed that +869C/-509T haplotype was associated with a significant decreased risk of CI (OR= 0.86, 95%CI, 0.70-0.92; P=0.007). Furthermore,+869T/-509C haplotype was associated with a significant increased risk of CI (OR=1.31, 95%CI, 1.10-2.03; P=0.019).

CONCLUSIONS

The results of this study indicate that polymorphisms and the haplotypes in the TGFβ1 gene might be genetic markers for CI in the Chinese population.

Blood pressure genetics: time to focus

This review briefly charts the recent history of the genetic study of high blood pressure (BP). After an inconsistent start it was hoped that very large genome-wide association studies (GWAS) might be able to provide some reliable answers. The two largest and most recent GWAS: CHARGE and GlobalBPgen were able to identify, despite some significant inconsistencies, genetic loci that accounted for only about 2% of the genetic factors believed to influence BP. The loci were associated with an estimated effect on BP of 1 mm Hg or less. No doubt many other loci exerting even smaller (<0.5 mm Hg) exist. This review contends that it is time to focus on the loci that can be viewed as confirmed, rather than extending the GWAS searches for less significant genetic influences. It is time to identify the precise deoxyribonucleic acid variants affecting BP, understand their mechanisms of action and think of ways in which such knowledge can be used to prevent and treat high BP in novel, effective, and possibly tailored ways.

Adaptive clustering and adaptive weighting methods to detect disease associated rare variants

Current statistical methods to test association between rare variants and phenotypes are essentially the group-wise methods that collapse or aggregate all variants in a predefined group into a single variant. Comparing with the variant-by-variant methods, the group-wise methods have their advantages. However, two factors may affect the power of these methods. One is that some of the causal variants may be protective. When both risk and protective variants are presented, it will lose power by collapsing or aggregating all variants because the effects of risk and protective variants will counteract each other. The other is that not all variants in the group are causal; rather, a large proportion is believed to be neutral. When a large proportion of variants are neutral, collapsing or aggregating all variants may not be an optimal solution. We propose two alternative methods, adaptive clustering (AC) method and adaptive weighting (AW) method, aiming to test rare variant association in the presence of neutral and/or protective variants. Both of AC and AW are applicable to quantitative traits as well as qualitative traits. Results of extensive simulation studies show that AC and AW have similar power and both of them have clear advantages from power to computational efficiency comparing with existing group-wise methods and existing data-driven methods that allow neutral and protective variants. We recommend AW method because AW method is computationally more efficient than AC method.

The Brisbane Systems Genetics Study: genetical genomics meets complex trait genetics

There is growing evidence that genetic risk factors for common disease are caused by hereditary changes of gene regulation acting in complex pathways. Clearly understanding the molecular genetic relationships between genetic control of gene expression and its effect on complex diseases is essential. Here we describe the Brisbane Systems Genetics Study (BSGS), a family-based study that will be used to elucidate the genetic factors affecting gene expression and the role of gene regulation in mediating endophenotypes and complex diseases.

BSGS comprises of a total of 962 individuals from 314 families, for which we have high-density genotype, gene expression and phenotypic data. Families consist of combinations of both monozygotic and dizygotic twin pairs, their siblings, and, for 72 families, both parents. A significant advantage of the inclusion of parents is improved power to disentangle environmental, additive genetic and non-additive genetic effects of gene expression and measured phenotypes. Furthermore, it allows for the estimation of parent-of-origin effects, something that has not previously been systematically investigated in human genetical genomics studies. Measured phenotypes available within the BSGS include blood phenotypes and biochemical traits measured from components of the tissue sample in which transcription levels are determined, providing an ideal test case for systems genetics approaches.

We report results from an expression quantitative trait loci (eQTL) analysis using 862 individuals from BSGS to test for associations between expression levels of 17,926 probes and 528,509 SNP genotypes. At a study wide significance level approximately 15,000 associations were observed between expression levels and SNP genotypes. These associations corresponded to a total of 2,081 expression quantitative trait loci (eQTL) involving 1,503 probes. The majority of identified eQTL (87%) were located within cis-regions.

Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource

Genetic dissection of complex, polygenic trait variation is a key goal of medical and evolutionary genetics. Attempts to identify genetic variants underlying complex traits have been plagued by low mapping resolution in traditional linkage studies, and an inability to identify variants that cumulatively explain the bulk of standing genetic variation in genome-wide association studies (GWAS). Thus, much of the heritability remains unexplained for most complex traits. Here we describe a novel, freely available resource for the Drosophila community consisting of two sets of recombinant inbred lines (RILs), each derived from an advanced generation cross between a different set of eight highly inbred, completely resequenced founders. The Drosophila Synthetic Population Resource (DSPR) has been designed to combine the high mapping resolution offered by multiple generations of recombination, with the high statistical power afforded by a linkage-based design. Here, we detail the properties of the mapping panel of >1600 genotyped RILs, and provide an empirical demonstration of the utility of the approach by genetically dissecting alcohol dehydrogenase (ADH) enzyme activity. We confirm that a large fraction of the variation in this classic quantitative trait is due to allelic variation at the Adh locus, and additionally identify several previously unknown modest-effect trans-acting QTL (quantitative trait loci). Using a unique property of multiparental linkage mapping designs, for each QTL we highlight a relatively small set of candidate causative variants for follow-up work. The DSPR represents an important step toward the ultimate goal of a complete understanding of the genetics of complex traits in the Drosophila model system.

The changing landscape of genetic testing and its impact on clinical and laboratory services and research in Europe

The arrival of new genetic technologies that allow efficient examination of the whole human genome (microarray, next-generation sequencing) will impact upon both laboratories (cytogenetic and molecular genetics in the first instance) and clinical/medical genetic services. The interpretation of analytical results in terms of their clinical relevance and the predicted health status poses a challenge to both laboratory and clinical geneticists, due to the wealth and complexity of the information obtained. There is a need to discuss how to best restructure the genetic services logistically and to determine the clinical utility of genetic testing so that patients can receive appropriate advice and genetic testing. To weigh up the questions and challenges of the new genetic technologies, the European Society of Human Genetics (ESHG) held a series of workshops on 10 June 2010 in Gothenburg. This was part of an ESHG satellite symposium on the 'Changing landscape of genetic testing', co-organized by the ESHG Genetic Services Quality and Public and Professional Policy Committees. The audience consisted of a mix of geneticists, ethicists, social scientists and lawyers. In this paper, we summarize the discussions during the workshops and present some of the identified ways forward to improve and adapt the genetic services so that patients receive accurate and relevant information. This paper covers ethics, clinical utility, primary care, genetic services and the blurring boundaries between healthcare and research.

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.

Generating large-scale longitudinal data resources for aging research

OBJECTIVES

The need for large studies and the types of large-scale data resources (LSDRs) are discussed along with their general scientific utility, role in aging research, and affordability. The diversification of approaches to large-scale data resourcing is described in order to facilitate their use in aging research.

METHODS

The need for LSDRs is discussed in terms of (a) large sample size; (b) longitudinal design; (c) as platforms for additional investigator-initiated research projects; and (d) broad-based access to core genetic, biological, and phenotypic data.

DISCUSSION

It is concluded that a "lite-touch, lo-tech, lo-cost" approach to LSDRs is a viable strategy for the development of LSDRs and would enhance the likelihood of LSDRs being established which are dedicated to the wide range of important aging-related issues.

Is life law-like?

Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.

Association of transforming growth factor-β1 gene C-509T and T869C polymorphisms with atherosclerotic cerebral infarction in the Chinese: a case-control study

Background

Transforming growth factor-β1 (TGF-β1) is a multifunctional cytokine involved in inflammation and pathogenesis of atherosclerosis. There is scant information on the relation between variations within the TGF-β1 gene polymorphisms and risks of ischemic cerebrovascular diseases. Therefore, this case-controlled study was carried out to investigate the possible association of the TGF-β1 gene C-509T and T869C polymorphisms, and their combined genotypes with the risk of atherosclerotic cerebral infarction (CI) in the Chinese population.

Results

We recruited 164 CI patients and 167 healthy control subjects who were frequency-matched for age and gender. The frequencies of the -509TT genotype and T allele gene were significantly higher in the CI group (P = 0.007, P = 0.006). The frequencies of +869CC genotype and C allele were higher in the CI group (P = 0.002, P = 0.004). In the CI group, the individuals with -509TT genotype had a significantly higher level of plasma triglyceride (TG) (P = 0.017). +869CC genotype correlated significantly with higher level of plasma low density lipoprotein cholesterol (LDL-c) in the CI group (P = 0.015). With haplotype analysis, the frequency of the -509T/+869C combined genotype was significantly higher in the CI group than in controls (P < 0.001).

Conclusions

Our study suggests that C-509T and T869C gene polymorphisms in TGF-β1 may be a critical risk factor of genetic susceptibility to CI in the Chinese population.

Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method

To develop a model synthesis method for parsimoniously modeling gene-environmental interactions (GEI) associated with clinical outcomes and phenotypes. The AMBROSIA model synthesis approach utilizes the k-way interaction information (KWII), an information-theoretic metric capable of identifying variable combinations associated with GEI. For model synthesis, AMBROSIA considers relevance of combinations to the phenotype, it precludes entry of combinations with redundant information, and penalizes for unjustifiable complexity; each step is KWII based. The performance and power of AMBROSIA were evaluated with simulations and Genetic Association Workshop 15 (GAW15) data sets of rheumatoid arthritis (RA). AMBROSIA identified parsimonious models in data sets containing multiple interactions with linkage disequilibrium present. For the GAW15 data set containing 9187 single-nucleotide polymorphisms, the parsimonious AMBROSIA model identified nine RA-associated combinations with power >90%. AMBROSIA was compared with multifactor dimensionality reduction across several diverse models and had satisfactory power. Software source code is available from http://www.cse.buffalo.edu/DBGROUP/bioinformatics/resources.html. AMBROSIA is a promising method for GEI model synthesis.

Mapping of a milk production quantitative trait locus to a 1.056 Mb region on bovine chromosome 5 in the Fleckvieh dual purpose cattle breed

BACKGROUND

In a previous study in the Fleckvieh dual purpose cattle breed, we mapped a quantitative trait locus (QTL) affecting milk yield (MY1), milk protein yield (PY1) and milk fat yield (FY1) during first lactation to the distal part of bovine chromosome 5 (BTA5), but the confidence interval was too large for positional cloning of the causal gene. Our objective here was to refine the position of this QTL and to define the candidate region for high-throughput sequencing.

METHODS

In addition to those previously studied, new Fleckvieh families were genotyped, in order to increase the number of recombination events. Twelve new microsatellites and 240 SNP markers covering the most likely QTL region on BTA5 were analysed. Based on haplotype analysis performed in this complex pedigree, families segregating for the low frequency allele of this QTL (minor allele) were selected. Single- and multiple-QTL analyses using combined linkage and linkage disequilibrium methods were performed.

RESULTS

Single nucleotide polymorphism haplotype analyses on representative family sires and their ancestors revealed that the haplotype carrying the minor QTL allele is rare and most probably originates from a unique ancestor in the mapping population. Analyses of different subsets of families, created according to the results of haplotype analysis and availability of SNP and microsatellite data, refined the previously detected QTL affecting MY1 and PY1 to a region ranging from 117.962 Mb to 119.018 Mb (1.056 Mb) on BTA5. However, the possibility of a second QTL affecting only PY1 at 122.115 Mb was not ruled out.

CONCLUSION

This study demonstrates that targeting families segregating for a less frequent QTL allele is a useful method. It improves the mapping resolution of the QTL, which is due to the division of the mapping population based on the results of the haplotype analysis and to the increased frequency of the minor allele in the families. Consequently, we succeeded in refining the region containing the previously detected QTL to 1 Mb on BTA5. This candidate region contains 27 genes with unknown or partially known function(s) and is small enough for high-throughput sequencing, which will allow future detailed analyses of candidate genes.

Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health

Differences in diet appear to contribute substantially to the burden of disease in populations, and therefore changes in diet could lead to major improvements in public health. This is predicated on the reliable identification of causal effects of nutrition on health, and unfortunately nutritional epidemiology has deficiencies in terms of identifying these. This is reflected in the many cases where observational studies have suggested that a nutritional factor is protective against disease, and randomized controlled trials have failed to verify this. The use of genetic variants as proxy measures of nutritional exposure-an application of the Mendelian randomization principle-can contribute to strengthening causal inference in this field. Genetic variants are not subject to bias due to reverse causation (disease processes influencing exposure, rather than vice versa) or recall bias, and if obvious precautions are applied are not influenced by confounding or attenuation by errors. This is illustrated in the case of epidemiological studies of alcohol intake and various health outcomes, through the use of genetic variants related to alcohol metabolism (in ALDH2 and ADH1B). Examples from other areas of nutritional epidemiology and of the informative nature of gene-environment interactions interpreted within the Mendelian randomization framework are presented, and the potential limitations of the approach addressed.

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.

Detecting rare variants for complex traits using family and unrelated data

Large genome-wide association studies (GWAS) have been performed to detect common genetic variants involved in common diseases, but most of the variants found this way account for only a small portion of the trait variance. Furthermore, candidate gene-based resequencing suggests that many rare genetic variants contribute to the trait variance of common diseases. Here we propose two designs, sibpair and unrelated-case designs, to detect rare genetic variants in either a candidate gene-based or genome-wide association analysis. First we show that we can detect and classify together rare risk haplotypes using a relatively small sample with either of these designs, and then have increased power to test association in a larger case-control sample. This method can also be applied to resequencing data. Next we apply the method to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease (CAD) and hypertension (HT) data, the latter being the only trait for which no genome-wide association evidence was reported in the original WTCCC study, and identify one interesting gene associated with HT and four associated with CAD at a genome-wide significance level of 5%. These results suggest that searching for rare genetic variants is feasible and can be fruitful in current GWAS, candidate gene studies or resequencing studies.

Interpretation of association signals and identification of causal variants from genome-wide association studies

GWAS have been successful in identifying disease susceptibility loci, but it remains a challenge to pinpoint the causal variants in subsequent fine-mapping studies. A conventional fine-mapping effort starts by sequencing dozens of randomly selected samples at susceptibility loci to discover candidate variants, which are then placed on custom arrays or used in imputation algorithms to find the causal variants. We propose that one or several rare or low-frequency causal variants can hitchhike the same common tag SNP, so causal variants may not be easily unveiled by conventional efforts. Here, we first demonstrate that the true effect size and proportion of variance explained by a collection of rare causal variants can be underestimated by a common tag SNP, thereby accounting for some of the "missing heritability" in GWAS. We then describe a case-selection approach based on phasing long-range haplotypes and sequencing cases predicted to harbor causal variants. We compare this approach with conventional strategies on a simulated data set, and we demonstrate its advantages when multiple causal variants are present. We also evaluate this approach in a GWAS on hearing loss, where the most common causal variant has a minor allele frequency (MAF) of 1.3% in the general population and 8.2% in 329 cases. With our case-selection approach, it is present in 88% of the 32 selected cases (MAF = 66%), so sequencing a subset of these cases can readily reveal the causal allele. Our results suggest that thinking beyond common variants is essential in interpreting GWAS signals and identifying causal variants.

Genomic Approaches to the Host Response to Pathogens

The need to better understand host–pathogen interactions has risen with the expansion in genomics and related technologies. This chapter focuses on two aspects of the host response to pathogens where major advances are being made using genomic approaches. The availability of complete genomic sequences of an expanding number of pathogens, the human and mouse genome sequences, and the advent of genome-wide genotyping and gene expression profiling has opened up new avenues of investigation in the field. The genotype of the pathogen plays a major role in the response of the host to infection with more virulent pathogenic strains possessing the capability to interfere with the host immune response. In addition, different individuals in a population can have very different responses to a genetically identical pathogen. Part of the differential response is governed by the underlying genetic differences between individuals. The advent of genome-wide genotyping using single nucleotide polymorphisms or microsatellite markers is leading to major advances in molecular epidemiology. The future impact of genomic approaches on the development of diagnostics and therapeutics is discussed for infectious diseases. This includes defining the basis of genetic susceptibility to infection and system-wide molecular response to a pathogen.

Toward the use of genomics to study microevolutionary change in bacteria

Bacteria evolve rapidly in response to the environment they encounter. Some environmental changes are experienced numerous times by bacteria from the same population, providing an opportunity to dissect the genetic basis of adaptive evolution. Here I discuss two examples in which the patterns of rapid change provide insight into medically important bacterial phenotypes, namely immune escape by Neisseria meningitidis and host specificity of Campylobacter jejuni. Genomic analysis of populations of bacteria from these species holds great promise but requires appropriate concepts and statistical tools.

Genetic variation and effects on human eating behavior

Feeding is a physiological process, influenced by genetic factors and the environment. In recent years, many studies have been performed to unravel the involvement of genetics in both eating behavior and its pathological forms: eating disorders and obesity. In this review, we provide a condensed introduction on the neurological aspects of eating and we describe the current status of research into the genetics of eating behavior, primarily focused on specific traits such as taste, satiation, and hunger. This is followed by an overview on the genetic studies done to unravel the heritable background of obesity and eating disorders. We examine the discussion currently taking place in the field of genetics of complex disorders and phenotypes on how to perform good and powerful studies, with the use of large-scale whole-genome association studies as one of the possible solutions. In the final part of this review, we give our view on the latest developments, including endophenotype approaches and animal studies. Studies of endophenotypes of eating behavior may help to identify core traits that are genetically influenced. Such studies would yield important knowledge on the underlying biological scaffold on which diagnostic criteria for eating disorders could be based and would provide information to influence eating behavior toward healthier living.

Genoeconomics: promises and caveats for a new field

Since the publication 150 years ago of the Origin of Species, the scientific study of the evolution of human-specific traits has been the focus of many efforts from very different areas of science. Nowadays, after a century and a half of research, impressive results have accumulated, particularly about those traits that presumably would "make us human," setting us apart from the rest of primates, and about how these traits would have evolved. Over the last few years, a new area of research, genoeconomics, has started to make important contributions toward the study of hominization. Here, I review the foundations and promises of this new branch of science and discuss a few of the pitfalls that may hinder its advance.