Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.

Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.

C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation. Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear. Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms. As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes. Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes. Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system. Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits.

Chronic inflammation is associated with chronic diseases, morbidity and mortality while lower base inflammation levels are thought to be predictive of healthy aging. Thus, to pursue a long and healthy lifespan, it is essential to understand the inflammatory regulatory mechanisms. To that end, we studied the functional role of C-reactive protein (CRP)–an inflammatory biomarker that is used to measure cardiovascular risk in clinical practice. There is evidence for a strong genetic component of elevated CRP levels but it is still unclear if it has a direct impact on the processes that lead to inflammatory diseases. In order to elucidate the function of CRP in the blood, we used statistical methods for causal inference to infer causal relationships between changes in CRP and gene expression levels. Our statistical analysis and cell culture experiments suggest that CRP drives the expression of complement regulatory protein CD59. Thus, CRP can have a functional role in protecting human blood cells from the adverse effects of the immune defence system.

An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype

BACKGROUND

The genetics of depression has been explored in genome-wide association studies that focused on either major depressive disorder or depressive symptoms with mostly negative findings. A broad depression phenotype including both phenotypes has not been tested previously using a genome-wide association approach. We aimed to identify genetic polymorphisms significantly associated with a broad phenotype from depressive symptoms to major depressive disorder.

METHODS

We analyzed two prior studies of 70,017 participants of European ancestry from general and clinical populations in the discovery stage. We performed a replication meta-analysis of 28,328 participants. Single nucleotide polymorphism (SNP)-based heritability and genetic correlations were calculated using linkage disequilibrium score regression. Discovery and replication analyses were performed using a p-value-based meta-analysis. Lifetime major depressive disorder and depressive symptom scores were used as the outcome measures.

RESULTS

The SNP-based heritability of major depressive disorder was 0.21 (SE = 0.02), the SNP-based heritability of depressive symptoms was 0.04 (SE = 0.01), and their genetic correlation was 1.001 (SE = 0.2). We found one genome-wide significant locus related to the broad depression phenotype (rs9825823, chromosome 3: 61,082,153, p = 8.2 × 10^-9) located in an intron of the FHIT gene. We replicated this SNP in independent samples (p = .02) and the overall meta-analysis of the discovery and replication cohorts (1.0 × 10^-9).

CONCLUSIONS

This large study identified a new locus for depression. Our results support a continuum between depressive symptoms and major depressive disorder. A phenotypically more inclusive approach may help to achieve the large sample sizes needed to detect susceptibility loci for depression.

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms.

Bayesian association scan reveals loci associated with human lifespan and linked biomarkers

The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes (RBM6, SULT1A1 and CHRNA5) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan.

Risk prediction of developing venous thrombosis in combined oral contraceptive users

Background

Venous thromboembolism (VTE) is a complex multifactorial disease influenced by genetic and environmental risk factors. An example for the latter is the regular use of combined oral contraceptives (CC), which increases the risk to develop VTE by 3 to 7 fold, depending on estrogen dosage and the type of progestin present in the pill. One out of 1'000 women using CC develops thrombosis, often with life-long consequences; a risk assessment is therefore necessary prior to such treatment. Currently known clinical risk factors associated with VTE development in general are routinely checked by medical doctors, however they are far from being sufficient for risk prediction, even when combined with genetic tests for Factor V Leiden and Factor II G20210A variants. Thus, clinical and notably genetic risk factors specific to the development of thrombosis associated with the use of CC in particular should be identified.

Methods and findings

Step-wise (logistic) model selection was applied to a population of 1622 women using CC, half of whom (794) had developed a thromboembolic event while using contraceptives. 46 polymorphisms and clinical parameters were tested in the model selection and a specific combination of 4 clinical risk factors and 9 polymorphisms were identified. Among the 9 polymorphisms, there are two novel genetic polymorphisms (rs1799853 and rs4379368) that had not been previously associated with the development of thromboembolic event. This new prediction model outperforms (AUC 0.71, 95% CI 0.69–0.74) previously published models for general thromboembolic events in a cross-validation setting. Further validation in independent populations should be envisaged.

Conclusion

We identified two new genetic variants associated to VTE development, as well as a robust prediction model to assess the risk of thrombosis for women using combined oral contraceptives. This model outperforms current medical practice as well as previously published models and is the first model specific to CC use.

Large meta-analysis of genome-wide association studies identifies five loci for lean body mass

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10⁻⁸) or suggestively genome wide (p < 2.3 × 10⁻⁶). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.

Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.

Quantifying the extent to which index event biases influence large genetic association studies

As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P  <  0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P  =  5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P <  5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of  >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Anti-Apolipoprotein A-1 IgG Predict All-Cause Mortality and Are Associated with Fc Receptor-Like 3 Polymorphisms

Background

Autoantibodies against apolipoprotein A-1 (anti-apoA-1 IgG) have emerged as an independent biomarker for cardiovascular disease and mortality. However, their association with all-cause mortality in the community, as well as their genetic determinants, have not been studied.

Objective

To determine whether anti-apoA-1 IgG: (a) predict all-cause mortality in the general population and (b) are associated with single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS).

Methods

Clinical, biological, and genetic data were obtained from the population-based, prospective CoLaus study, including 5,220 participants (mean age 52.6 years, 47.3% men) followed over a median duration of 5.6 years. The primary study outcome was all-cause mortality.

Results

After multivariate adjustment, anti-apoA-1 IgG positivity independently predicted all-cause mortality: hazard ratio (HR) = 1.54, 95% confidence interval (95% CI): 1.11–2.13, P = 0.01. A dose–effect relationship was also observed, each SD of logarithmically transformed anti-apoA-1 IgG being associated with a 15% increase in mortality risk: HR = 1.15, 95% CI: 1.02–1.28, P = 0.028. The GWAS yielded nine SNPs belonging to the Fc receptor-like 3 (FCRL3) gene, which were significantly associated with anti-apoA-1 IgG levels, with the lead SNP (rs6427397, P = 1.54 × 10⁻⁹) explaining 0.67% of anti-apoA-1 IgG level variation.

Conclusion

Anti-apoA-1 IgG levels (a) independently predict all-cause mortality in the general population and (b) are linked to FCRL3, a susceptibility gene for numerous autoimmune diseases. Our findings indicate that preclinical autoimmunity to anti-apoA-1 IgG may represent a novel mortality risk factor.

Gene-obesogenic environment interactions in the UK Biobank study

Background

Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI).

Methods

We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment.

Results

We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely.

Conclusions

Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

J, Chu AY, Bragg-Gresham JL, van Dongen J, Huffman JE, Ahluwalia TS, Cadby G, Eklund N, Eriksson J, Esko T, Feitosa MF, Goel A, Gorski M, Hayward C, Heard-Costa NL, Jackson AU, Jokinen E, Kanoni S, Kristiansson K, Kutalik Z, Lahti J, Luan J, Mägi R, Mahajan A, Mangino M, Medina-Gomez C, Monda KL, Nolte IM, Pérusse L, Prokopenko I, Qi L, Rose LM, Salvi E, Smith MT, Snieder H, Stančáková A, Ju Sung Y, Tachmazidou I, Teumer A, Thorleifsson G, van der Harst P, Walker RW, Wang SR, Wild SH, Willems SM, Wong A, Zhang W, Albrecht E, Couto Alves A, Bakker SJL, Barlassina C, Bartz TM, Beilby J, Bellis C, Bergman RN, Bergmann S, Blangero J, Blüher M, Boerwinkle E, Bonnycastle LL, Bornstein SR, Bruinenberg M, Campbell H, Chen YDI, Chiang CWK, Chines PS, Collins FS, Cucca F, Cupples LA, D'Avila F, de Geus EJ.C, Dedoussis G, Dimitriou M, Döring A, Eriksson JG, Farmaki AE, Farrall M, Ferreira T, Fischer K, Forouhi NG, Friedrich N, Gjesing AP, Glorioso N, Graff M, Grallert H, Grarup N, Gräßler J, Grewal J, Hamsten A, Harder MN, Hartman CA, Hassinen M, Hastie N, Hattersley AT, Havulinna AS, Heliövaara M, Hillege H, Hofman A, Holmen O, Homuth G, Hottenga JJ, Hui J, Husemoen LL, Hysi PG, Isaacs A, Ittermann T, Jalilzadeh S, James AL, Jørgensen T, Jousilahti P, Jula A, Marie Justesen J, Justice AE, Kähönen M, Karaleftheri M, Tee Khaw K, Keinanen-Kiukaanniemi SM, Kinnunen L, Knekt PB, Koistinen HA, Kolcic I, Kooner IK, Koskinen S, Kovacs P, Kyriakou T, Laitinen T, Langenberg C, Lewin AM, Lichtner P, Lindgren CM, Lindström J, Linneberg A, Lorbeer R, Lorentzon M, Luben R, Lyssenko V, Männistö S, Manunta P, Leach IM, McArdle WL, Mcknight B, Mohlke KL, Mihailov E, Milani L, Mills R, Montasser ME, Morris AP, Müller G, Musk AW, Narisu N, Ong KK, Oostra BA, Osmond C, Palotie A, Pankow JS, Paternoster L, Penninx BW, Pichler I, Pilia MG, Polašek O, Pramstaller PP, Raitakari OT, Rankinen T, Rao DC, Rayner NW, Ribel-Madsen R, Rice TK, Richards M, Ridker PM, Rivadeneira F, Ryan KA, Sanna S, Sarzynski MA, Scholtens S, Scott RA, Sebert S, Southam L, Sparsø TH, Steinthorsdottir V, Stirrups K, Stolk RP, Strauch K, Stringham HM, Swertz MA, Swift AJ, Tönjes A, Tsafantakis E, van der Most PJ, Van Vliet-Ostaptchouk JV, Vandenput L, Vartiainen E, Venturini C, Verweij N, Viikari JS, Vitart V, Vohl MC, Vonk JM, Waeber G, Widén E, Willemsen G, Wilsgaard T, Winkler TW, Wright AF, Yerges-Armstrong LM, Hua Zhao J, Carola Zillikens M, Boomsma DI, Bouchard C, Chambers JC, Chasman DI, Cusi D, Gansevoort RT, Gieger C, Hansen T, Hicks AA, Hu F, Hveem K, Jarvelin MR, Kajantie E, Kooner JS, Kuh D, Kuusisto J, Laakso M, Lakka TA, Lehtimäki T, Metspalu A, Njølstad I, Ohlsson C, Oldehinkel AJ, Palmer LJ, Pedersen O, Perola M, Peters A, Psaty BM, Puolijoki H, Rauramaa R, Rudan I, Salomaa V, Schwarz PEH, Shudiner AR, Smit JH, Sørensen TIA, Spector TD, Stefansson K, Stumvoll M, Tremblay A, Tuomilehto J, Uitterlinden AG, Uusitupa M, Völker U, Vollenweider P, Wareham NJ, Watkins H, Wilson JF, Zeggini E, Abecasis GR, Boehnke M, Borecki IB, Deloukas P, van Duijn CM, Fox C, Groop LC, Heid IM, Hunter DJ, Kaplan RC, McCarthy MI, North KE, O'Connell JR, Schlessinger D, Thorsteinsdottir U, Strachan DP, Frayling T, Hirschhorn JN, Müller-Nurasyid M, Loos RJF. A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations

Background

Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (n_A = 995, n_B = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors.

Results

w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m² per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation.

Conclusions

This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10-8). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (Rg = -0.22, P = 5.5 × 10-13), T2D (Rg = -0.27, P = 1.1 × 10-6) and coronary artery disease (Rg = -0.30, P = 6.5 × 10-9). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10-4). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated.

ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase

ORMDL proteins are believed to be negative regulators of serine palmitoyltransferase (SPT), which catalyzes the first and rate limiting step in sphingolipid (SL) de novo synthesis. Several single-nucleotide polymorphisms (SNPs) that are close to the ORMDL3 locus have been reported to increase ORMDL3 expression and to be associated with an elevated risk for early childhood asthma; however, the direct effect of ORMDL3 expression on SPT activity and its link to asthma remains elusive. In this study, we investigated whether ORMDL3 expression is associated with changes in SPT activity and total SL levels. Ormdl3-knockout (Ormdl3^-/-) and transgenic (Ormdl3^Tg/wt) mice were generated to study the effect of ORMDL3 on total SL levels in plasma and tissues. Cellular SPT activity was measured in mouse embryonic fibroblasts from Ormdl3^-/- mice, as well as in HEK293 cells in which ORMDL3 was overexpressed and silenced. Furthermore, we analyzed the association of the reported ORMDL3 asthma SNPs with plasma sphingoid bases in a population-based cohort of 971 individuals. Total C₁₈-long chain bases were not significantly altered in the plasma and tissues of Ormdl3^-/- mice, whereas C₁₈-sphinganine showed a small and significant increase in plasma, lung, and liver tissues. Mouse embryonic fibroblast cells from Ormdl3^-/- mice did not show an altered SPT activity compared with Ormdl3^+/- and Ormdl3^+/+ mice. Overexpression or knockdown of ORMDL3 in HEK293 cells did not alter SPT activity; however, parallel knockdown of all 3 ORMDL isoforms increased enzyme activity significantly. A significant association of the annotated ORMDL3 asthma SNPs with plasma long-chain sphingoid base levels could not be confirmed. ORMDL3 expression levels seem not to be directly associated with changes in SPT activity. ORMDL3 might influence de novo sphingolipid metabolism downstream of SPT.-Zhakupova, A., Debeuf, N., Krols, M., Toussaint, W., Vanhoutte, L., Alecu, I., Kutalik, Z., Vollenweider, P., Ernst, D., von Eckardstein, A., Lambrecht, B. N., Janssens, S., Hornemann, T. ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase.

Across-cohort QC analyses of GWAS summary statistics from complex traits

Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics F_st statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

New quality measure for SNP array based CNV detection

MOTIVATION

Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software.

RESULTS

Out-of-sample comparison showed that the correlation between the consensus CNV status and the QS is twice as high as it is for any previously proposed CNV filters. ROC curves displayed an AUC higher than 0.8 and simulations showed an increase up to 20% in statistical power when using QS in comparison to other filtering strategies. Superior performance was confirmed also for alternative consensus CNV definition and through improving known CNV-trait associations.

AVAILABILITY AND IMPLEMENTATION

http://goo.gl/T6yuFM CONTACT: zoltan.kutalik@unil.ch or aurelien@mace@unil.chSupplementary information: Supplementary data are available at Bioinformatics online.

Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

[This corrects the article DOI: 10.1371/journal.pgen.1005378.].

Narcolepsy-Associated HLA Class I Alleles Implicate Cell-Mediated Cytotoxicity

STUDY OBJECTIVES

Narcolepsy with cataplexy is tightly associated with the HLA class II allele DQB1*06:02. Evidence indicates a complex contribution of HLA class II genes to narcolepsy susceptibility with a recent independent association with HLA-DPB1. The cause of narcolepsy is supposed be an autoimmune attack against hypocretin-producing neurons. Despite the strong association with HLA class II, there is no evidence for CD4+ T-cell-mediated mechanism in narcolepsy. Since neurons express class I and not class II molecules, the final effector immune cells involved might include class I-restricted CD8+ T-cells.

METHODS

HLA class I (A, B, and C) and II (DQB1) genotypes were analyzed in 944 European narcolepsy with cataplexy patients and in 4,043 control subjects matched by country of origin. All patients and controls were DQB1*06:02 positive and class I associations were conditioned on DQB1 alleles.

RESULTS

HLA-A*11:01 (OR = 1.49 [1.18-1.87] P = 7.0*10(-4)), C*04:01 (OR = 1.34 [1.10-1.63] P = 3.23*10(-3)), and B*35:01 (OR = 1.46 [1.13-1.89] P = 3.64*10(-3)) were associated with susceptibility to narcolepsy. Analysis of polymorphic class I amino-acids revealed even stronger associations with key antigen-binding residues HLA-A-Tyr(9) (OR = 1.32 [1.15-1.52] P = 6.95*10(-5)) and HLA-C-Ser(11) (OR = 1.34 [1.15-1.57] P = 2.43*10(-4)).

CONCLUSIONS

Our findings provide a genetic basis for increased susceptibility to infectious factors or an immune cytotoxic mechanism in narcolepsy, potentially targeting hypocretin neurons.

Prevalence and determinants of periodic limb movements in the general population

OBJECTIVE

Periodic limb movements during sleep (PLMS) are sleep phenomena characterized by periodic episodes of repetitive stereotyped limb movements. The aim of this study was to describe the prevalence and determinants of PLMS in a middle to older aged general population.

METHODS

Data from 2,162 subjects (51.2% women, mean age = 58.4 ± 11.1 years) participating in a population-based study (HypnoLaus, Lausanne, Switzerland) were collected. Assessments included laboratory tests, sociodemographic data, personal and treatment history, and full polysomnography at home. PLMS index (PLMSI) was determined, and PLMSI > 15/h was considered as significant.

RESULTS

Prevalence of PLMSI > 15/h was 28.6% (31.3% in men, 26% in women). Compared to subjects with PLMSI ≤ 15/h, subjects with PLMSI > 15/h were older (p < 0.001), were predominantly males (p = 0.007), had a higher proportion of restless legs syndrome (RLS; p < 0.001), had a higher body mass index (p = 0.001), and had a lower mean glomerular filtration rate (p < 0.001). Subjects with PLMSI > 15/h also had a higher prevalence of diabetes, hypertension, and beta-blocker or hypnotic treatments. The prevalence of antidepressant use was higher, but not statistically significant (p = 0.07). Single nucleotide polymorphisms (SNPs) within BTBD9 (rs3923809), TOX3 (rs3104788), and MEIS1 (rs2300478) genes were significantly associated with PLSMI > 15/h. Conversely, mean hemoglobin and ferritin levels were similar in both groups. In the multivariate analysis, age, male gender, antidepressant intake, RLS, and rs3923809, rs3104788, and rs2300478 SNPs were independently associated with PLMSI > 15/h.

INTERPRETATION

PLMS are highly prevalent in our middle-aged European population. Age, male gender, RLS, antidepressant treatment, and specific BTBD9, TOX3, and MEIS1 SNP distribution are independent predictors of PLMSI > 15/h.

Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance

BACKGROUND

Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.

AIMS

We sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.

METHOD

To detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.

RESULTS

Integrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85 × 10(-5)). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54 × 10(-8)). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.

CONCLUSIONS

Our findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.

Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P<10(-6) in 19,979 additional individuals. We identify five loci robustly associated (P<5 × 10(-8)) with leptin levels in/near LEP, SLC32A1, GCKR, CCNL1 and FTO. Although the association of the FTO obesity locus with leptin levels is abolished by adjustment for BMI, associations of the four other loci are independent of adiposity. The GCKR locus was found associated with multiple metabolic traits in previous GWAS and the CCNL1 locus with birth weight. Knockdown experiments in mouse adipose tissue explants show convincing evidence for adipogenin, a regulator of adipocyte differentiation, as the novel causal gene in the SLC32A1 locus influencing leptin levels. Our findings provide novel insights into the regulation of leptin production by adipose tissue and open new avenues for examining the influence of variation in leptin levels on adiposity and metabolic health.