In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family‐based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family‐based imputation method, Genotype Imputation Given Inheritance (GIGI), is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population‐based methods perform better. Here, we propose a flexible approach to combine imputation data from both family‐ and population‐based methods. We also extend the Sequence Kernel Association Test for Rare and Common variants (SKAT‐RC), originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension “famSKAT‐RC.” We compare the performance of famSKAT‐RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of the association tests with this approach over the use of either family‐ or population‐based imputation methods alone, in the context of rare and common variants. Moreover, our results show better performance of famSKAT‐RC compared to the other considered tests, in most scenarios investigated here.

Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R² for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R² criterion; and (5) R² is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.

Background: Enthusiasm for using polygenic risk scores (PRSs) in clinical practice is tempered by concerns about their portability to diverse ancestry groups, thus motivating genome-wide association studies in non-European ancestry cohorts. Methods: We conducted a genome-wide association study for coronary heart disease in a Middle Eastern cohort using whole genome sequencing and assessed the performance of 6 PRSs developed with methods including LDpred (PGS000296), metaGRS (PGS000018), Pruning and Thresholding (PGS000337), and an EnsemblePRS we developed. Additionally, we evaluated the burden of rare variants in lipid genes in cases and controls. Whole genome sequencing at 30× coverage was performed in 1067 coronary heart disease cases (mean age=59 years; 70.3% males) and 6170 controls (mean age=40 years; 43.5% males). Results: The majority of PRSs performed well; odds ratio (OR) per 1 SD increase (OR _1sd ) was highest for PGS000337 (OR _1sd =1.81, 95% CI [1.66–1.98], P =3.07×10 ⁻⁴¹ ). EnsemblePRS performed better than individual PRSs (OR _1sd =1.8, 95% CI [1.66–1.96], P =5.89×10 ⁻⁴⁴ ). The OR for the 10th decile versus the remaining deciles was >3.2 for PGS000337, PGS000296, PGS000018, and reached 4.58 for EnsemblePRS. Of 400 known genome-wide significant loci, 33 replicated at P <10 ⁻⁴ . However, the 9p21 locus did not replicate. Six suggestive ( P <10 ⁻⁵ ) new loci/genes with plausible biological function were identified (eg, CORO7 , RBM47 , PDE4D ). The burden of rare functional variants in LDLR , APOB , PCSK9 , and ANGPTL4 was greater in cases than controls. Conclusions: Overall, we demonstrate that PRSs derived from European ancestry genome-wide association studies performed well in a Middle Eastern cohort, suggesting these could be used in the clinical setting while ancestry-specific PRSs are developed.

Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10⁻³⁰⁰) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.