Background

Genetic determinants of peripheral arterial disease (PAD) remain largely unknown. To identify genetic variants associated with the ankle-brachial index (ABI), a noninvasive measure of PAD, we conducted a meta-analysis of genome-wide association study data from 21 population-based cohorts.

Methods and Results

Continuous ABI and PAD (ABI≤0.9) phenotypes adjusted for age and sex were examined. Each study conducted genotyping and imputed data to the ~2.5 million SNPs in HapMap. Linear and logistic regression models were used to test each SNP for association with ABI and PAD using additive genetic models. Study-specific data were combined using fixed-effects inverse variance weighted meta-analyses. There were a total of 41,692 participants of European ancestry (~60% women, mean ABI 1.02 to 1.19), including 3,409 participants with PAD and with GWAS data available. In the discovery meta-analysis, rs10757269 on chromosome 9 near CDKN2B had the strongest association with ABI (β= −0.006, p=2.46x10−8). We sought replication of the 6 strongest SNP associations in 5 population-based studies and 3 clinical samples (n=16,717). The association for rs10757269 strengthened in the combined discovery and replication analysis (p=2.65x10−9). No other SNP associations for ABI or PAD achieved genome-wide significance. However, two previously reported candidate genes for PAD and one SNP associated with coronary artery disease (CAD) were associated with ABI : DAB21P (rs13290547, p=3.6x10−5); CYBA (rs3794624, p=6.3x10−5); and rs1122608 (LDLR, p=0.0026).

Conclusions

GWAS in more than 40,000 individuals identified one genome-wide significant association on chromosome 9p21 with ABI. Two candidate genes for PAD and 1 SNP for CAD are associated with ABI.

Glycosylation of immunoglobulin G (IgG) influences IgG effector function by modulating binding to Fc receptors. To identify genetic loci associated with IgG glycosylation, we quantitated N-linked IgG glycans using two approaches. After isolating IgG from human plasma, we performed 77 quantitative measurements of N-glycosylation using ultra-performance liquid chromatography (UPLC) in 2,247 individuals from four European discovery populations. In parallel, we measured IgG N-glycans using MALDI-TOF mass spectrometry (MS) in a replication cohort of 1,848 Europeans. Meta-analysis of genome-wide association study (GWAS) results identified 9 genome-wide significant loci (P<2.27×10−9) in the discovery analysis and two of the same loci (B4GALT1 and MGAT3) in the replication cohort. Four loci contained genes encoding glycosyltransferases (ST6GAL1, B4GALT1, FUT8, and MGAT3), while the remaining 5 contained genes that have not been previously implicated in protein glycosylation (IKZF1, IL6ST-ANKRD55, ABCF2-SMARCD3, SUV420H1, and SMARCB1-DERL3). However, most of them have been strongly associated with autoimmune and inflammatory conditions (e.g., systemic lupus erythematosus, rheumatoid arthritis, ulcerative colitis, Crohn's disease, diabetes type 1, multiple sclerosis, Graves' disease, celiac disease, nodular sclerosis) and/or haematological cancers (acute lymphoblastic leukaemia, Hodgkin lymphoma, and multiple myeloma). Follow-up functional experiments in haplodeficient Ikzf1 knock-out mice showed the same general pattern of changes in IgG glycosylation as identified in the meta-analysis. As IKZF1 was associated with multiple IgG N-glycan traits, we explored biomarker potential of affected N-glycans in 101 cases with SLE and 183 matched controls and demonstrated substantial discriminative power in a ROC-curve analysis (area under the curve = 0.842). Our study shows that it is possible to identify new loci that control glycosylation of a single plasma protein using GWAS. The results may also provide an explanation for the reported pleiotropy and antagonistic effects of loci involved in autoimmune diseases and haematological cancer.

Author Summary

After analysing glycans attached to human immunoglobulin G in 4,095 individuals, we performed the first genome-wide association study (GWAS) of the glycome of an individual protein. Nine genetic loci were found to associate with glycans with genome-wide significance. Of these, four were enzymes that directly participate in IgG glycosylation, thus the observed associations were biologically founded. The remaining five genetic loci were not previously implicated in protein glycosylation, but the most of them have been reported to be relevant for autoimmune and inflammatory conditions and/or haematological cancers. A particularly interesting gene, IKZF1 was found to be associated with multiple IgG N-glycans. This gene has been implicated in numerous diseases, including systemic lupus erythematosus (SLE). We analysed N-glycans in 101 cases with SLE and 183 matched controls and demonstrated their substantial biomarker potential. Our study shows that it is possible to identify new loci that control glycosylation of a single plasma protein using GWAS. Our results may also provide an explanation for opposite effects of some genes in autoimmune diseases and haematological cancer.

Aims

Lipoprotein-associated phospholipase A2 (Lp-PLA2) generates proinflammatory and proatherogenic compounds in the arterial vascular wall and is a potential therapeutic target in coronary heart disease (CHD). We searched for genetic loci related to Lp-PLA2 mass or activity by a genome-wide association study as part of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

Methods and results

In meta-analyses of findings from five population-based studies, comprising 13 664 subjects, variants at two loci (PLA2G7, CETP) were associated with Lp-PLA2 mass. The strongest signal was at rs1805017 in PLA2G7 [P = 2.4 × 10−23, log Lp-PLA2 difference per allele (beta): 0.043]. Variants at six loci were associated with Lp-PLA2 activity (PLA2G7, APOC1, CELSR2, LDL, ZNF259, SCARB1), among which the strongest signals were at rs4420638, near the APOE–APOC1–APOC4–APOC2 cluster [P = 4.9 × 10−30; log Lp-PLA2 difference per allele (beta): −0.054]. There were no significant gene–environment interactions between these eight polymorphisms associated with Lp-PLA2 mass or activity and age, sex, body mass index, or smoking status. Four of the polymorphisms (in APOC1, CELSR2, SCARB1, ZNF259), but not PLA2G7, were significantly associated with CHD in a second study.

Conclusion

Levels of Lp-PLA2 mass and activity were associated with PLA2G7, the gene coding for this protein. Lipoprotein-associated phospholipase A2 activity was also strongly associated with genetic variants related to low-density lipoprotein cholesterol levels.

To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.

The Qatari population, located at the Arabian migration crossroads of African and Eurasia, is comprised of Bedouin, Persian and African genetic subgroups. By deep exome sequencing of only 7 Qataris, including individuals in each subgroup, we identified 2,750 nonsynonymous SNPs predicted to be deleterious, many of which are linked to human health, or are in genes linked to human health. Many of these SNPs were at significantly elevated deleterious allele frequency in Qataris compared to other populations worldwide. Despite the small sample size, SNP allele frequency was highly correlated with a larger Qatari sample. Together, the data demonstrate that exome sequencing of only a small number of individuals can reveal genetic variations with potential health consequences in understudied populations.

The heat shock protein (HSP) 70 family has been implicated in the pathology of Alzheimer’s disease (AD). In this study, we examined common genetic variations in the 80 genes encoding HSP70 and its co-chaperones. We conducted a study in a series of 462 patients and 5238 unaffected participants derived from the Rotterdam Study, a population-based study including 7983 persons aged 55 years and older. We genotyped a total of 12,053 Single Nucleotide Polymorphisms (SNPs) using the HumanHap550K Genotyping BeadChip from Illumina. Replication was performed in two independent cohort studies, the Framingham Heart study (FHS; N=806) and Cardiovascular Health Study (CHS; N=2150). When adjusting for multiple testing, we found a small but consistent, though not significant effect of rs12118313 located 32kb from PFDN2, with an OR of 1.19 (p-value from meta-analysis =0.003). However this SNP was in the intron of another gene, suggesting it is unlikely this SNP reflects the effect of PFDN2. In a formal pathway analysis we found nominally significant evidence for an association of BAG, DNAJA and prefoldin with AD. These findings corroborate with those of a study of 2032 AD patients and 5328 controls, in which several members of the prefoldin family showed evidence for association to AD. Our study did not reveal evidence for a genetic variant if the HSP70 family with a major effect on AD. However, our findings of the single SNP analysis and pathway analysis suggest that multiple genetic variants in prefoldin are associated with AD.

Background

The objective was to estimate the heritability for height and weight during fetal life and early childhood in two independent studies, one including parent and singleton offsprings and one of mono- and dizygotic twins.

Methods

This study was embedded in the Generation R Study (n = 3407, singletons) and the Netherlands Twin Register (n = 33694, twins). For the heritability estimates in Generation R, regression models as proposed by Galton were used. In the Twin Register we used genetic structural equation modelling. Parental height and weight were measured and fetal growth characteristics (femur length and estimated fetal weight) were measured by ultrasounds in 2nd and 3rd trimester (Generation R only). Height and weight were assessed at multiple time-points from birth to 36 months in both studies.

Results

Heritability estimates for length increased from 2nd to 3rd trimester from 13% to 28%. At birth, heritability estimates for length in singletons and twins were both 26% and 27%, respectively, and at 36 months, the estimates for height were 63% and 72%, respectively. Heritability estimates for fetal weight increased from 2nd to 3rd trimester from 17% to 27%. For birth weight, heritability estimates were 26% in singletons and 29% in twins. At 36 months, the estimate for twins was 71% and higher than for singletons (42%).

Conclusions

Heritability estimates for height and weight increase from second trimester to infancy. This increase in heritability is observed in singletons and twins. Longer follow-up studies are needed to examine how the heritability develops in later childhood and puberty.

Stature is a classical and highly heritable complex trait, with 80%–90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ2 = 83.89, df = 1; p = 5.2×10−20). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.

Author Summary

Studies investigating the extent to which genetics influences human characteristics such as height have concentrated mainly on common variants of genes, where having one or two copies of a given variant influences the trait or risk of disease. This study explores whether a different type of genetic variant might also be important. We investigate the role of recessive genetic variants, where two identical copies of a variant are required to have an effect. By measuring genome-wide homozygosity—the phenomenon of inheriting two identical copies at a given point of the genome—in 35,000 individuals from 21 European populations, and by comparing this to individual height, we found that the more homozygous the genome, the shorter the individual. The offspring of first cousins (who have increased homozygosity) were predicted to be up to 3 cm shorter on average than the offspring of unrelated parents. Height is influenced by the combined effect of many recessive variants dispersed across the genome. This may also be true for other human characteristics and diseases, opening up a new way to understand how genetic variation influences our health.

Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.

Author Summary

Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.

Intraocular pressure (IOP) is a highly heritable risk factor for primary open-angle glaucoma and is the only target for current glaucoma therapy. The genetic factors which determine IOP are largely unknown. We performed a genome-wide association study for IOP in 11,972 participants from 4 independent population-based studies in The Netherlands. We replicated our findings in 7,482 participants from 4 additional cohorts from the UK, Australia, Canada, and the Wellcome Trust Case-Control Consortium 2/Blue Mountains Eye Study. IOP was significantly associated with rs11656696, located in GAS7 at 17p13.1 (p = 1.4×10−8), and with rs7555523, located in TMCO1 at 1q24.1 (p = 1.6×10−8). In a meta-analysis of 4 case-control studies (total N = 1,432 glaucoma cases), both variants also showed evidence for association with glaucoma (p = 2.4×10−2 for rs11656696 and p = 9.1×10−4 for rs7555523). GAS7 and TMCO1 are highly expressed in the ciliary body and trabecular meshwork as well as in the lamina cribrosa, optic nerve, and retina. Both genes functionally interact with known glaucoma disease genes. These data suggest that we have identified two clinically relevant genes involved in IOP regulation.

Author Summary

Glaucoma is a major eye disease in the elderly and is the second leading cause of blindness worldwide. The numerous familial glaucoma cases, as well as evidence from epidemiological and twin studies, strongly support a genetic component in developing glaucoma. However, it has proven difficult to identify the specific genes involved. Intraocular pressure (IOP) is the major risk factor for glaucoma and the only target for the current glaucoma therapy. IOP has been shown to be highly heritable. We investigated the role of common genetic variants in IOP by performing a genome-wide association study. Discovery analyses in 11,972 participants and subsequent replication analyses in a further 7,482 participants yielded two common genetic variants that were associated with IOP. The first (rs11656696) is located in GAS7 at chromosome 17, the second (rs7555523) in TMCO1 at chromosome 1. Both variants were associated with glaucoma in a meta-analysis of 4 case-control studies. GAS7 and TMCO1 are expressed in the ocular tissues that are involved in glaucoma. Both genes functionally interact with the known glaucoma disease genes. These data suggest that we have identified two genes involved in IOP regulation and glaucomatous neuropathy.

Objective

Neuroinflammation contributes to the pathogenesis of sporadic Alzheimer’s disease (AD). Variations in genes relevant to inflammation may be candidate genes for AD risk. Whole-genome association studies have identified relevant new and known genes. Their combined effects do not explain 100% of the risk, genetic interactions may contribute. We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1β, IL-6, and IL-10 may interact to increase AD risk.

Methods

The Epistasis Project identifies interactions that affect the risk of AD. Genotyping of single nucleotide polymorphisms (SNPs) in PPARA, IL1A, IL1B, IL6 and IL10 was performed. Possible associations were analyzed by fitting logistic regression models with AD as outcome, controlling for centre, age, sex and presence of apolipoprotein ε4 allele (APOEε4). Adjusted synergy factors were derived from interaction terms (p<0.05 two-sided).

Results

We observed four significant interactions between different SNPs in PPARA and in interleukins IL1A, IL1B, IL10 that may affect AD risk. There were no significant interactions between PPARA and IL6.

Conclusions

In addition to an association of the PPARA L162V polymorphism with the AD risk, we observed four significant interactions between SNPs in PPARA and SNPs in IL1A, IL1B and IL10 affecting AD risk. We prove that gene-gene interactions explain part of the heritability of AD and are to be considered when assessing the genetic risk. Necessary replications will require between 1450 and 2950 of both cases and controls, depending on the prevalence of the SNP, to have 80% power to detect the observed synergy factors.

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10−204) and 10 loci for sphingolipids (smallest P-value = 3.10×10−57). After a correction for multiple comparisons (P-value<2.2×10−9), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

Author Summary

Phospho- and sphingolipids are integral to membrane formation and are involved in crucial cellular functions such as signalling, membrane fluidity, membrane protein trafficking, neurotransmission, and receptor trafficking. In addition to severe monogenic diseases resulting from defective phospho- and sphingolipid function and metabolism, the evidence suggests that variations in these lipid levels at the population level are involved in the determination of cardiovascular and neurologic traits and subsequent disease. We took advantage of modern laboratory methods, including microarray-based genotyping and electrospray ionization tandem mass spectrometry, to hunt for genetic variation influencing the levels of more than 350 phospho- and sphingolipid phenotypes. We identified nine novel loci, in addition to confirming a number of previously described loci. Several other genetic regions provided substantial evidence of their involvement in these traits. All of these loci are strong candidates for further research in the field of lipid biology and are likely to yield considerable insights into the complex metabolic pathways underlying circulating phospho- and sphingolipid levels. Understanding these mechanisms might help to illuminate factors leading to the development of common cardiovascular and neurological diseases and might provide molecular targets for the development of new therapies.

The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.

Author Summary

There is now evidence that rare variants can contribute to the etiology of complex disease. Next generation sequencing technologies have enabled their detection in large cohorts, and new statistical methods have been proposed to ascertain their association with complex diseases and traits in order to improve power over single-marker analysis. Each of these new methods assumes a particular nature of the relationship between rare variants and complex disease, yet these hypotheses have been largely unverified. Therefore we sought to compare the power of commonly used and novel statistical methods for rare variants using Sanger sequencing data from 1,998 individuals sequenced at 7 genes by simulating several phenotypes under models spanning a spectrum of the common hypotheses concerning such associations. While all methods perform reasonably well under their own model-specific hypotheses, no single method gives consistently acceptable power when these hypotheses are violated. Unlike GWAS, wherein all variants can often be tested using the same method across the entire genome, the analysis and interpretation of sequencing studies will therefore be considerably more challenging.

Background

Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties.

We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants.

Results

In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests.

Conclusions

Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.

Genome-wide association studies have identified 32 loci associated with body mass index (BMI), a measure that does not allow distinguishing lean from fat mass. To identify adiposity loci, we meta-analyzed associations between ~2.5 million SNPs and body fat percentage from 36,626 individuals, and followed up the 14 most significant (P<10−6) independent loci in 39,576 individuals. We confirmed the previously established adiposity locus in FTO (P=3×10−26), and identified two new loci associated with body fat percentage, one near IRS1 (P=4×10−11) and one near SPRY2 (P=3×10−8). Both loci harbour genes with a potential link to adipocyte physiology, of which the locus near IRS1 shows an intriguing association pattern. The body-fat-decreasing allele associates with decreased IRS1 expression and with an impaired metabolic profile, including decreased subcutaneous-to-visceral fat ratio, increased insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease, and decreased adiponectin levels. Our findings provide new insights into adiposity and insulin resistance.

Multiple loss-of-function (LOF) alleles at the same gene may influence a phenotype not only in the homozygote state when alleles are considered individually, but also in the compound heterozygote (CH) state. Such LOF alleles typically have low frequencies and moderate to large effects. Detecting such variants is of interest to the genetics community, and relevant statistical methods for detecting and quantifying their effects are sorely needed. We present a collapsed double heterozygosity (CDH) test to detect the presence of multiple LOF alleles at a gene. When causal SNPs are available, which may be the case in next generation genome sequencing studies, this CDH test has overwhelmingly higher power than single SNP analysis. When causal SNPs are not directly available such as in current GWA settings, we show the CDH test has higher power than standard single SNP analysis if tagging SNPs are in linkage disequilibrium with the underlying causal SNPs to at least a moderate degree (r2>0.1). The test is implemented for genome-wide analysis in the publically available software package GenABEL which is based on a sliding window approach. We provide the proof of principle by conducting a genome-wide CDH analysis of red hair color, a trait known to be influenced by multiple loss-of-function alleles, in a total of 7,732 Dutch individuals with hair color ascertained. The association signals at the MC1R gene locus from CDH were uniformly more significant than traditional GWA analyses (the most significant P for CDH = 3.11×10−142 vs. P for rs258322 = 1.33×10−66). The CDH test will contribute towards finding rare LOF variants in GWAS and sequencing studies.

The genetic structure of human populations is important in population genetics, forensics and medicine. Using genome-wide scans and individuals with all four grandparents born in the same settlement, we here demonstrate remarkable geographical structure across 8–30 km in three different parts of rural Europe. After excluding close kin and inbreeding, village of origin could still be predicted correctly on the basis of genetic data for 89–100% of individuals.

Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations.

Migraine is a common neurological disorder with a genetically complex background. This paper describes a meta-analysis of genome-wide association (GWA) studies on migraine, performed by the Dutch–Icelandic migraine genetics (DICE) consortium, which brings together six population-based European migraine cohorts with a total sample size of 10 980 individuals (2446 cases and 8534 controls). A total of 32 SNPs showed marginal evidence for association at a P-value<10−5. The best result was obtained for SNP rs9908234, which had a P-value of 8.00 × 10−8. This top SNP is located in the nerve growth factor receptor (NGFR) gene. However, this SNP did not replicate in three cohorts from the Netherlands and Australia. Of the other 31 SNPs, 18 SNPs were tested in two replication cohorts, but none replicated. In addition, we explored previously identified candidate genes in the meta-analysis data set. This revealed a modest gene-based significant association between migraine and the metadherin (MTDH) gene, previously identified in the first clinic-based GWA study (GWAS) for migraine (Bonferroni-corrected gene-based P-value=0.026). This finding is consistent with the involvement of the glutamate pathway in migraine. Additional research is necessary to further confirm the involvement of glutamate.

Background

Although genetic factors contribute to the onset of heart failure (HF), no large-scale genome-wide investigation of HF risk has been published to date. We investigated the association of 2,478,304 single nucleotide polymorphisms (SNPs) with incident HF by meta-analyzing data from 4 community-based prospective cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study.

Methods and Results

Eligible participants for these analyses were of European or African ancestry and free of clinical HF at baseline. Each study independently conducted genome-wide scans and imputed data to the ~2.5 million SNPs in HapMap. Within each study, Cox proportional hazards regression models provided age- and sex-adjusted estimates of the association between each variant and time to incident HF. Fixed-effect meta-analyses combined results for each SNP from the 4 cohorts to produce an overall association estimate and p-value. A genome-wide significance p-value threshold was set a priori at 5.0×10−7. During a mean follow-up of 11.5 years, 2,526 incident HF events (12%) occurred in 20,926 European-ancestry participants. The meta-analysis identified a genome-wide significant locus at chromosomal position 15q22 (1.4×10−8), which was 58.8 kb from USP3. Among 2,895 African-ancestry participants, 466 incident HF events (16%) occurred during a mean follow-up of 13.7 years. One genome-wide significant locus was identified at 12q14 (6.7×10−8), which was 6.3 kb from LRIG3.

Conclusions

We identified 2 loci that were associated with incident HF and exceeded genome-wide significance. The findings merit replication in other community-based settings of incident HF.

Background

Prognosis and survival are significant concerns for individuals with heart failure (HF). In order to better understand the pathophysiology of HF prognosis, the association between 2,366,858 single nucleotide polymorphisms (SNPs) and all-cause mortality was evaluated among individuals with incident HF from four community-based prospective cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study.

Methods and Results

Participants were 2,526 individuals of European ancestry and 466 individuals of African ancestry who suffered an incident HF event during follow-up in the respective cohorts. Within each study, the association between genetic variants and time to mortality among individuals with HF was assessed by Cox proportional hazards models that included adjustment for sex and age at the time of the HF event. Prospective fixed-effect meta-analyses were conducted for the four study populations of European ancestry (N=1,645 deaths) and for the two populations of African ancestry (N=281 deaths). Genome-wide significance was set at P=5.0×10-7. Meta-analytic findings among individuals of European ancestry revealed one genome-wide significant locus on chromosome 3p22 in an intron of CKLF-like MARVEL transmembrane domain containing 7 (CMTM7, p = 3.2×10-7). Eight additional loci in individuals of European ancestry and four loci in individuals of African ancestry were identified by high-signal SNPs (p < 1.0×10-5), but did not meet genome-wide significance.

Conclusions

This study identified a novel locus associated with all-cause mortality among individuals of European ancestry with HF. This finding warrants additional investigation, including replication, in other studies of HF.

The global health agenda beyond 2015 will inevitably need to broaden its focus from mortality reduction to the social determinants of deaths, growing inequities among children and mothers, and ensuring the sustainability of the progress made against the infectious diseases. New research tools, including technologies that enable high-throughput genetic and ‘-omics’ research, could be deployed for better understanding of the aetiology of maternal and child health problems. The research needed to address those challenges will require conceptually different studies than those used in the past. It should be guided by stringent ethical frameworks related to the emerging collections of biological specimens and other health related information. We will aim to establish an international birth cohort which should assist low- and middle-income countries to use emerging genomic research technologies to address the main problems in maternal and child health, which are still major contributors to the burden of disease globally.

Context

Genome wide association studies (GWAS) have recently identified CLU, PICALM and CR1 as novel genes for late-onset Alzheimer’s disease (AD).

Objective

In a three-stage analysis of new and previously published GWAS on over 35000 persons (8371 AD cases), we sought to identify and strengthen additional loci associated with AD and confirm these in an independent sample. We also examined the contribution of recently identified genes to AD risk prediction.

Design, Setting, and Participants

We identified strong genetic associations (p<10−3) in a Stage 1 sample of 3006 AD cases and 14642 controls by combining new data from the population-based Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (1367 AD cases (973 incident)) with previously reported results from the Translational Genomics Research Institute (TGEN) and Mayo AD GWAS. We identified 2708 single nucleotide polymorphisms (SNPs) with p-values<10−3, and in Stage 2 pooled results for these SNPs with the European AD Initiative (2032 cases, 5328 controls) to identify ten loci with p-values<10−5. In Stage 3, we combined data for these ten loci with data from the Genetic and Environmental Risk in AD consortium (3333 cases, 6995 controls) to identify four SNPs with a p-value<1.7×10−8. These four SNPs were replicated in an independent Spanish sample (1140 AD cases and 1209 controls).

Main outcome measure

Alzheimer’s Disease.

Results

We showed genome-wide significance for two new loci: rs744373 near BIN1 (OR:1.13; 95%CI:1.06–1.21 per copy of the minor allele; p=1.6×10−11) and rs597668 near EXOC3L2/BLOC1S3/MARK4 (OR:1.18; 95%CI1.07–1.29; p=6.5×10−9). Associations of CLU, PICALM, BIN1 and EXOC3L2 with AD were confirmed in the Spanish sample (p<0.05). However, CLU and PICALM did not improve incident AD prediction beyond age, sex, and APOE (improvement in area under receiver-operating-characteristic curve <0.003).

Conclusions

Two novel genetic loci for AD are reported that for the first time reach genome-wide statistical significance; these findings were replicated in an independent population. Two recently reported associations were also confirmed, but these loci did not improve AD risk prediction, although they implicate biological pathways that may be useful targets for potential interventions.

Background.

Genome-wide association studies (GWAS) may yield insights into longevity.

Methods.

We performed a meta-analysis of GWAS in Caucasians from four prospective cohort studies: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Longevity was defined as survival to age 90 years or older (n = 1,836); the comparison group comprised cohort members who died between the ages of 55 and 80 years (n = 1,955). In a second discovery stage, additional genotyping was conducted in the Leiden Longevity Study cohort and the Danish 1905 cohort.

Results.

There were 273 single-nucleotide polymorphism (SNP) associations with p < .0001, but none reached the prespecified significance level of 5 × 10−8. Of the most significant SNPs, 24 were independent signals, and 16 of these SNPs were successfully genotyped in the second discovery stage, with one association for rs9664222, reaching 6.77 × 10−7 for the combined meta-analysis of CHARGE and the stage 2 cohorts. The SNP lies in a region near MINPP1 (chromosome 10), a well-conserved gene involved in regulation of cellular proliferation. The minor allele was associated with lower odds of survival past age 90 (odds ratio = 0.82). Associations of interest in a homologue of the longevity assurance gene (LASS3) and PAPPA2 were not strengthened in the second stage.

Conclusion.

Survival studies of larger size or more extreme or specific phenotypes may support or refine these initial findings.

Smoking is a risk factor for most of the diseases leading in mortality1. We conducted genome-wide association (GWA) meta-analyses of smoking data within the ENGAGE consortium to search for common alleles associating with the number of cigarettes smoked per day (CPD) in smokers (N=31,266) and smoking initiation (N=46,481). We tested selected SNPs in a second stage (N=45,691 smokers), and assessed some in a third sample (N=9,040). Variants in three genomic regions associated with CPD (P< 5·10−8), including previously identified SNPs at 15q25 represented by rs1051730-A (0.80 CPD,P=2.4·10−69), and SNPs at 19q13 and 8p11, represented by rs4105144-C (0.39 CPD, P=2.2·10−12) and rs6474412-T (0.29 CPD,P= 1.4·10−8), respectively. Among the genes at the two novel loci, are genes encoding nicotine-metabolizing enzymes (CYP2A6 and CYP2B6), and nicotinic acetylcholine receptor subunits (CHRNB3 and CHRNA6) highlighted in previous studies of nicotine dependence2-3. Nominal associations with lung cancer were observed at both 8p11 (rs6474412-T,OR=1.09,P=0.04) and 19q13 (rs4105144-C,OR=1.12,P=0.0006).