Validating, augmenting and refining genome-wide association signals

Genome-wide association study identifies a potent locus associated with human opioid sensitivity

Opioids, such as morphine and fentanyl, are widely used as effective analgesics for the treatment of acute and chronic pain. In addition, the opioid system has a key role in the rewarding effects of morphine, ethanol, cocaine and various other drugs. Although opioid sensitivity is well known to vary widely among individual subjects, several candidate genetic polymorphisms reported so far are not sufficient for fully understanding the wide range of interindividual differences in human opioid sensitivity. By conducting a multistage genome-wide association study (GWAS) in healthy subjects, we found that genetic polymorphisms within a linkage disequilibrium block that spans 2q33.3-2q34 were strongly associated with the requirements for postoperative opioid analgesics after painful cosmetic surgery. The C allele of the best candidate single-nucleotide polymorphism (SNP), rs2952768, was associated with more analgesic requirements, and consistent results were obtained in patients who underwent abdominal surgery. In addition, carriers of the C allele in this SNP exhibited less vulnerability to severe drug dependence in patients with methamphetamine dependence, alcohol dependence, and eating disorders and a lower 'Reward Dependence' score on a personality questionnaire in healthy subjects. Furthermore, the C/C genotype of this SNP was significantly associated with the elevated expression of a neighboring gene, CREB1. These results show that SNPs in this locus are the most potent genetic factors associated with human opioid sensitivity known to date, affecting both the efficacy of opioid analgesics and liability to severe substance dependence. Our findings provide valuable information for the personalized treatment of pain and drug dependence.

A Genome-Wide Association Study of spontaneous preterm birth in a European population

Background: Preterm birth is defined as a birth prior to 37 completed weeks’ gestation. It affects more than 10% of all births worldwide, and is the leading cause of neonatal mortality in non-anomalous newborns. Even if the preterm newborn survives, there is an increased risk of lifelong morbidity. Despite the magnitude of this public health problem, the etiology of spontaneous preterm birth is not well understood. Previous studies suggest that genetics is an important contributing factor. We therefore employed a genome-wide association approach to explore possible fetal genetic variants that may be associated with spontaneous preterm birth.Methods: We obtained preterm birth phenotype and genotype data from the National Center for Biotechnology Information Genotypes and Phenotypes Database (study accession phs000103.v1.p1). This dataset contains participants collected by the Danish National Birth Cohort and includes 1000 preterm births and 1000 term births as controls. Whole genomes were genotyped on the Illumina Human660W-Quad_v1_A platform, which contains more than 500,000 markers. After data quality control, we performed genome-wide association studies for the 22 autosomal chromosomes.Results: No single nucleotide polymorphism reached genome-wide significance after Bonferroni correction for multiple testing.Conclusion: We found no evidence of genetic association with spontaneous preterm birth in this European population. Approaches that facilitate detection of both common and rare genetic variants, such as evaluation of high-risk pedigrees and genome sequencing, may be more successful in identifying genes associated with spontaneous preterm birth.

Genomics and the Evolution of Phenotypic Traits

Evolutionary genetics has entered an unprecedented era of discovery, catalyzed in large part by the development of technologies that provide information about genome sequence and function. An important benefit is the ability to move beyond a handful of model organisms in lab settings to identify the genetic basis for evolutionarily interesting traits in many organisms in natural settings. Other benefits are the abilities to identify causal mutations and validate their phenotypic consequences more readily and in many more species. Genomic technologies have reinvigorated interest in some of the most fundamental and persistent questions in evolutionary genetics, revealed previously unsuspected evolutionary phenomena, and opened the door to a wide range of new questions.

Regional replication of association with refractive error on 15q14 and 15q25 in the Age-Related Eye Disease Study cohort

PURPOSE

Refractive error is a complex trait with multiple genetic and environmental risk factors, and is the most common cause of preventable blindness worldwide. The common nature of the trait suggests the presence of many genetic factors that individually may have modest effects. To achieve an adequate sample size to detect these common variants, large, international collaborations have formed. These consortia typically use meta-analysis to combine multiple studies from many different populations. This approach is robust to differences between populations; however, it does not compensate for the different haplotypes in each genetic background evidenced by different alleles in linkage disequilibrium with the causative variant. We used the Age-Related Eye Disease Study (AREDS) cohort to replicate published significant associations at two loci on chromosome 15 from two genome-wide association studies (GWASs). The single nucleotide polymorphisms (SNPs) that exhibited association on chromosome 15 in the original studies did not show evidence of association with refractive error in the AREDS cohort. This paper seeks to determine whether the non-replication in this AREDS sample may be due to the limited number of SNPs chosen for replication.

METHODS

We selected all SNPs genotyped on the Illumina Omni2.5v1_B array or custom TaqMan assays or imputed from the GWAS data, in the region surrounding the SNPs from the Consortium for Refractive Error and Myopia study. We analyzed the SNPs for association with refractive error using standard regression methods in PLINK. The effective number of tests was calculated using the Genetic Type I Error Calculator.

RESULTS

Although use of the same SNPs used in the Consortium for Refractive Error and Myopia study did not show any evidence of association with refractive error in this AREDS sample, other SNPs within the candidate regions demonstrated an association with refractive error. Significant evidence of association was found using the hyperopia categorical trait, with the most significant SNPs rs1357179 on 15q14 (p=1.69×10⁻³) and rs7164400 on 15q25 (p=8.39×10⁻⁴), which passed the replication thresholds.

CONCLUSIONS

This study adds to the growing body of evidence that attempting to replicate the most significant SNPs found in one population may not be significant in another population due to differences in the linkage disequilibrium structure and/or allele frequency. This suggests that replication studies should include less significant SNPs in an associated region rather than only a few selected SNPs chosen by a significance threshold.

Epilepsy, hippocampal sclerosis and febrile seizures linked by common genetic variation around SCN1A

Epilepsy comprises several syndromes, amongst the most common being mesial temporal lobe epilepsy with hippocampal sclerosis. Seizures in mesial temporal lobe epilepsy with hippocampal sclerosis are typically drug-resistant, and mesial temporal lobe epilepsy with hippocampal sclerosis is frequently associated with important co-morbidities, mandating the search for better understanding and treatment. The cause of mesial temporal lobe epilepsy with hippocampal sclerosis is unknown, but there is an association with childhood febrile seizures. Several rarer epilepsies featuring febrile seizures are caused by mutations in SCN1A, which encodes a brain-expressed sodium channel subunit targeted by many anti-epileptic drugs. We undertook a genome-wide association study in 1018 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 7552 control subjects, with validation in an independent sample set comprising 959 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 3591 control subjects. To dissect out variants related to a history of febrile seizures, we tested cases with mesial temporal lobe epilepsy with hippocampal sclerosis with (overall n = 757) and without (overall n = 803) a history of febrile seizures. Meta-analysis revealed a genome-wide significant association for mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures at the sodium channel gene cluster on chromosome 2q24.3 [rs7587026, within an intron of the SCN1A gene, P = 3.36 × 10(-9), odds ratio (A) = 1.42, 95% confidence interval: 1.26-1.59]. In a cohort of 172 individuals with febrile seizures, who did not develop epilepsy during prospective follow-up to age 13 years, and 6456 controls, no association was found for rs7587026 and febrile seizures. These findings suggest SCN1A involvement in a common epilepsy syndrome, give new direction to biological understanding of mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures, and open avenues for investigation of prognostic factors and possible prevention of epilepsy in some children with febrile seizures.

Functional screening in Drosophila identifies Alzheimer's disease susceptibility genes and implicates Tau-mediated mechanisms

Using a Drosophila model of Alzheimer's disease (AD), we systematically evaluated 67 candidate genes based on AD-associated genomic loci (P < 10(-4)) from published human genome-wide association studies (GWAS). Genetic manipulation of 87 homologous fly genes was tested for modulation of neurotoxicity caused by human Tau, which forms neurofibrillary tangle pathology in AD. RNA interference (RNAi) targeting 9 genes enhanced Tau neurotoxicity, and in most cases reciprocal activation of gene expression suppressed Tau toxicity. Our screen implicates cindr, the fly ortholog of the human CD2AP AD susceptibility gene, as a modulator of Tau-mediated disease mechanisms. Importantly, we also identify the fly orthologs of FERMT2 and CELF1 as Tau modifiers, and these loci have been independently validated as AD susceptibility loci in the latest GWAS meta-analysis. Both CD2AP and FERMT2 have been previously implicated with roles in cell adhesion, and our screen additionally identifies a fly homolog of the human integrin adhesion receptors, ITGAM and ITGA9, as a modifier of Tau neurotoxicity. Our results highlight cell adhesion pathways as important in Tau toxicity and AD susceptibility and demonstrate the power of model organism genetic screens for the functional follow-up of human GWAS.

Genetic associations with expression for genes implicated in GWAS studies for atherosclerotic cardiovascular disease and blood phenotypes

Genome-wide association studies (GWAS) have uncovered many genetic associations for cardiovascular disease (CVD). However, data are limited regarding causal genetic variants within implicated loci. We sought to identify regulatory variants (cis- and trans-eQTLs) affecting expression levels of 93 genes selected by their proximity to SNPs with significant associations in prior GWAS for CVD traits. Expression levels were measured by qRT-PCR in leukocytes from 1846 Framingham Heart Study participants. An additive genetic model was applied to 2.5 million imputed SNPs for each gene. Approximately 45% of genes (N = 38) harbored at least one cis-eSNP after a regional multiple-test adjustment. Applying a more rigorous significance threshold (P < 5 × 10(-8)), we found the expression level of 10 genes was significantly associated with more than one cis-eSNP. The top cis-eSNPs for 7 of these 10 genes exhibited moderate-to-strong association with ≥ 1 CVD clinical phenotypes. Several eSNPs or proxy SNPs (r(2) = 1) were replicated by other eQTL studies. After adjusting for the lead GWAS SNPs for the 10 genes, expression variances explained by top cis-eSNPs were attenuated markedly for LPL, FADS2 and C6orf184, suggesting a shared genetic basis for the GWAS and expression trait. A significant association between cis-eSNPs, gene expression and lipid levels was discovered for LPL and C6orf184. In conclusion, strong cis-acting variants are localized within nearly half of the GWAS loci studied, with particularly strong evidence for a regulatory role of the top GWAS SNP for expression of LPL, FADS2 and C6orf184.

Meta-analysis of mismatch repair polymorphisms within the cogent consortium for colorectal cancer susceptibility

In the last four years, Genome-Wide Association Studies (GWAS) have identified sixteen low-penetrance polymorphisms on fourteen different loci associated with colorectal cancer (CRC). Due to the low risks conferred by known common variants, most of the 35% broad-sense heritability estimated by twin studies remains unexplained. Recently our group performed a case-control study for eight Single Nucleotide Polymorphisms (SNPs) in 4 CRC genes. The present investigation is a follow-up of that study. We have genotyped six SNPs that showed a positive association and carried out a meta-analysis based on eight additional studies comprising in total more than 8000 cases and 6000 controls. The estimated recessive odds ratio for one of the SNPs, rs3219489 (MUTYH Q338H), decreased from 1.52 in the original Swedish study, to 1.18 in the Swedish replication, and to 1.08 in the initial meta-analysis. Since the corresponding summary probability value was 0.06, we decided to retrieve additional information for this polymorphism. The incorporation of six further studies resulted in around 13000 cases and 13000 controls. The newly updated OR was 1.03. The results from the present large, multicenter study illustrate the possibility of decreasing effect sizes with increasing samples sizes. Phenotypic heterogeneity, differential environmental exposures, and population specific linkage disequilibrium patterns may explain the observed difference of genetic effects between Sweden and the other investigated cohorts.

Postgwas: advanced GWAS interpretation in R

We present a comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. In the spirit of comparable tools for gene-expression analysis, we attempt to unify and simplify several procedures that are essential for the interpretation of GWAS results. This includes the generation of advanced Manhattan and regional association plots including rare variant display as well as novel interaction network analysis tools for the investigation of systems-biology aspects. Our package supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R. Previous software of that range is dispersed over a wide range of platforms and mostly not adaptable for custom work pipelines. We demonstrate the utility of this package by providing an example workflow on a publicly available dataset.

Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.

As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.

Replication study of RAD54B and GREB1 polymorphisms and risk of PCOS in Han Chinese

A previous genome-wide association study (GWAS) of polycystic ovary syndrome (PCOS) identified several susceptibility loci, with P-values about 10⁻⁵. In the present study, an independent cohort was used for a replication study to evaluate the association of RAD54B and GREB1 with polycystic ovary syndrome (PCOS) in the Han Chinese population. Four single-nucleotide polymorphisms (SNP), rs2930961 (RAD54B), rs12470971, rs11686574 and rs6740248 (GREB1), were genotyped in 1124 PCOS patients and 1067 healthy controls from the Han Chinese population. Real-time quantitative PCR by TaqMan-MGB probe assay was applied for genotyping. The allele and genotype frequencies of these four SNP were not significantly different in the replication cohort. However, the minor allele frequency of rs2930961 was significantly different in hyperandrogenism of PCOS. After meta-analysis by combining the results of these two studies, there was a non-significant trend for the association of rs2930961 (RAD54B) with PCOS. RAD54B and GREB1 gene polymorphisms may not be associated with PCOS in the Han Chinese population. Nevertheless, RAD54B may contribute to hyperandrogenism in PCOS patients.

GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.

The genetic basis for interindividual immune response variation to measles vaccine: new understanding and new vaccine approaches

The live-attenuated measles vaccine is effective, but measles outbreaks still occur in vaccinated populations. This warrants elucidation of the determinants of measles vaccine-induced protective immunity. Interindividual variability in markers of measles vaccine-induced immunity, including neutralizing antibody levels, is regulated in part by host genetic factor variations. This review summarizes recent advances in our understanding of measles vaccine immunogenetics relative to the perspective of developing better measles vaccines. Important genetic regulators of measles vaccine-induced immunity, such as HLA class I and HLA class II genotypes, single nucleotide polymorphisms in cytokine/cytokine receptor genes (IL12B, IL12RB1, IL2, IL10) and the cell surface measles virus receptor CD46 gene, have been identified and independently replicated. New technologies present many opportunities for identification of novel genetic signatures and genetic architectures. These findings help explain a variety of immune response-related phenotypes and promote a new paradigm of 'vaccinomics' for novel vaccine development.

Pleiotropy in complex traits: challenges and strategies

Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

The power of meta-analysis in genome-wide association studies

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

Genome-wide association study of antibody response to Newcastle disease virus in chicken

Background

Since the first outbreak in Indonesia in 1926, Newcastle disease has become one of the most common and contagious bird diseases throughout the world. To date, enhancing host antibody response by vaccination remains the most efficient strategy to control outbreaks of Newcastle disease. Antibody response plays an important role in host resistance to Newcastle disease, and selection for antibody response can effectively improve disease resistance in chickens. However, the molecular basis of the variation in antibody response to Newcastle disease virus (NDV) is not clear. The aim of this study was to detect genes modulating antibody response to NDV by a genome-wide association study (GWAS) in chickens.

Results

To identify genes or chromosomal regions associated with antibody response to NDV after immunization, a GWAS was performed using 39,833 SNP markers in a chicken F₂ resource population derived from a cross between two broiler lines that differed in their resistance. Two SNP effects reached 5% Bonferroni genome-wide significance (P<1.26×10^-6). These two SNPs, rs15354805 and rs15355555, were both on chicken (Gallus gallus) chromosome 1 and spanned approximately 600 Kb, from 100.4 Mb to 101.0 Mb. Rs15354805 is in intron 7 of the chicken Roundabout, axon guidance receptor, homolog 2 (ROBO2) gene, and rs15355555 is located about 243 Kb upstream of ROBO2. Rs15354805 explained 5% of the phenotypic variation in antibody response to NDV, post immunization, in chickens. Rs15355555 had a similar effect as rs15354805 because of its linkage disequilibrium with rs15354805 (r²=0.98).

Conclusion

The region at about 100 Mb from the proximal end of chicken chromosome 1, including the ROBO1 and ROBO2 genes, has a strong effect on the antibody response to the NDV in chickens. This study paves the way for further research on the host immune response to NDV.

Meta-analysis methods for genome-wide association studies and beyond

Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

A multi-centre clinico-genetic analysis of the VPS35 gene in Parkinson disease indicates reduced penetrance for disease-associated variants

Background

Two recent studies identified a mutation (p.Asp620Asn) in the vacuolar protein sorting 35 gene as a cause for an autosomal dominant form of Parkinson disease . Although additional missense variants were described, their pathogenic role yet remains inconclusive.

Methods and results

We performed the largest multi-center study to ascertain the frequency and pathogenicity of the reported vacuolar protein sorting 35 gene variants in more than 15,000 individuals worldwide. p.Asp620Asn was detected in 5 familial and 2 sporadic PD cases and not in healthy controls, p.Leu774Met in 6 cases and 1 control, p.Gly51Ser in 3 cases and 2 controls. Overall analyses did not reveal any significant increased risk for p.Leu774Met and p.Gly51Ser in our cohort.

Conclusions

Our study apart from identifying the p.Asp620Asn variant in familial cases also identified it in idiopathic Parkinson disease cases, and thus provides genetic evidence for a role of p.Asp620Asn in Parkinson disease in different populations worldwide.

Association of the ADRB2 Gly16Arg and Glu27Gln polymorphisms with athlete status

The β-adrenergic receptors (β-ARs) have known functional roles in cardiovascular and pulmonary responses as well as the appropriate substrate metabolism required for athletic ability. Thus, the β-AR genes are plausible candidates for the variations observed in strength/power and endurance performance levels. The aims of the present study were to compare the frequency distribution of the ADRB2 Gly16Arg and ADRB2 Glu27Gln polymorphisms among athletes of sports with different metabolic and cardiopulmonary demands (endurance vs. strength/power) and to test the association between the Gly16Arg and Glu27Gln genotypes and athlete status. The study was performed in a group of 223 Polish athletes of the highest nationally competitive standard (123 endurance-oriented athletes and 100 strength/power athletes). Control samples were prepared from 354 unrelated, sedentary volunteers. The χ² test of independence revealed that the frequencies of the Gly16 and Glu27 alleles were significantly higher in the strength/power athletes than in the controls (69.0% vs. 59.7%; df = 1, P = 0.017 and 51% vs. 41.5%; df = 1 P = 0.017, respectively). The study showed that ADRB2 Gly16Arg and Glu27Gln markers are associated with athlete status in Polish athletes. An excess of Gly16 and Glu27 alleles and the Gly16:Glu27 haplotype observed in the strength/power athlete subgroup suggests that the Gly16 and Glu27 alleles might increase the probability of becoming a strength/power athlete rather than an endurance-oriented athlete.

A survey of putative anxiety-associated genes in panic disorder patients with and without bladder symptoms

BACKGROUND

We have previously described a subtype of panic disorder (PD) that we termed 'bladder syndrome', characterized by urological and bladder symptoms (and possibly interstitial cystitis) in the patients and/or their family members and confirmed the validity of this subset in family linkage and association analysis. In this study, we determine (a) whether 20 single-nucleotide polymorphisms (SNPs) reported in the literature can be replicated in a new PD dataset and (b) whether dividing the sample into those with and without the 'bladder syndrome' can help to resolve the genetic heterogeneity within this new sample.

METHODS

We selected 20 putative associated SNPs from the literature, taken from studies published since 2004. We tested these SNPs for association in a sample of 351 PD patients and 552 controls, and then divided them into subgroups of 92 patients from bladder families and 259 from nonbladder families.

RESULTS

(a) When analyzed in all PD patients, none of the 20 SNPs appeared to be replicated (except for SLC6A4 from our previous study, but in a sample that overlaps substantially with that in our previous report). (b) However, some intriguing findings emerged when we separated bladder from nonbladder families: SLC6A4, reported by us previously, yielded stronger evidence than before (P=0.0018) when examined only in nonbladder families, and in contrast, is not statistically significant in bladder families. Two other markers yielded nominally significant results in bladder families - rs5751876 in ADORA2A (P=0.046) and rs12579350 in TMEM16B (P=0.035) - but were not significant in nonbladder families. (c) Two markers had noticeably lower P-values when we differentiated the women and analyzed them separately - rs12579350 in TMEM16B (P-value decreased from 0.035, as above, to 0.00055) and a different SNP in ADORA2A, rs4822492 (P-value decreases from 0.07 to 0.028).

SIGNIFICANCE

Our results indicate that most of the 20 reported associations do not hold up when PD is analyzed as one group. However, our findings provide further evidence that PD with bladder symptoms may be genetically different from PD without bladder. We suggest that it is worth pursuing SLC6A4 in nonbladder PD, and ADORA2A and TMEM16B in bladder PD. Also, the possibility of a male-female difference in PD is worth pursuing. We also briefly discuss issues of replication and multiple tests.

Molecular hyperdiversity and evolution in very large populations

The genomic density of sequence polymorphisms critically affects the sensitivity of inferences about ongoing sequence evolution, function and demographic history. Most animal and plant genomes have relatively low densities of polymorphisms, but some species are hyperdiverse with neutral nucleotide heterozygosity exceeding 5%. Eukaryotes with extremely large populations, mimicking bacterial and viral populations, present novel opportunities for studying molecular evolution in sexually reproducing taxa with complex development. In particular, hyperdiverse species can help answer controversial questions about the evolution of genome complexity, the limits of natural selection, modes of adaptation and subtleties of the mutation process. However, such systems have some inherent complications and here we identify topics in need of theoretical developments. Close relatives of the model organisms Caenorhabditis elegans and Drosophila melanogaster provide known examples of hyperdiverse eukaryotes, encouraging functional dissection of resulting molecular evolutionary patterns. We recommend how best to exploit hyperdiverse populations for analysis, for example, in quantifying the impact of noncrossover recombination in genomes and for determining the identity and micro-evolutionary selective pressures on noncoding regulatory elements.

Candidate gene polymorphisms for behavioural adaptations during urbanization in blackbirds

Successful urban colonization by formerly rural species represents an ideal situation in which to study adaptation to novel environments. We address this issue using candidate genes for behavioural traits that are expected to play a role in such colonization events. We identified and genotyped 16 polymorphisms in candidate genes for circadian rhythms, harm avoidance and migratory and exploratory behaviour in 12 paired urban and rural populations of the blackbird Turdus merula across the Western Palaearctic. An exonic microsatellite in the SERT gene, a candidate gene for harm avoidance behaviour, exhibited a highly significant association with habitat type in an analysis conducted across all populations. Genetic divergence at this locus was consistent in 10 of the 12 population pairs; this contrasts with previously reported stochastic genetic divergence between these populations at random markers. Our results indicate that behavioural traits related to harm avoidance and associated with the SERT polymorphism experience selection pressures during most blackbird urbanization events. These events thus appear to be influenced by homogeneous adaptive processes in addition to previously reported demographic founder events.

Correction of phenotype misclassification based on high-discrimination genetic predictive risk models

Misclassification of phenotype status can seriously affect accuracy in association studies, including studies of genetic risk factors. A common problem is the classification of participants as nondiseased because of insufficient diagnostic workup or because participants have not been followed up long enough to develop disease. Some validated predictive models may have high discrimination in predicting disease. We suggest that information from such models can be used to predict the risk that a nondiseased participant will eventually develop disease and to recode the status of participants predicted to be at highest risk. We evaluate conditions under which recoding results in a maximal net improvement in the accuracy of phenotype classification. Net improvement is expected only when the positive likelihood ratio of the predictive model is larger than the inverse of the odds of disease among apparently nondiseased controls. We conducted simulations to probe the impact of reclassification on the power to detect new risk factors under several scenarios of classification accuracy of the previously developed models. We also apply this framework to a validated model of progression to advanced age-related macular degeneration that uses genetic and nongenetic variables (area under the curve = 0.915). In the training cohort (n = 2,937) and a separate validation cohort (n = 1,227), 195-272 and 78-91 nonprogressor participants, respectively, were reclassified as progressors. Correction of phenotype misclassification based on highly informative predictive models may be helpful in identifying additional genetic and other risk factors, when there are validated risk factors that provide strong discriminating ability.

Assessment of systematic effects of methodological characteristics on candidate genetic associations

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case-control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities

The success of genome-wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top-validated single-nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1-5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross-validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ~0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ~0.56; heritability ~38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni-tuebingen.de/software/MACLEAPS/.

Bridging the Communication Divide: A Role for Health Psychology in the Genomic Era

The application of genomics to population health has the potential to revolutionize the practice of medicine. Indeed, discoveries into the genomic basis of cancer and other common chronic diseases have resulted in new and improved predictive tests for identifying individuals at increased risk for these conditions and long before their onset occurs. When used properly, information gained from predictive genomic tests can be combined with other leading indicators (e.g., environmental and behavioral risk factors) to inform medical management decisions, preventive health practices, and risk-reducing strategies. However, genomics remains an emerging science and the translation of genomic discoveries into improved population health management remains elusive. There are divides in the translational science continuum at several junctures, and many of these divides could be narrowed or closed with additional data. For example, we know relatively little about how to effectively communicate with the public about the complex interplay among genomics, behavior, and health. Moreover, there is a need to develop better methods of counseling and educating the public in light of newly emerging knowledge about the genomic basis of health and disease. We assert that the discipline of psychology, and health psychology in particular, is well-poised to continue to make significant contributions to this growing area of science and practice. Through a focus on health-related social and behavioral research, psychology can lead the way in overcoming divides in communication, understanding, and action about genomics for the betterment of both individual and public health practices.

Osteoarthritis year 2012 in review: genetics and genomics

The field of genetics and genomics is a highly technological driven field that is advancing fast. The purpose of this year in review of genetics and genomics was to highlight the publications that apply these new technologies tools to improve understanding of the pathophysiology of osteoarthritis (OA). In addition, most recent developments in genetics and genomics research and their relevance to OA are discussed in this review.

A functional polymorphism in the CHRNA3 gene and risk of chronic obstructive pulmonary disease in a Korean population

A genome-wide association study has identified the 15q25 region as being associated with the risk of chronic obstructive pulmonary disease (COPD) in Caucasians. This study intended as a confirmatory assessment of this association in a Korean population. The rs6495309C > T polymorphism in the promoter of nicotinic acetylcholine receptor alpha subunit 3 (CHRNA3) gene was investigated in a case-control study that consisted of 406 patients with COPD and 394 healthy control subjects. The rs6495309 CT or TT genotype was associated with a significantly decreased risk of COPD when compared to the rs6495309 CC genotype (adjusted odds ratio = 0.69, 95% confidence interval = 0.50-0.95, P = 0.023). The effect of the rs6495309C > T on the risk of COPD was more evident in moderate to very severe COPD than in mild COPD under a dominant model for the variant T allele (P = 0.024 for homogeneity). The CHRNA3 rs6495309C > T polymorphism on chromosome 15q25 is associated with the risk of COPD in a Korean population.

Identification of the BCAR1-CFDP1-TMEM170A locus as a determinant of carotid intima-media thickness and coronary artery disease risk

BACKGROUND

Carotid intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. To date, large-scale investigations of genetic determinants of cIMT are sparse.

METHODS AND RESULTS

To identify cIMT-associated genes and genetic variants, a discovery analysis using the Illumina 200K CardioMetabochip was conducted in 3430 subjects with detailed ultrasonographic determinations of cIMT from the IMPROVE (Carotid Intima Media Thickness [IMT] and IMT-Progression as Predictors of Vascular Events in a High Risk European Population) study. Segment-specific IMT measurements of common carotid, bifurcation, and internal carotid arteries, and composite IMT variables considering the whole carotid tree (IMT(mean), IMT(max), and IMT(mean-max)), were analyzed. A replication stage investigating 42 single-nucleotide polymorphisms for association with common carotid IMT was undertaken in 5 independent European cohorts (total n=11,590). A locus on chromosome 16 (lead single-nucleotide polymorphism rs4888378, intronic in CFDP1) was associated with cIMT at significance levels passing multiple testing correction at both stages (array-wide significant discovery P=6.75 × 10(-7) for IMT(max); replication P=7.24×10(-6) for common cIMT; adjustments for sex, age, and population substructure where applicable; minor allele frequency 0.43 and 0.41, respectively). The protective minor allele was associated with lower carotid plaque score in a replication cohort (P=0.04, n=2120) and lower coronary artery disease risk in 2 case-control studies of subjects with European ancestry (odds ratio [95% confidence interval] 0.83 [0.77-0.90], P=6.53 × 10(-6), n=13 591; and 0.95 [0.92-0.98], P=1.83 × 10(-4), n=82 297, respectively). Queries of human biobank data sets revealed associations of rs4888378 with nearby gene expression in vascular tissues (n=126-138).

CONCLUSIONS

This study identified rs4888378 in the BCAR1-CFDP1-TMEM170A locus as a novel genetic determinant of cIMT and coronary artery disease risk in individuals of European descent.

Detecting rare variant effects using extreme phenotype sampling in sequencing association studies

In the increasing number of sequencing studies aimed at identifying rare variants associated with complex traits, the power of the test can be improved by guided sampling procedures. We confirm both analytically and numerically that sampling individuals with extreme phenotypes can enrich the presence of causal rare variants and can therefore lead to an increase in power compared to random sampling. Although application of traditional rare variant association tests to these extreme phenotype samples requires dichotomizing the continuous phenotypes before analysis, the dichotomization procedure can decrease the power by reducing the information in the phenotypes. To avoid this, we propose a novel statistical method based on the optimal Sequence Kernel Association Test that allows us to test for rare variant effects using continuous phenotypes in the analysis of extreme phenotype samples. The increase in power of this method is demonstrated through simulation of a wide range of scenarios as well as in the triglyceride data of the Dallas Heart Study.

A unified method for detecting secondary trait associations with rare variants: application to sequence data

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

Next-generation sequencing has greatly expanded our ability to identify missing heritability due to rare variants. In order to increase the power to detect associations, one desirable study design is to combine samples from multiple cohorts for mapping commonly measured traits. However, many current studies sequence selected samples (e.g. samples with extreme QT), which can bias the analysis of secondary traits, unless the sampling ascertainment mechanisms are properly adjusted. We developed a unified method for detecting secondary trait associations with rare variants (STAR) in selected and random samples, which can flexibly incorporate all rare variant association tests and allow joint analysis of multiple cohorts ascertained under different study designs. We demonstrate via simulations that STAR greatly boosts the power for detecting secondary trait associations. As an application of STAR, a dataset from the SardiNIA project was analyzed, where DNA samples from well-phenotyped individuals with extreme low-density lipoprotein levels were sequenced. LDLR was identified to be significantly associated with systolic blood pressure, which is supported by a previous pharmacogenetics study. In conclusion, STAR is an important tool for sequence-based association studies.

Fine-mapping of the 6q25 locus identifies a novel SNP associated with breast cancer risk in African-American women

The rs2046210 single nucleotide polymorphism (SNP) in the 6q25.1 region was identified in a breast cancer genome-wide association study of Chinese women. The SNP has been replicated in European ancestry populations, but replication efforts have failed in African ancestry populations. We evaluated a total of 13 tagging SNPs in the linkage disequilibrium block around rs2046210 in a case-control study of breast cancer nested within the Black Women's Health Study, which included 1191 cases and 1941 controls. Replication of initial significant findings was carried out in 665 cases and 821 controls of African ancestry from the Women's Circle of Health Study (WCHS). No significant association was found for rs2046210 in univariate analysis. A new SNP, rs2046211, was significantly associated with reduced risk of breast cancer and was replicated in data from WCHS. In joint analyses that included both SNPs, the rs2046210-A allele was associated with increased risk of breast cancer [odds ratio (OR) = 1.14; 95% confidence interval (CI) = 1.02-1.28], and the rs2046211-G allele was associated with reduced risk (OR = 0.80; 95% CI = 0.67-0.95). Haplotype analysis confirmed these results and showed that the rs2046210-A allele is present in high-risk (rs2046211-C/rs2046210-A) and low-risk (rs2046211-G/rs2046210-A) haplotypes. Our results confirm the importance of 6q25.1 as a breast cancer susceptibility region. We replicated the rs2046210 association, after accounting for the haplotype background that included rs2046211 in African-American women, and we report the presence of a novel signal that is tagged by rs2046211.

A functional polymorphism on chromosome 15q25 associated with survival of early stage non-small-cell lung cancer

INTRODUCTION

The 15q25 region has been associated with lung-cancer risk and might also be associated with the prognosis of lung cancer. This study was conducted to determine the impact of a functional polymorphism in the CHRNA3 gene on chromosome 15q25 in the survival of patients with early-stage non-small-cell lung cancer (NSCLC).

METHODS

Five hundred and eighty-three consecutive patients with surgically resected NSCLC were enrolled. The rs6495309C > T polymorphism in the promoter of the CHRNA3 gene was investigated. The association between genotype and overall survival (OS) and disease-free survival (DFS) was analyzed.

RESULTS

Patients with the rs6495309 CT or TT genotype had a significantly better OS and DFS than the rs6495309 CC genotype (adjusted hazard ratio for OS = 0.56, 95% confidence interval = 0.41-0.75, p = 0.0001; and adjusted hazard ratio for DFS = 0.61, 95% confidence interval = 0.48-0.79, p = 0.0001). An association between the rs6495309C > T polymorphism and survival outcome was demonstrated in smokers and never-smokers, and in squamous-cell carcinomas and adenocarcinomas.

CONCLUSION

The CHRNA3 rs6495309C > T polymorphism may affect survival in patients with early-stage NSCLC. Analysis of the rs6495309C > T polymorphism can help identify patients at high risk of a poor disease outcome.

Exome sequencing and complex disease: practical aspects of rare variant association studies

Genetic association and linkage studies can provide insights into complex disease biology, guiding the development of new diagnostic and therapeutic strategies. Over the past decade, genetic association studies have largely focused on common, easy to measure genetic variants shared between many individuals. These common variants typically have subtle functional consequence and translating the resulting association signals into biological insights can be challenging. In the last few years, exome sequencing has emerged as a cost-effective strategy for extending these studies to include rare coding variants, which often have more marked functional consequences. Here, we provide practical guidance in the design and analysis of complex trait association studies focused on rare, coding variants.

Replication study and meta-analysis in European samples supports association of the 3p21.1 locus with bipolar disorder

BACKGROUND

Common genetic polymorphisms at chromosome 3p21.1, including rs2251219 in polybromo 1 (PBRM1), have been implicated in susceptibility to bipolar affective disorder (BP) through genome-wide association studies. Subsequent studies have suggested that this is also a risk locus for other psychiatric phenotypes, including major depression and schizophrenia.

METHODS

To replicate the association, we studied 2562 cases with BP and 25,439 control subjects collected from seven cohorts with either genome-wide association or individual genotyping of rs2251219 and tagging single nucleotide polymorphisms across the PBRM1 gene. Results from the different case-control groups were combined with the inverse variance weighting method.

RESULTS

In our dataset, rs2251219 was associated with BP (odds ratio [OR] = .89, p = .003), and meta-analysis of previously published data with our nonoverlapping new data confirmed genome-wide significant association (OR = .875, p = 2.68 × 10(-9)). Genotypic data from the SGENE-plus consortium were used to examine the association of the same variant with schizophrenia in an overall sample of 8794 cases and 25,457 control subjects, but this was not statistically significant (OR = .97, p = .21).

CONCLUSIONS

There is strong evidence of association of rs2251219 with BP. However, our data do not support association of this marker with schizophrenia. Because the region of association has high linkage disequilibrium, forming a large haplotype block across many genes, it is not clear which gene is causally implicated in the disorder.

Interpreting genetic effects through models of cardiac electromechanics

Multiscale models of cardiac electromechanics are being increasingly focused on understanding how genetic variation and environment underpin multiple disease states. In this paper we review the current state of the art in both the development of specific models and the physiological insights they have produced. This growing research body includes the development of models for capturing the effects of changes in function in both single and multiple proteins in both specific expression systems and in vivo contexts. Finally, the potential for using this approach for ultimately predicting phenotypes from genetic sequence information is discussed.

Ankyrin 3: genetic association with bipolar disorder and relevance to disease pathophysiology

Bipolar disorder (BD) is a multi-factorial disorder caused by genetic and environmental influences. It has a large genetic component, with heritability estimated between 59-93%. Recent genome-wide association studies (GWAS) using large BD patient populations have identified a number of genes with strong statistical evidence for association with susceptibility for BD. Among the most significant and replicated genes is ankyrin 3 (ANK3), a large gene that encodes multiple isoforms of the ankyrin G protein. This article reviews the current evidence for genetic association of ANK3 with BD, followed by a comprehensive overview of the known biology of the ankyrin G protein, focusing on its neural functions and their potential relevance to BD. Ankyrin G is a scaffold protein that is known to have many essential functions in the brain, although the mechanism by which it contributes to BD is unknown. These functions include organizational roles for subcellular domains in neurons including the axon initial segment and nodes of Ranvier, through which ankyrin G orchestrates the localization of key ion channels and GABAergic presynaptic terminals, as well as creating a diffusion barrier that limits transport into the axon and helps define axo-dendritic polarity. Ankyrin G is postulated to have similar structural and organizational roles at synaptic terminals. Finally, ankyrin G is implicated in both neurogenesis and neuroprotection. ANK3 and other BD risk genes participate in some of the same biological pathways and neural processes that highlight several mechanisms by which they may contribute to BD pathophysiology. Biological investigation in cellular and animal model systems will be critical for elucidating the mechanism through which ANK3 confers risk of BD. This knowledge is expected to lead to a better understanding of the brain abnormalities contributing to BD symptoms, and to potentially identify new targets for treatment and intervention approaches.

Interrogating the major histocompatibility complex with high-throughput genomics

The major histocompatibility complex (MHC) region on the short arm of chromosome 6 harbors the largest number of replicated associations across the human genome for a wide range of diseases, but the functional basis for these associations is still poorly understood. One fundamental challenge in fine-mapping associations to functional alleles is the enormous sequence diversity and broad linkage disequilibrium of the MHC, both of which hamper the cost-effective interrogation in large patient samples and the identification of causal variants. In this review, we argue that there is now a valuable opportunity to leverage existing genome-wide association study (GWAS) datasets for in-depth investigation to identify independent effects in the MHC. Application of imputation to GWAS data facilitates comprehensive interrogation of the classical human leukocyte antigen (HLA) loci. These datasets are, in many cases, sufficiently large to give investigators the ability to disentangle effects at different loci. We also explain how querying variation at individual amino acid positions for association can be powerful and expand traditional analyses that focus only on the classical HLA types.

Fine-mapping of IL16 gene and prostate cancer risk in African Americans

BACKGROUND

Prostate cancer is the most common type of cancer among men in the United States, and its incidence and mortality rates are disproportionate among ethnic groups. Although genome-wide association studies of European descents have identified candidate loci associated with prostate cancer risk, including a variant in IL16, replication studies in African Americans (AA) have been inconsistent. Here we explore single-nucleotide polymorphism (SNP) variation in IL16 in AAs and test for association with prostate cancer.

METHODS

Association tests were conducted for 2,257 genotyped and imputed SNPs spanning IL16 in 605 AA prostate cancer cases and controls from Washington, D.C. Eleven of them were also genotyped in a replication population of 1,093 AAs from Chicago. We tested for allelic association adjusting for age, global and local West African ancestry.

RESULTS

Analyses of genotyped and imputed SNPs revealed that a cluster of IL16 SNPs were significantly associated with prostate cancer risk. The strongest association was found at rs7175701 (P = 9.8 × 10(-8)). In the Chicago population, another SNP (rs11556218) was associated with prostate cancer risk (P = 0.01). In the pooled analysis, we identified three independent loci within IL16 that were associated with prostate cancer risk. SNP expression quantitative trait loci analyses revealed that rs7175701 is predicted to influence the expression of IL16 and other cancer-related genes.

CONCLUSION

Our study provides evidence that IL16 polymorphisms play a role in prostate cancer susceptibility among AAs.

IMPACT

Our findings are significant given that there has been limited focus on the role of IL16 genetic polymorphisms on prostate cancer risk in AAs.

Confirmation of TNIP1 but not RHOB and PSORS1C1 as systemic sclerosis risk factors in a large independent replication study

INTRODUCTION

A recent genome-wide association study in European systemic sclerosis (SSc) patients identified three loci (PSORS1C1, TNIP1 and RHOB) as novel genetic risk factors for the disease. The aim of this study was to replicate the previously mentioned findings in a large multicentre independent SSc cohort of Caucasian ancestry.

METHODS

4389 SSc patients and 7611 healthy controls from different European countries and the USA were included in the study. Six single nucleotide polymorphisms (SNP): rs342070, rs13021401 (RHOB), rs2233287, rs4958881, rs3792783 (TNIP1) and rs3130573 (PSORS1C1) were analysed. Overall significance was calculated by pooled analysis of all the cohorts. Haplotype analyses and conditional logistic regression analyses were carried out to explore further the genetic structure of the tested loci.

RESULTS

Pooled analyses of all the analysed SNPs in TNIP1 revealed significant association with the whole disease (rs2233287 p(MH)=1.94×10(-4), OR 1.19; rs4958881 p(MH)=3.26×10(-5), OR 1.19; rs3792783 p(MH)=2.16×10(-4), OR 1.19). These associations were maintained in all the subgroups considered. PSORS1C1 comparison showed association with the complete set of patients and all the subsets except for the anti-centromere-positive patients. However, the association was dependent on different HLA class II alleles. The variants in the RHOB gene were not associated with SSc or any of its subsets.

CONCLUSIONS

These data confirmed the influence of TNIP1 on an increased susceptibility to SSc and reinforced this locus as a common autoimmunity risk factor.

Estimating the contribution of genetic variants to difference in incidence of disease between population groups

Genome-wide association studies have identified multiple genetic susceptibility variants to several complex human diseases. However, risk-genotype frequency at loci showing robust associations might differ substantially among different populations. In this paper, we present methods to assess the contribution of genetic variants to the difference in the incidence of disease between different population groups for different scenarios. We derive expressions for the contribution of a single genetic variant, multiple genetic variants, and the contribution of the joint effect of a genetic variant and an environmental factor to the difference in the incidence of disease. The contribution of genetic variants to the difference in incidence increases with increasing difference in risk-genotype frequency, but declines with increasing difference in incidence between the two populations. The contribution of genetic variants also increases with increasing relative risk and the contribution of joint effect of genetic and environmental factors increases with increasing relative risk of the gene-environmental interaction. The contribution of genetic variants to the difference in incidence between two populations can be expressed as a function of the population attributable risks of the genetic variants in the two populations. The contribution of a group of genetic variants to the disparity in incidence of disease could change considerably by adding one more genetic variant to the group. Any estimate of genetic contribution to the disparity in incidence of disease between two populations at this stage seems to be an elusive goal.

Perspectives on systems biology applications in diabetic kidney disease

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Large-scale replication and heterogeneity in Parkinson disease genetic loci

OBJECTIVE

Eleven genetic loci have reached genome-wide significance in a recent meta-analysis of genome-wide association studies in Parkinson disease (PD) based on populations of Caucasian descent. The extent to which these genetic effects are consistent across different populations is unknown.

METHODS

Investigators from the Genetic Epidemiology of Parkinson's Disease Consortium were invited to participate in the study. A total of 11 SNPs were genotyped in 8,750 cases and 8,955 controls. Fixed as well as random effects models were used to provide the summary risk estimates for these variants. We evaluated between-study heterogeneity and heterogeneity between populations of different ancestry.

RESULTS

In the overall analysis, single nucleotide polymorphisms (SNPs) in 9 loci showed significant associations with protective per-allele odds ratios of 0.78-0.87 (LAMP3, BST1, and MAPT) and susceptibility per-allele odds ratios of 1.14-1.43 (STK39, GAK, SNCA, LRRK2, SYT11, and HIP1R). For 5 of the 9 replicated SNPs there was nominally significant between-site heterogeneity in the effect sizes (I(2) estimates ranged from 39% to 48%). Subgroup analysis by ethnicity showed significantly stronger effects for the BST1 (rs11724635) in Asian vs Caucasian populations and similar effects for SNCA, LRRK2, LAMP3, HIP1R, and STK39 in Asian and Caucasian populations, while MAPT rs2942168 and SYT11 rs34372695 were monomorphic in the Asian population, highlighting the role of population-specific heterogeneity in PD.

CONCLUSION

Our study allows insight to understand the distribution of newly identified genetic factors contributing to PD and shows that large-scale evaluation in diverse populations is important to understand the role of population-specific heterogeneity.

Normal early pregnancy: a transient state of epigenetic change favoring hypomethylation

The objective of this study was to analyze genome-wide differential methylation patterns in maternal leukocyte DNA in early pregnant and non-pregnant states. This is an age and body mass index matched case-control study comparing the methylation patterns of 27,578 cytosine-guanine (CpG) sites in 14,495 genes in maternal leukocyte DNA in early pregnancy (n = 14), in the same women postpartum (n = 14), and in nulligravid women (n = 14) on a BeadChip platform. Transient widespread hypomethylation was found in early pregnancy as compared with the non-pregnant states. Methylation of nine genes was significantly different in early pregnancy compared with both postpartum and nulligravid states (< 10% False Discovery Rate). Early pregnancy may be characterized by widespread hypomethylation compared with non-pregnant states; there is no apparent permanent methylation imprint after a normal term gestation. Nine potential candidate genes were identified as differentially methylated in early pregnancy and may play a role in the maternal adaptation to pregnancy.

Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing.

Analysis of autosomal genes reveals gene-sex interactions and higher total genetic risk in men with systemic lupus erythematosus

OBJECTIVES

Systemic lupus erythematosus (SLE) is a sexually dimorphic autoimmune disease which is more common in women, but affected men often experience a more severe disease. The genetic basis of sexual dimorphism in SLE is not clearly defined. A study was undertaken to examine sex-specific genetic effects among SLE susceptibility loci.

METHODS

A total of 18 autosomal genetic susceptibility loci for SLE were genotyped in a large set of patients with SLE and controls of European descent, consisting of 5932 female and 1495 male samples. Sex-specific genetic association analyses were performed. The sex-gene interaction was further validated using parametric and non-parametric methods. Aggregate differences in sex-specific genetic risk were examined by calculating a cumulative genetic risk score for SLE in each individual and comparing the average genetic risk between male and female patients.

RESULTS

A significantly higher cumulative genetic risk for SLE was observed in men than in women. (P=4.52x10-8) A significant sex-gene interaction was seen primarily in the human leucocyte antigen (HLA) region but also in IRF5, whereby men with SLE possess a significantly higher frequency of risk alleles than women. The genetic effect observed in KIAA1542 is specific to women with SLE and does not seem to have a role in men.

CONCLUSIONS

The data indicate that men require a higher cumulative genetic load than women to develop SLE. These observations suggest that sex bias in autoimmunity could be influenced by autosomal genetic susceptibility loci.