Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. Here, we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated to complex traits. We leverage expression imputation to perform a transcriptome wide association scan (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Familial hypercholesterolaemia (FH) is a common genetic cause of premature coronary heart disease (CHD). Globally, one baby is born with FH every minute. If diagnosed and treated early in childhood, individuals with FH can have normal life expectancy. This consensus paper aims to improve awareness of the need for early detection and management of FH children. Familial hypercholesterolaemia is diagnosed either on phenotypic criteria, i.e. an elevated low-density lipoprotein cholesterol (LDL-C) level plus a family history of elevated LDL-C, premature coronary artery disease and/or genetic diagnosis, or positive genetic testing. Childhood is the optimal period for discrimination between FH and non-FH using LDL-C screening. An LDL-C ≥5 mmol/L (190 mg/dL), or an LDL-C ≥4 mmol/L (160 mg/dL) with family history of premature CHD and/or high baseline cholesterol in one parent, make the phenotypic diagnosis. If a parent has a genetic defect, the LDL-C cut-off for the child is ≥3.5 mmol/L (130 mg/dL). We recommend cascade screening of families using a combined phenotypic and genotypic strategy. In children, testing is recommended from age 5 years, or earlier if homozygous FH is suspected. A healthy lifestyle and statin treatment (from age 8 to 10 years) are the cornerstones of management of heterozygous FH. Target LDL-C is <3.5 mmol/L (130 mg/dL) if >10 years, or ideally 50% reduction from baseline if 8–10 years, especially with very high LDL-C, elevated lipoprotein(a), a family history of premature CHD or other cardiovascular risk factors, balanced against the long-term risk of treatment side effects. Identifying FH early and optimally lowering LDL-C over the lifespan reduces cumulative LDL-C burden and offers health and socioeconomic benefits. To drive policy change for timely detection and management, we call for further studies in the young. Increased awareness, early identification, and optimal treatment from childhood are critical to adding decades of healthy life for children and adolescents with FH.

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS 1-3 . We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements 4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Plasma triglyceride concentration is a biomarker for circulating triglyceride-rich lipoproteins and their metabolic remnants. Common mild-to-moderate hypertriglyceridaemia is typically multigenic, and results from the cumulative burden of common and rare variants in more than 30 genes, as quantified by genetic risk scores. Rare autosomal recessive monogenic hypertriglyceridaemia can result from large-effect mutations in six different genes. Hypertriglyceridaemia is exacerbated by non-genetic factors. On the basis of recent genetic data, we redefine the disorder into two states: severe (triglyceride concentration >10 mmol/L), which is more likely to have a monogenic cause; and mild-to-moderate (triglyceride concentration 2–10 mmol/L). Because of clustering of susceptibility alleles and secondary factors in families, biochemical screening and counselling for family members is essential, but routine genetic testing is not warranted. Treatment includes management of lifestyle and secondary factors, and pharmacotherapy. In severe hypertriglyceridaemia, intervention is indicated because of pancreatitis risk; in mild-to-moderate hypertriglyceridaemia, intervention can be indicated to prevent cardiovascular disease, dependent on triglyceride concentration, concomitant lipoprotein disturbances, and overall cardiovascular risk.

Familial combined hyperlipidemia (FCHL), characterized by elevated levels of serum total cholesterol, triglycerides or both 1,2 , is observed in about 20% of individuals with premature coronary heart disease 1 . We previously identified a locus linked to FCHL on 1q21-q23 in Finnish families with the disease 3 . This region has also been linked to FCHL in families from other populations 4-6 as well as to type 2 diabetes mellitus 7-12 . These clinical entities have several overlapping phenotypic features, raising the possibility that the same gene may underlie the obtained linkage results. Here, we show that the human gene encoding thioredoxin interacting protein (TXNIP) on 1q, which underlies combined hyperlipidemia in mice 13 , is not associated with FCHL. We show that FCHL is linked and associated with the gene encoding upstream transcription factor 1 (USF1) in 60 extended families with FCHL, including 721 genotyped individuals (P = 0.00002), especially in males with high triglycerides (P = 0.0000009). Expression profiles in fat biopsy samples from individuals with FCHL seemed to differ depending on their carrier status for the associated USF1 haplotype. USF1 encodes a transcription factor known to regulate several genes of glucose and lipid metabolism 14-17 .To identify the gene on 1q21 associated with FCHL, we initially sequenced four functionally relevant regional candidates: TXNIP, USF1, retinoid X receptor gamma (RXRG) and apolipoprotein A-II (APOA2). In parallel, we carried out a functionally unbiased genetic analysis of 60 single-nucleotide polymorphisms (SNPs) in 26 genes in 42 families with FCHL, including the 31 families in the original linkage study 3 . We then genotyped the ten SNPs most likely to be relevant in the extended sample of 60 families of FCHL (Supplementary Table 1 online). Fifty SNPs were located in a 5.8-Mb region flanking the peak markers D1S104 and D1S1677 (Fig. 1). All the families that we studied included a proband with severe coronary heart disease and an abnormal lipid phenotype and an average of 5-6 members affected with FCHL.We sequenced the entire TXNIP gene and the 2,000-bp upstream DNA region in 60 FCHL probands. Of the 20 SNPs identified, none resulted in amino acid changes, and all were rare, with a maximal 7% allele frequency. We also did not observe the nonsense mutation causing hyperlipidemia in mice 13 . We genotyped the four most common SNPs in the 60 families with FCHL but found no evidence of association Table 2 for distances, SNP numbers and LD clusters of these SNPs). (c) The SNPs associated with triglyceride levels in men and (d) the SNPs associated with FCHL and triglycerides in all family members.