Genome-wide association study of anthropometric traits in Korcula Island, Croatia

Immunoglobulin G glycome composition in transition from premenopause to postmenopause

Gonadal hormones affect immunoglobulin G (IgG) glycosylation, and the more proinflammatory IgG glycome composition might be one of the molecular mechanisms behind the increased proinflammatory phenotype in perimenopause. Using ultra-high-performance liquid chromatography, we analyzed IgG glycome composition in 5,080 samples from 1940 pre-, peri-, and postmenopausal women. Statistically significant decrease in galactosylation and sialylation was observed in postmenopausal women. Furthermore, during the transition from pre- to postmenopausal period, the rate of increase in agalactosylated structures (0.051/yr; 95%CI = 0.043-0.059, p < 0.001) and decrease in digalactosylated (-0.043/yr; 95%CI = -0.050 to -0.037, p < 0.001) and monosialylated glycans (-0.029/yr; 95%CI = -0.034 to -0.024, p < 0.001) were significantly higher than in either pre- or postmenopausal periods. The conversion to the more proinflammatory IgG glycome and the resulting decrease in the ability of IgG to suppress low-grade chronic inflammation may be an important molecular mechanism mediating the increased health risk in perimenopause and postmenopause.

A study of Kibbutzim in Israel reveals risk factors for cardiometabolic traits and subtle population structure

Genetic studies in isolated populations often increase power for identifying loci associated with complex diseases and traits. We present here the Kibbutzim Family Study (KFS), aimed at investigating the genetic basis of cardiometabolic traits in extended Israeli families characterized by long-term social stability and a homogeneous environment. Extensive information on cardiometabolic traits, as well as genome-wide genotypes, were collected on 901 individuals. We observed that most KFS participants were of Ashkenazi Jewish (AJ) genetic origin, confirmed a recent severe bottleneck in the AJ recent history, and detected a subtle within-AJ population structure. Focusing on genetic variants relatively common in the KFS but very rare in Europeans, we observed that AJ-enriched variants appear in cancer-related pathways more than expected by chance. We conducted an association study of the AJ-enriched variants against 16 cardiometabolic traits, and found seven loci (24 variants) to be significantly associated. The strongest association, which we also replicated in an independent study, was between a variant upstream of MSRA (frequency ≈1% in the KFS and nearly absent in Europeans) and weight (P = 3.6∙10-8). In conclusion, the KFS is a valuable resource for the study of the population genetics of Israel as well as the genetics of cardiometabolic traits.

Validation of Surrogates of Urine Osmolality in Population Studies

BACKGROUND

The importance of vasopressin and/or urine concentration in various kidney, cardiovascular, and metabolic diseases has been emphasized recently. Due to technical constraints, urine osmolality (Uosm), a direct reflect of urinary concentrating activity, is rarely measured in epidemiologic studies.

METHODS

We analyzed 2 possible surrogates of Uosm in 4 large population-based cohorts (total n = 4,247) and in patients with chronic kidney disease (CKD, n = 146). An estimated Uosm (eUosm) based on the concentrations of sodium, potassium, and urea, and a urine concentrating index (UCI) based on the ratio of creatinine concentrations in urine and plasma were compared to the measured Uosm (mUosm).

RESULTS

eUosm is an excellent surrogate of mUosm, with a highly significant linear relationship and values within 5% of mUosm (r = 0.99 or 0.98 in each population cohort). Bland-Altman plots show a good agreement between eUosm and mUosm with mean differences between the 2 variables within ±24 mmol/L. This was verified in men and women, in day and night urine samples, and in CKD patients. The relationship of UCI with mUosm is also significant but is not linear and exhibits more dispersed values. Moreover, the latter index is no longer representative of mUosm in patients with CKD as it declines much more quickly with declining glomerular filtration rate than mUosm.

CONCLUSION

The eUosm is a valid marker of urine concentration in population-based and CKD cohorts. The UCI can provide an estimate of urine concentration when no other measurement is available, but should be used only in subjects with normal renal function.

Recent genomic heritage in Scotland

BACKGROUND

The Generation Scotland Scottish Family Health Study (GS:SFHS) includes 23,960 participants from across Scotland with records for many health-related traits and environmental covariates. Genotypes at ~700 K SNPs are currently available for 10,000 participants. The cohort was designed as a resource for genetic and health related research and the study of complex traits. In this study we developed a suite of analyses to disentangle the genomic differentiation within GS:SFHS individuals to describe and optimise the sample and methods for future analyses.

RESULTS

We combined the genotypic information of GS:SFHS with 1092 individuals from the 1000 Genomes project and estimated their genomic relationships. Then, we performed Principal Component Analyses of the resulting relationships to investigate the genomic origin of different groups. We characterised two groups of individuals: those with a few sparse rare markers in the genome, and those with several large rare haplotypes which might represent relatively recent exogenous ancestors. We identified some individuals with likely Italian ancestry and a group with some potential African/Asian ancestry. An analysis of homozygosity in the GS:SFHS sample revealed a very similar pattern to other European populations. We also identified an individual carrying a chromosome 1 uniparental disomy. We found evidence of local geographic stratification within the population having impact on the genomic structure.

CONCLUSIONS

These findings illuminate the history of the Scottish population and have implications for further analyses such as the study of the contributions of common and rare variants to trait heritabilities and the evaluation of genomic and phenotypic prediction of disease.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies

Obesity is reaching epidemic proportions with recent worldwide figures estimated at 1.4 billion and rising year-on-year. Obesity affects all socioeconomic backgrounds and ethnicities and is a pre-requisite for metabolic syndrome. Metabolic syndrome is a clustering of risk factors, such as central obesity, insulin resistance, dyslipidaemia and hypertension that together culminate in the increased risk of type 2 diabetes mellitus and cardiovascular disease. As these conditions are among the leading causes of deaths worldwide and metabolic syndrome increases the risk of type 2 diabetes mellitus fivefold and cardiovascular disease threefold, it is of critical importance that a precise definition is agreed upon by all interested parties. Also of particular interest is the relationship between metabolic syndrome and cancer. Metabolic syndrome has been associated with a plethora of cancers including breast, pancreatic, colon and liver cancer. Furthermore, each individual risk factor for metabolic syndrome has also an association with cancer. Our review collates internationally generated information on metabolic syndrome, its many definitions and its associations with life-threatening conditions including type 2 diabetes mellitus, cardiovascular disease and cancer, providing a foundation for future advancements on this topic.

The CC genotype of the delta-sarcoglycan gene polymorphism rs13170573 is associated with obstructive sleep apnea in the Chinese population

Obstructive sleep apnea (OSA) is a highly heterogeneous sleep disorder, and increasing evidence suggests that genetic factors play a role in the etiology of OSA. Airway muscle dysfunction might promote pharyngeal collapsibility, mutations or single nucleotide polymorphisms (SNPs) in the delta-sarcoglycan (SCGD) gene associated with muscle dysfunction. To evaluate if SCGD gene SNPs are associated with OSA, 101 individuals without OSA and 97 OSA patients were recruited randomly. The genotype distributions of SNPs (rs157350, rs7715464, rs32076, rs13170573 and rs1835919) in case and control populations were evaluated. The GG, GC and CC genotypes of rs13170573 in control and OSA groups were 51.5% and 37.1%, 36.6% and 35.1%, and 11.9% and 27.8%, respectively. Significantly fewer OSA patients possessed the GG genotype and significantly more possessed the CC genotype compared with controls. Further multivariate logistic regression analysis showed that the CC genotype was an independent risk factor for OSA, with an odds ratio (OR) of 2.17 (95% confidence interval [CI]: 1.19-6.01). Other factors, such as age ≥ 50 years, male gender, body mass index (BMI) ≥ 25 kg/m(2), low-density lipoprotein cholesterol (LDL-C) level ≥ 3.33 mg/dL, smoking and hypertension, were also independent risk factors for OSA in our multivariate logistic regression model.

A draft de novo genome assembly for the northern bobwhite (Colinus virginianus) reveals evidence for a rapid decline in effective population size beginning in the Late Pleistocene

Wild populations of northern bobwhites (Colinus virginianus; hereafter bobwhite) have declined across nearly all of their U.S. range, and despite their importance as an experimental wildlife model for ecotoxicology studies, no bobwhite draft genome assembly currently exists. Herein, we present a bobwhite draft de novo genome assembly with annotation, comparative analyses including genome-wide analyses of divergence with the chicken (Gallus gallus) and zebra finch (Taeniopygia guttata) genomes, and coalescent modeling to reconstruct the demographic history of the bobwhite for comparison to other birds currently in decline (i.e., scarlet macaw; Ara macao). More than 90% of the assembled bobwhite genome was captured within <40,000 final scaffolds (N50 = 45.4 Kb) despite evidence for approximately 3.22 heterozygous polymorphisms per Kb, and three annotation analyses produced evidence for >14,000 unique genes and proteins. Bobwhite analyses of divergence with the chicken and zebra finch genomes revealed many extremely conserved gene sequences, and evidence for lineage-specific divergence of noncoding regions. Coalescent models for reconstructing the demographic history of the bobwhite and the scarlet macaw provided evidence for population bottlenecks which were temporally coincident with human colonization of the New World, the late Pleistocene collapse of the megafauna, and the last glacial maximum. Demographic trends predicted for the bobwhite and the scarlet macaw also were concordant with how opposing natural selection strategies (i.e., skewness in the r-/K-selection continuum) would be expected to shape genome diversity and the effective population sizes in these species, which is directly relevant to future conservation efforts.

Genome-wide association studies of obesity and metabolic syndrome

Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years.

Local exome sequences facilitate imputation of less common variants and increase power of genome wide association studies

The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1-10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28-38%, for SNPs with a minor allele frequency in the range 1-3%.

Inference of identity by descent in population isolates and optimal sequencing studies

In an isolated population, individuals are likely to share large genetic regions inherited from common ancestors. Identity by descent (IBD) can be inferred from SNP genotypes, which is useful in a number of applications, including identifying genetic variants influencing complex disease risk, and planning efficient cohort-sequencing strategies. We present ANCHAP--a method for detecting IBD in isolated populations. We compare accuracy of the method against other long-range and local phasing methods, using parent-offspring trios. In our experiments, we show that ANCHAP performs similarly as the other long-range method, but requires an order-of-magnitude less computational resources. A local phasing model is able to achieve similar sensitivity, but only at the cost of higher false discovery rates. In some regions of the genome, the studied individuals share haplotypes particularly often, which hints at the history of the populations studied. We demonstrate the method using SNP genotypes from three isolated island populations, as well as in a cohort of unrelated individuals. In samples from three isolated populations of around 1000 individual each, an average individual shares a haplotype at a genetic locus with 9-12 other individuals, compared with only 1 individual within the non-isolated population. We describe an application of ANCHAP to optimally choose samples in resequencing studies. We find that with sample sizes of 1000 individuals from an isolated population genotyped using a dense SNP array, and with 20% of these individuals sequenced, 65% of sequences of the unsequenced subjects can be partially inferred.

Impact of common variation in bone-related genes on type 2 diabetes and related traits

Exploring genetic pleiotropy can provide clues to a mechanism underlying the observed epidemiological association between type 2 diabetes and heightened fracture risk. We examined genetic variants associated with bone mineral density (BMD) for association with type 2 diabetes and glycemic traits in large well-phenotyped and -genotyped consortia. We undertook follow-up analysis in ∼19,000 individuals and assessed gene expression. We queried single nucleotide polymorphisms (SNPs) associated with BMD at levels of genome-wide significance, variants in linkage disequilibrium (r(2) > 0.5), and BMD candidate genes. SNP rs6867040, at the ITGA1 locus, was associated with a 0.0166 mmol/L (0.004) increase in fasting glucose per C allele in the combined analysis. Genetic variants in the ITGA1 locus were associated with its expression in the liver but not in adipose tissue. ITGA1 variants appeared among the top loci associated with type 2 diabetes, fasting insulin, β-cell function by homeostasis model assessment, and 2-h post-oral glucose tolerance test glucose and insulin levels. ITGA1 has demonstrated genetic pleiotropy in prior studies, and its suggested role in liver fibrosis, insulin secretion, and bone healing lends credence to its contribution to both osteoporosis and type 2 diabetes. These findings further underscore the link between skeletal and glucose metabolism and highlight a locus to direct future investigations.

Genome-wide analysis of epistasis in body mass index using multiple human populations

We surveyed gene–gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E−08) and a Bonferroni corrected threshold (P=1.1E−12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E−08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E−08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.

Extent of height variability explained by known height-associated genetic variants in an isolated population of the Adriatic coast of Croatia

BACKGROUND

Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.

METHODOLOGY/PRINCIPAL FINDINGS

In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20~50 SNPs reported by the remaining individual GWA studies explained 3~5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.

CONCLUSIONS/SIGNIFICANCE

We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.

Characterisation of genome-wide association epistasis signals for serum uric acid in human population isolates

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.

Interactions between genetic variants in glucose transporter type 9 (SLC2A9) and dietary habits in serum uric acid regulation

AIM

To investigate possible interactions between genetic variants in glucose transporter type 9 (SLC2A9) gene and dietary habits in serum uric acid regulation.

METHODS

Participants for this study were recruited from two isolated Croatian island communities of Vis (n=918) and Korcula (n=898). Three single nucleotide polymorphisms (SNP) from the SLC2A9 gene (rs1014290, rs6449213, rs737267) were correlated with dietary habits and uric acid.

RESULTS

A significant decrease in uric acid levels was recorded with increasing consumption of milk, sour cream, duck and turkey, and eggs. The only significant interaction was found between potato consumption and rs737267 and a near-significant interaction was found between soft drinks and rs1014290 (interaction P=0.068). Increased consumption of soft drinks interacting with the TT genotype at rs1014290 increased serum uric acid. No significant interactions were observed between food products consumption and rs6449213.

CONCLUSION

There is a certain extent of interaction between SLC2A9 and dietary patterns in serum uric acid determination. The metabolic effect of soft drinks seems to be determined by the underlying genotype of rs1014290.

Predictive value of 8 genetic loci for serum uric acid concentration

AIM

To investigate the value of genomic information in prediction of individual serum uric acid concentrations.

METHODS

Three population samples were investigated: from isolated Adriatic island communities of Vis (n=980) and Korcula (n=944), and from general population of the city of Split (n=507). Serum uric acid concentration was correlated with the genetic risk score based on 8 previously described genes: PDZK1, GCKR, SLC2A9, ABCG2, LRRC16A, SLC17A1, SLC16A9, and SLC22A12, represented by a total of 16 single-nucleotide polymorphisms (SNP). The data were analyzed using classification and regression tree (CART) and general linear modeling.

RESULTS

The most important variables for uric acid prediction with CART were genetic risk score in men and age in women. The percent of variance for any single SNP in predicting serum uric acid concentration varied from 0.0%-2.0%. The use of genetic risk score explained 0.1%-2.5% of uric acid variance in men and 3.9%-4.9% in women. The highest percent of variance was obtained when age, sex, and genetic risk score were used as predictors, with a total of 30.9% of variance in pooled analysis.

CONCLUSION

Despite overall low percent of explained variance, uric acid seems to be among the most predictive human quantitative traits based on the currently available SNP information. The use of genetic risk scores is a valuable approach in genetic epidemiology and increases the predictability of human quantitative traits based on genomic information compared with single SNP approach.

GSA-SNP: a general approach for gene set analysis of polymorphisms

Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population.

A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level

BACKGROUND

Serum creatinine (S CR) is the most important biomarker for a quick and non-invasive assessment of kidney function in population-based surveys. A substantial proportion of the inter-individual variability in S CR level is explicable by genetic factors.

METHODS

We performed a meta-analysis of genome-wide association studies of S CR undertaken in five population isolates ('discovery cohorts'), all of which are part of the European Special Population Network (EUROSPAN) project. Genes showing the strongest evidence for an association with SCR (candidate loci) were replicated in two additional population-based samples ('replication cohorts').

RESULTS

After the discovery meta-analysis, 29 loci were selected for replication. Association between SCR level and polymorphisms in the collagen type XXII alpha 1 (COL22A1) gene, on chromosome 8, and in the synaptotagmin-1 (SYT1) gene, on chromosome 12, were successfully replicated in the replication cohorts (p value = 1.0 x 10(-6) and 1.7 x 10(-4), respectively). Evidence of association was also found for polymorphisms in a locus including the gamma-aminobutyric acid receptor rho-2 (GABRR2) gene and the ubiquitin-conjugating enzyme E2-J1 (UBE2J1) gene (replication p value = 3.6 x 10(-3)). Previously reported findings, associating glomerular filtration rate with SNPs in the uromodulin (UMOD) gene and in the schroom family member 3 (SCHROOM3) gene were also replicated.

CONCLUSIONS

While confirming earlier results, our study provides new insights in the understanding of the genetic basis of serum creatinine regulatory processes. In particular, the association with the genes SYT1 and GABRR2 corroborate previous findings that highlighted a possible role of the neurotransmitters GABAA receptors in the regulation of the glomerular basement membrane and a possible interaction between GABAA receptors and synaptotagmin-I at the podocyte level.