Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses.

The immune response to pathogens varies substantially among people. Whereas both genetic and nongenetic factors contribute to interperson variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine production after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine-stimulus pairs, 11 categories of host factors together explained up to 67% of interindividual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine production (correlation 0.28-0.89), and nongenetic factors influenced cytokine production as well.

Host genetic regulation of human gut microbial structural variation.

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

Multi-ancestry polygenic risk scores for venous thromboembolism.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx_combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx_combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE.

Gut Microbial Associations to Plasma Metabolites Linked to Cardiovascular Phenotypes and Risk.

RATIONALE: Altered gut microbial composition has been linked to cardiovascular diseases (CVDs), but its functional links to host metabolism and immunity in relation to CVD development remain unclear. OBJECTIVES: To systematically assess functional links between the microbiome and the plasma metabolome, cardiometabolic phenotypes, and CVD risk and to identify diet-microbe-metabolism-immune interactions in well-documented cohorts. METHODS AND RESULTS: We assessed metagenomics-based microbial associations between 231 plasma metabolites and microbial species and pathways in the population-based LLD (Lifelines DEEP) cohort (n=978) and a clinical obesity cohort (n=297). After correcting for age, sex, and body mass index, the gut microbiome could explain ≤11.1% and 16.4% of the variation in plasma metabolites in the population-based and obesity cohorts, respectively. Obese-specific microbial associations were found for lipid compositions in the VLDL, IDL, and LDL lipoprotein subclasses. Bacterial L-methionine biosynthesis and a Ruminococcus species were associated to cardiovascular phenotypes in obese individuals, namely atherosclerosis and liver fat content, respectively. Integration of microbiome-diet-inflammation analysis in relation to metabolic risk score of CVD in the population cohort revealed 48 microbial pathways associated to CVD risk that were largely independent of diet and inflammation. Our data also showed that plasma levels rather than fecal levels of short-chain fatty acids were relevant to inflammation and CVD risk. CONCLUSIONS: This study presents the largest metagenome-based association study on plasma metabolism and microbiome relevance to diet, inflammation, CVD risk, and cardiometabolic phenotypes in both population-based and clinical obesity cohorts. Our findings identified novel bacterial species and pathways that associated to specific lipoprotein subclasses and revealed functional links between the gut microbiome and host health that provide a basis for developing microbiome-targeted therapy for disease prevention and treatment.

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).

FTO genotype is associated with phenotypic variability of body mass index.

There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.

Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis.

BACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. CONCLUSIONS: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.

Population genomic analysis of ancient and modern genomes yields new insights into the genetic ancestry of the Tyrolean Iceman and the genetic structure of Europe.

Genome sequencing of the 5,300-year-old mummy of the Tyrolean Iceman, found in 1991 on a glacier near the border of Italy and Austria, has yielded new insights into his origin and relationship to modern European populations. A key finding of that study was an apparent recent common ancestry with individuals from Sardinia, based largely on the Y chromosome haplogroup and common autosomal SNP variation. Here, we compiled and analyzed genomic datasets from both modern and ancient Europeans, including genome sequence data from over 400 Sardinians and two ancient Thracians from Bulgaria, to investigate this result in greater detail and determine its implications for the genetic structure of Neolithic Europe. Using whole-genome sequencing data, we confirm that the Iceman is, indeed, most closely related to Sardinians. Furthermore, we show that this relationship extends to other individuals from cultural contexts associated with the spread of agriculture during the Neolithic transition, in contrast to individuals from a hunter-gatherer context. We hypothesize that this genetic affinity of ancient samples from different parts of Europe with Sardinians represents a common genetic component that was geographically widespread across Europe during the Neolithic, likely related to migrations and population expansions associated with the spread of agriculture.

Methods for association analysis and meta-analysis of rare variants in families.

Advances in exome sequencing and the development of exome genotyping arrays are enabling explorations of association between rare coding variants and complex traits. To ensure power for these rare variant analyses, a variety of association tests that group variants by gene or functional unit have been proposed. Here, we extend these tests to family-based studies. We develop family-based burden tests, variable frequency threshold tests and sequence kernel association tests. Through simulations, we compare the performance of different tests. We describe situations where family-based studies provide greater power than studies of unrelated individuals to detect rare variants associated with moderate to large changes in trait values. Broadly speaking, we find that when sample sizes are limited and only a modest fraction of all trait-associated variants can be identified, family samples are more powerful. Finally, we illustrate our approach by analyzing the relationship between coding variants and levels of high-density lipoprotein (HDL) cholesterol in 11,556 individuals from the HUNT and SardiNIA studies, demonstrating association for coding variants in the APOC3, CETP, LIPC, LIPG, and LPL genes and illustrating the value of family samples, meta-analysis, and gene-level tests. Our methods are implemented in freely available C++ code.

Genotype calling and haplotyping in parent-offspring trios.

Emerging sequencing technologies allow common and rare variants to be systematically assayed across the human genome in many individuals. In order to improve variant detection and genotype calling, raw sequence data are typically examined across many individuals. Here, we describe a method for genotype calling in settings where sequence data are available for unrelated individuals and parent-offspring trios and show that modeling trio information can greatly increase the accuracy of inferred genotypes and haplotypes, especially on low to modest depth sequencing data. Our method considers both linkage disequilibrium (LD) patterns and the constraints imposed by family structure when assigning individual genotypes and haplotypes. Using simulations, we show that trios provide higher genotype calling accuracy across the frequency spectrum, both overall and at hard-to-call heterozygous sites. In addition, trios provide greatly improved phasing accuracy--improving the accuracy of downstream analyses (such as genotype imputation) that rely on phased haplotypes. To further evaluate our approach, we analyzed data on the first 508 individuals sequenced by the SardiNIA sequencing project. Our results show that our method reduces the genotyping error rate by 50% compared with analysis using existing methods that ignore family structure. We anticipate our method will facilitate genotype calling and haplotype inference for many ongoing sequencing projects.

Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course.

Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.0 × 10⁻8) near FTO (P = 3.72 × 10⁻²³), TMEM18 (P = 3.24 × 10⁻¹7), MC4R (P = 4.41 × 10⁻¹7), TNNI3K (P = 4.32 × 10⁻¹¹), SEC16B (P = 6.24 × 10⁻9), GNPDA2 (P = 1.11 × 10⁻8) and POMC (P = 4.94 × 10⁻8) as well as a potential secondary signal at the POMC locus (rs2118404, P = 2.4 × 10⁻5 after conditioning on the established single-nucleotide polymorphism at this locus) in adolescents and young adults. To evaluate the impact of the established genetic loci on BMI at these young ages, we examined differences between the effect sizes of 32 published BMI loci in European adult populations (aged 18-90) and those observed in our adolescent and young adult meta-analysis. Four loci (near PRKD1, TNNI3K, SEC16B and CADM2) had larger effects and one locus (near SH2B1) had a smaller effect on BMI during adolescence and young adulthood compared with older adults (P < 0.05). These results suggest that genetic loci for BMI can vary in their effects across the life course, underlying the importance of evaluating BMI at different ages.

The association of thyroid peroxidase antibody risk loci with susceptibility to and phenotype of Graves' disease.

BACKGROUND: Despite great progress, the genetic basis of Graves' disease (GD) remains poorly understood. Recently, a population-based genomewide association study (GWAS) identified five novel loci (ATXN2/SH2B3, MAGI3, BACH2, TPO and KALRN) as significantly associated with the presence of thyroid peroxidase autoantibodies (TPOAbs), whereas several other loci showed suggestive association. METHODS: In this study, we investigated 16 single nucleotide polymorphisms (SNPs) associated with TPOAbs for the association with susceptibility to and phenotype of GD in a cohort of 647 patients with GD and 769 controls from a Polish Caucasian population. RESULTS: SNPs within/near HCP5 (rs3094228, P = 1·6 × 10(-12) , OR = 1·88), MAGI3 (rs1230666, P = 1·9 × 10(-5) , OR = 1·51) and ATXN2/SH2B3 (rs653178, P = 0·0015, OR = 1·28) loci were significantly associated with susceptibility to GD. Allele frequencies differed significantly in subgroups of patients with GD stratified by age of GD onset for HCP5 (P = 0·0014, OR = 1·50) and showed a suggestive difference for MAGI3 (P = 0·0035, OR = 1·50) SNPs. Although rs11675434 located near TPO showed no association with GD susceptibility, it was significantly associated with the presence of clinically evident Graves' ophthalmopathy (GO, P = 5·2 × 10(-5) , OR = 1·64), and this effect was independent from smoking status, age of GD onset and gender. CONCLUSIONS: This is the first study showing an association of the ATXN2/SH2B3 locus with susceptibility to GD. Furthermore, we observed a novel significant association within the HLA region at a SNP located near HCP5 and confirmed the association of the MAGI3 locus with GD susceptibility. HCP5 and MAGI3 SNPs were further correlated with age of GD onset. Finally, we identified TPO as a new susceptibility locus for GO.

Genome-wide association study of susceptibility loci for breast cancer in Sardinian population.

BACKGROUND: Despite progress in identifying genes associated with breast cancer, many more risk loci exist. Genome-wide association analyses in genetically-homogeneous populations, such as that of Sardinia (Italy), could represent an additional approach to detect low penetrance alleles. METHODS: We performed a genome-wide association study comparing 1431 Sardinian patients with non-familial, BRCA1/2-mutation-negative breast cancer to 2171 healthy Sardinian blood donors. DNA was genotyped using GeneChip Human Mapping 500 K Arrays or Genome-Wide Human SNP Arrays 6.0. To increase genomic coverage, genotypes of additional SNPs were imputed using data from HapMap Phase II. After quality control filtering of genotype data, 1367 cases (9 men) and 1658 controls (1156 men) were analyzed on a total of 2,067,645 SNPs. RESULTS: Overall, 33 genomic regions (67 candidate SNPs) were associated with breast cancer risk at the p < 0(-6) level. Twenty of these regions contained defined genes, including one already associated with breast cancer risk: TOX3. With a lower threshold for preliminary significance to p < 10(-5), we identified 11 additional SNPs in FGFR2, a well-established breast cancer-associated gene. Ten candidate SNPs were selected, excluding those already associated with breast cancer, for technical validation as well as replication in 1668 samples from the same population. Only SNP rs345299, located in intron 1 of VAV3, remained suggestively associated (p-value, 1.16 x 10(-5)), but it did not associate with breast cancer risk in pooled data from two large, mixed-population cohorts. CONCLUSIONS: This study indicated the role of TOX3 and FGFR2 as breast cancer susceptibility genes in BRCA1/2-wild-type breast cancer patients from Sardinian population.

The burden of multiple sclerosis variants in continental Italians and Sardinians.

BACKGROUND: Recent studies identified > 100 non-HLA (human leukocyte antigen) multiple sclerosis (MS) susceptibility variants in Northern European populations, but their role in Southern Europeans is largely unexplored. OBJECTIVE: We aimed to investigate the cumulative impact of those variants in two Mediterranean populations: Continental Italians and Sardinians. METHODS: We calculated four weighted Genetic Risk Scores (wGRS), using up to 102 non-HLA MS risk variants and 5 HLA MS susceptibility markers in 1691 patients and 2194 controls from continental Italy; and 2861 patients and 3034 controls from Sardinia. We then assessed the differences between populations using Nagelkerke's R(2) and the area under the Receiver Operating Characteristic (ROC) curves. RESULTS: As expected, the genetic burden (mean wGRS value) was significantly higher in MS patients than in controls, in both populations. Of note, the burden was significantly higher in Sardinians. Conversely, the proportion of variability explained and the predictive power were significantly higher in continental Italians. Notably, within the Sardinian patients, we also observed a significantly higher burden of non-HLA variants in individuals who do not carry HLA risk alleles. CONCLUSIONS: The observed differences in MS genetic burden between the two Mediterranean populations highlight the need for more genetic studies in South Europeans, to further expand the knowledge of MS genetics.

A likelihood-based framework for variant calling and de novo mutation detection in families.

Family samples, which can be enriched for rare causal variants by focusing on families with multiple extreme individuals and which facilitate detection of de novo mutation events, provide an attractive resource for next-generation sequencing studies. Here, we describe, implement, and evaluate a likelihood-based framework for analysis of next generation sequence data in family samples. Our framework is able to identify variant sites accurately and to assign individual genotypes, and can handle de novo mutation events, increasing the sensitivity and specificity of variant calling and de novo mutation detection. Through simulations we show explicit modeling of family relationships is especially useful for analyses of low-frequency variants and that genotype accuracy increases with the number of individuals sequenced per family. Compared with the standard approach of ignoring relatedness, our methods identify and accurately genotype more variants, and have high specificity for detecting de novo mutation events. The improvement in accuracy using our methods over the standard approach is particularly pronounced for low-frequency variants. Furthermore the family-aware calling framework dramatically reduces Mendelian inconsistencies and is beneficial for family-based analysis. We hope our framework and software will facilitate continuing efforts to identify genetic factors underlying human diseases.

A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation.

Identifying the genes that influence levels of pro-inflammatory molecules can help to elucidate the mechanisms underlying this process. We first conducted a two-stage genome-wide association scan (GWAS) for the key inflammatory biomarkers Interleukin-6 (IL-6), the general measure of inflammation erythrocyte sedimentation rate (ESR), monocyte chemotactic protein-1 (MCP-1), and high-sensitivity C-reactive protein (hsCRP) in a large cohort of individuals from the founder population of Sardinia. By analysing 731,213 autosomal or X chromosome SNPs and an additional ∼1.9 million imputed variants in 4,694 individuals, we identified several SNPs associated with the selected quantitative trait loci (QTLs) and replicated all the top signals in an independent sample of 1,392 individuals from the same population. Next, to increase power to detect and resolve associations, we further genotyped the whole cohort (6,145 individuals) for 293,875 variants included on the ImmunoChip and MetaboChip custom arrays. Overall, our combined approach led to the identification of 9 genome-wide significant novel independent signals-5 of which were identified only with the custom arrays-and provided confirmatory evidence for an additional 7. Novel signals include: for IL-6, in the ABO gene (rs657152, p = 2.13×10(-29)); for ESR, at the HBB (rs4910472, p = 2.31×10(-11)) and UCN119B/SPPL3 (rs11829037, p = 8.91×10(-10)) loci; for MCP-1, near its receptor CCR2 (rs17141006, p = 7.53×10(-13)) and in CADM3 (rs3026968, p = 7.63×10(-13)); for hsCRP, within the CRP gene (rs3093077, p = 5.73×10(-21)), near DARC (rs3845624, p = 1.43×10(-10)), UNC119B/SPPL3 (rs11829037, p = 1.50×10(-14)), and ICOSLG/AIRE (rs113459440, p = 1.54×10(-08)) loci. Confirmatory evidence was found for IL-6 in the IL-6R gene (rs4129267); for ESR at CR1 (rs12567990) and TMEM57 (rs10903129); for MCP-1 at DARC (rs12075); and for hsCRP at CRP (rs1205), HNF1A (rs225918), and APOC-I (rs4420638). Our results improve the current knowledge of genetic variants underlying inflammation and provide novel clues for the understanding of the molecular mechanisms regulating this complex process.

The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits.

Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the "Metabochip," a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability.

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.