The probability of edge existence due to node degree: a baseline for network-based predictions

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

The probability of edge existence due to node degree: a baseline for network-based predictions

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Hetnet connectivity search provides rapid insights into how biomedical entities are related

BACKGROUND

Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes-including genes, diseases, drugs, pathways, and anatomical structures-with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia.

FINDINGS

We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs.

CONCLUSION

We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy.

The genetic regulation of protein expression in cerebrospinal fluid

Studies of the genetic regulation of cerebrospinal fluid (CSF) proteins may reveal pathways for treatment of neurological diseases. 398 proteins in CSF were measured in 1,591 participants from the BioFINDER study. Protein quantitative trait loci (pQTL) were identified as associations between genetic variants and proteins, with 176 pQTLs for 145 CSF proteins (P < 1.25 × 10^-10 , 117 cis-pQTLs and 59 trans-pQTLs). Ventricular volume (measured with brain magnetic resonance imaging) was a confounder for several pQTLs. pQTLs for CSF and plasma proteins were overall correlated, but CSF-specific pQTLs were also observed. Mendelian randomization analyses suggested causal roles for several proteins, for example, ApoE, CD33, and GRN in Alzheimer's disease, MMP-10 in preclinical Alzheimer's disease, SIGLEC9 in amyotrophic lateral sclerosis, and CD38, GPNMB, and ADAM15 in Parkinson's disease. CSF levels of GRN, MMP-10, and GPNMB were altered in Alzheimer's disease, preclinical Alzheimer's disease, and Parkinson's disease, respectively. These findings point to pathways to be explored for novel therapies. The novel finding that ventricular volume confounded pQTLs has implications for design of future studies of the genetic regulation of the CSF proteome.

The genomics of heart failure: design and rationale of the HERMES consortium

Aims

The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure.

Methods and results

The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P < 5 × 10⁻⁸ under an additive genetic model.

Conclusions

HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

Lymphangiogenic therapy prevents cardiac dysfunction by ameliorating inflammation and hypertension

The lymphatic vasculature is involved in the pathogenesis of acute cardiac injuries, but little is known about its role in chronic cardiac dysfunction. Here, we demonstrate that angiotensin II infusion induced cardiac inflammation and fibrosis at 1 week and caused cardiac dysfunction and impaired lymphatic transport at 6 weeks in mice, while co-administration of VEGFCc156s improved these parameters. To identify novel mechanisms underlying this protection, RNA sequencing analysis in distinct cell populations revealed that VEGFCc156s specifically modulated angiotensin II-induced inflammatory responses in cardiac and peripheral lymphatic endothelial cells. Furthermore, telemetry studies showed that while angiotensin II increased blood pressure acutely in all animals, VEGFCc156s-treated animals displayed a delayed systemic reduction in blood pressure independent of alterations in angiotensin II-mediated aortic stiffness. Overall, these results demonstrate that VEGFCc156s had a multifaceted therapeutic effect to prevent angiotensin II-induced cardiac dysfunction by improving cardiac lymphatic function, alleviating fibrosis and inflammation, and ameliorating hypertension.

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer's disease

To date, the development of disease-modifying therapies for Alzheimer’s disease (AD) has largely focused on the removal of amyloid beta Aβ fragments from the CNS. Proteomic profiling of patient fluids may help identify novel therapeutic targets and biomarkers associated with AD pathology. Here, we applied the Olink™ ProSeek immunoassay to measure 270 CSF and plasma proteins across 415 Aβ- negative cognitively normal individuals (Aβ- CN), 142 Aβ-positive CN (Aβ+ CN), 50 Aβ- mild cognitive impairment (MCI) patients, 75 Aβ+ MCI patients, and 161 Aβ+ AD patients from the Swedish BioFINDER study. A validation cohort included 59 Aβ- CN, 23 Aβ- + CN, 44 Aβ- MCI and 53 Aβ+ MCI. To compare protein concentrations in patients versus controls, we applied multiple linear regressions adjusting for age, gender, medications, smoking and mean subject-level protein concentration, and corrected findings for false discovery rate (FDR, q < 0.05). We identified, and replicated, altered levels of ten CSF proteins in Aβ+ individuals, including CHIT1, SMOC2, MMP-10, LDLR, CD200, EIF4EBP1, ALCAM, RGMB, tPA and STAMBP (− 0.14 < d < 1.16; q < 0.05). We also identified and replicated alterations of six plasma proteins in Aβ+ individuals OSM, MMP-9, HAGH, CD200, AXIN1, and uPA (− 0.77 < d < 1.28; q < 0.05). Multiple analytes associated with cognitive performance and cortical thickness (q < 0.05). Plasma biomarkers could distinguish AD dementia (AUC = 0.94, 95% CI = 0.87–0.98) and prodromal AD (AUC = 0.78, 95% CI = 0.68–0.87) from CN. These findings reemphasize the contributions of immune markers, phospholipids, angiogenic proteins and other biomarkers downstream of, and potentially orthogonal to, Aβ- and tau in AD, and identify candidate biomarkers for earlier detection of neurodegeneration.

Genome-wide RNAi screen reveals ALK1 mediates LDL uptake and transcytosis in endothelial cells

In humans and animals lacking functional LDL receptor (LDLR), LDL from plasma still readily traverses the endothelium. To identify the pathways of LDL uptake, a genome-wide RNAi screen was performed in endothelial cells and cross-referenced with GWAS-data sets. Here we show that the activin-like kinase 1 (ALK1) mediates LDL uptake into endothelial cells. ALK1 binds LDL with lower affinity than LDLR and saturates only at hypercholesterolemic concentrations. ALK1 mediates uptake of LDL into endothelial cells via an unusual endocytic pathway that diverts the ligand from lysosomal degradation and promotes LDL transcytosis. The endothelium-specific genetic ablation of Alk1 in Ldlr-KO animals leads to less LDL uptake into the aortic endothelium, showing its physiological role in endothelial lipoprotein metabolism. In summary, identification of pathways mediating LDLR-independent uptake of LDL may provide unique opportunities to block the initiation of LDL accumulation in the vessel wall or augment hepatic LDLR-dependent clearance of LDL.

Atherosclerosis is caused by low-density lipoprotein (LDL) buildup in the vessel wall, a process thought to be mediated by LDL receptor alone. Here, the authors show that the endothelium can uptake LDL via ALK1, a TGFβ signalling receptor, suggesting new therapies for blocking LDL accumulation in the vessel wall.

Multiple genes influence BMI on chromosome 7q31-34: the NHLBI Family Heart Study

The National Heart, Lung, and Blood Institute Family Heart Study (FHS) genome-wide linkage scan identified a region of chromosome 7q31-34 with a lod score of 4.9 for BMI at D7S1804 (131.9 Mb). We report the results of linkage and association to BMI in this region for two independent FHS samples. The first sample includes 225 FHS pedigrees with evidence of linkage to 7q31-34, using 1,132 single-nucleotide polymorphisms (SNPs) and 7 microsatellites. The second represents a case-control sample (318 cases; BMI >25 and 325 controls; BMI <25) derived from unrelated FHS participants who were not part of the genome scan. The latter set was genotyped for 606 SNPs, including 37 SNPs with prior evidence for association in the linked families. Although variance components linkage analysis using only SNPs generated a peak lod score that coincided with the original linkage scan at 131.9 Mb, a conditional linkage analysis showed evidence of a second quantitative trait locus (QTL) near 143 cM influencing BMI. Three SNPs (rs161339, rs12673281, and rs1993068) located near the three genes pleiotrophin (PTN), diacylglycerol (DAG) kinase iota (DGK iota), and cholinergic receptor, muscarinic 2 (CHRM2) demonstrated significant association in both linked families (P = 0.0005, 0.002, and 0.03, respectively) and the case-control sample (P = 0.01, 0.0003, and 0.03, respectively), regardless of the genetic model tested. These findings suggest that several genes may be associated with BMI in the 7q31-34 region.

A genome-wide association study of pulmonary function measures in the Framingham Heart Study

The ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC) is a measure used to diagnose airflow obstruction and is highly heritable. We performed a genome-wide association study in 7,691 Framingham Heart Study participants to identify single-nucleotide polymorphisms (SNPs) associated with the FEV₁/FVC ratio, analyzed as a percent of the predicted value. Identified SNPs were examined in an independent set of 835 Family Heart Study participants enriched for airflow obstruction. Four SNPs in tight linkage disequilibrium on chromosome 4q31 were associated with the percent predicted FEV₁/FVC ratio with p-values of genome-wide significance in the Framingham sample (best p-value = 3.6e-09). One of the four chromosome 4q31 SNPs (rs13147758; p-value 2.3e-08 in Framingham) was genotyped in the Family Heart Study and produced evidence of association with the same phenotype, percent predicted FEV₁/FVC (p-value = 2.0e-04). The effect estimates for association in the Framingham and Family Heart studies were in the same direction, with the minor allele (G) associated with higher FEV₁/FVC ratio levels. Results from the Family Heart Study demonstrated that the association extended to FEV₁ and dichotomous airflow obstruction phenotypes, particularly among smokers. The SNP rs13147758 was associated with the percent predicted FEV₁/FVC ratio in independent samples from the Framingham and Family Heart Studies producing a combined p-value of 8.3e-11, and this region of chromosome 4 around 145.68 megabases was associated with COPD in three additional populations reported in the accompanying manuscript. The associated SNPs do not lie within a gene transcript but are near the hedgehog-interacting protein (HHIP) gene and several expressed sequence tags cloned from fetal lung. Though it is unclear what gene or regulatory effect explains the association, the region warrants further investigation.

Cigarette smoking is the primary risk factor for impaired lung function, yet only 20% of smokers develop chronic obstructive pulmonary disease (COPD). This observation, along with family studies of lung function and COPD, suggests that genetic factors influence susceptibility to cigarette smoke. We examined the relationship between common genetic variants and measures of lung function in a sample of 7,691 participants from the Framingham Heart Study and confirmed our observations in 835 participants from the Family Heart Study selected to include cases of airflow obstruction. We identified a variant on chromosome 4 that was strongly associated with FEV₁/FVC in the Framingham Study and confirmed the association in the Family Heart Study. The accompanying manuscript identified the same region to be associated with COPD. Several interesting genes are present in the region that we identified, including a gene (HHIP) interacting with a biological pathway involved in lung development, but it is not yet clear which gene in the region explains the association. Our results identified a region of chromosome 4 that warrants further study to understand the genetic effects influencing lung function.

The Gly2019Ser mutation in LRRK2 is not fully penetrant in familial Parkinson's disease: the GenePD study

BACKGROUND

We report age-dependent penetrance estimates for leucine-rich repeat kinase 2 (LRRK2)-related Parkinson's disease (PD) in a large sample of familial PD. The most frequently seen LRRK2 mutation, Gly2019Ser (G2019S), is associated with approximately 5 to 6% of familial PD cases and 1 to 2% of idiopathic cases, making it the most common known genetic cause of PD. Studies of the penetrance of LRRK2 mutations have produced a wide range of estimates, possibly due to differences in study design and recruitment, including in particular differences between samples of familial PD versus sporadic PD.

METHODS

A sample, including 903 affected and 58 unaffected members from 509 families ascertained for having two or more PD-affected members, 126 randomly ascertained PD patients and 197 controls, was screened for five different LRRK2 mutations. Penetrance was estimated in families of LRRK2 carriers with consideration of the inherent bias towards increased penetrance in a familial sample.

RESULTS

Thirty-one out of 509 families with multiple cases of PD (6.1%) were found to have 58 LRRK2 mutation carriers (6.4%). Twenty-nine of the 31 families had G2019S mutations while two had R1441C mutations. No mutations were identified among controls or unaffected relatives of PD cases. Nine PD-affected relatives of G2019S carriers did not carry the LRRK2 mutation themselves. At the maximum observed age range of 90 to 94 years, the unbiased estimated penetrance was 67% for G2019S families, compared with a baseline PD risk of 17% seen in the non-LRRK2-related PD families.

CONCLUSION

Lifetime penetrance of LRRK2 estimated in the unascertained relatives of multiplex PD families is greater than that reported in studies of sporadically ascertained LRRK2 cases, suggesting that inherited susceptibility factors may modify the penetrance of LRRK2 mutations. In addition, the presence of nine PD phenocopies in the LRRK2 families suggests that these susceptibility factors may also increase the risk of non-LRRK2-related PD. No differences in penetrance were found between men and women, suggesting that the factors that influence penetrance for LRRK2 carriers are independent of the factors which increase PD prevalence in men.

Replication of association between ELAVL4 and Parkinson disease: the GenePD study

Genetic variants in embryonic lethal, abnormal vision, Drosophila-like 4 (ELAVL4) have been reported to be associated with onset age of Parkinson disease (PD) or risk for PD affection in Caucasian populations. In the current study we genotyped three single nucleotide polymorphisms in ELAVL4 in a Caucasian study sample consisting of 712 PD patients and 312 unrelated controls from the GenePD study. The minor allele of rs967582 was associated with increased risk of PD (odds ratio = 1.46, nominal P value = 0.011) in the GenePD population. The minor allele of rs967582 was also the risk allele for PD affection or earlier onset age in the previously studied populations. This replication of association with rs967582 in a third cohort further implicates ELAVL4 as a PD susceptibility gene.

Haplotypes and gene expression implicate the MAPT region for Parkinson disease: the GenePD Study

BACKGROUND

Microtubule-associated protein tau (MAPT) has been associated with several neurodegenerative disorders including forms of parkinsonism and Parkinson disease (PD). We evaluated the association of the MAPT region with PD in a large cohort of familial PD cases recruited by the GenePD Study. In addition, postmortem brain samples from patients with PD and neurologically normal controls were used to evaluate whether the expression of the 3-repeat and 4-repeat isoforms of MAPT, and neighboring genes Saitohin (STH) and KIAA1267, are altered in PD cerebellum.

METHODS

Twenty-one single-nucleotide polymorphisms (SNPs) in the region of MAPT on chromosome 17q21 were genotyped in the GenePD Study. Single SNPs and haplotypes, including the H1 haplotype, were evaluated for association to PD. Relative quantification of gene expression was performed using real-time RT-PCR.

RESULTS

After adjusting for multiple comparisons, SNP rs1800547 was significantly associated with PD affection. While the H1 haplotype was associated with a significantly increased risk for PD, a novel H1 subhaplotype was identified that predicted a greater increased risk for PD. The expression of 4-repeat MAPT, STH, and KIAA1267 was significantly increased in PD brains relative to controls. No difference in expression was observed for 3-repeat MAPT.

CONCLUSIONS

This study supports a role for MAPT in the pathogenesis of familial and idiopathic Parkinson disease (PD). Interestingly, the results of the gene expression studies suggest that other genes in the vicinity of MAPT, specifically STH and KIAA1267, may also have a role in PD and suggest complex effects for the genes in this region on PD risk.

Polymorphisms near EXOC4 and LRGUK on chromosome 7q32 are associated with Type 2 Diabetes and fasting glucose; the NHLBI Family Heart Study

Background

The chromosome 7q32 region is linked to metabolic syndrome and obesity related traits in the Family Heart Study. As part of a fine mapping study of the region, we evaluated the relationship of polymorphisms to fasting glucose levels and Type 2 diabetes.

Methods

Thirty-nine HapMap defined tag SNPs in a 1.08 Mb region and a novel deletion polymorphism were genotyped in 2,603 participants of the NHLBI Family Heart Study (FHS). Regression modeling, adjusting for BMI, age, sex, smoking and the TCF7L2 polymorphism, was used to evaluate the association of these polymorphisms with T2D and fasting glucoses levels.

Results

The deletion polymorphism confers a protective effect for T2D, with homozygous deletion carriers having a 53% reduced risk compared to non-deleted carriers. Among non-diabetics, the deletion was significantly associated with lower fasting glucose levels in men (p = 0.038) but not women (p = 0.118). In addition, seven SNPs near the deletion were significantly associated (p < 0.01) to diabetes.

Conclusion

Chromosome 7q32 contains both SNPs and a deletion that were associated to T2D. Although the deletion region contains several islands of strongly conserved sequence, it is not known to contain a transcribed gene. The closest nearby gene, EXOC4, is involved in insulin-stimulated glucose transport and may be a candidate for this association. Further work is needed to determine if the deletion represents a functional variant or may be in linkage disequilibrium with a functional mutation influencing EXOC4 or another nearby gene.

NYD-SP18 is associated with obesity in the NHLBI Family Heart Study

OBJECTIVE

The NHLBI Family Heart Study (FHS) genome-wide linkage scan identified a region of chromosome 7q with a logarithm of odds score of 4.9 for body mass index (BMI).

DESIGN

We report the results of fine mapping the linkage peak using 1020 single nucleotide polymorphisms (SNPs) to test for association to obesity in families exhibiting linkage to chromosome 7. Association observed in linked families (284 obese cases/381 controls) was examined in an independent set of unrelated FHS participants (172 obese cases/308 controls) to validate the observed association. Two dichotomous obesity phenotypes were studied based on clinical BMI cutoffs and the sex-specific distribution of both BMI and leptin levels.

RESULTS

Using a P-value of 0.01 as criteria for association in the linked families, a P-value of 0.05 as criteria for association in the unrelated sample, and requiring consistency in the direction of the effect of the minor allele between the two samples, we identified two coding SNPs in the NYD-SP18 gene with minor alleles increasing the risk of obesity. Adjustment for exercise, smoking and FTO genotype did not influence the result in linked families, but improved the result in the unrelated sample. Carrying a minor allele of the nonsynonymous SNP rs6971091 conferred an odds ratio of at least 2 for obesity defined by both BMI and leptin levels.

CONCLUSION

The effect of the NYD-SP18 SNP on obesity was larger than the effect of FTO in FHS families. Publicly available results from genome-wide association studies support the association between NYD-SP18 and BMI. The NYD-SP18 gene is described as testes development related, but little is known about the gene's function or the mechanism by which it may influence risk for obesity.