High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases.

Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the "gold standard" for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson's r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations.

BACKGROUND: Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits. METHODS: We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy. RESULTS: The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10-4), apolipoprotein B (p=5.6×10-4), and LDL cholesterol (p=9.5×10-4) concentrations. CONCLUSIONS: We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

Dissecting the contribution of single nucleotide polymorphisms in CCR9 and CCL25 genomic regions to the celiac disease phenotype.

PURPOSE AND OBJECTIVES: Given their role in homing immune cells to the intestine, CC motif chemokine receptor 9 (CCR9) and its specific ligand CC motif chemokine ligand 25 (CCL25) are interesting candidate genes for celiac disease. These genes are located in regions previously shown to be associated with or linked to celiac disease, but no investigations on their association with various celiac disease phenotypes have so far been conducted. Here we studied such associations of both genotyped and imputed single nucleotide polymorphisms (SNPs) with either regulatory function or exonic location of the CCR9 and CCL25 loci. RESULTS: Exploiting a carefully phenotyped cohort of 625 celiac disease patients and 1817 non-celiac controls, we identified that multiple SNPs with predicted regulatory function (RegulomeDB score ≤3a and/or eQTL effect) located between 100 kB upstream and downstream of CCR9 and CCL25 are associated with celiac disease and/or selected phenotypes. Of the genotyped SNPs in the CCR9 loci, rs213360 with an eQTL effect on CCR9 expression in blood was associated with celiac disease and all investigated phenotypes except high HLA risk. Rs1545985 with an eQTL on CCR9 expression and rs7652331 and rs12493471, both with RegulomeDB score ≤3a, were all associated with gastrointestinal symptoms and malabsorption and the latter additionally with anemia. The genotyped CCL25 SNPs rs952444 and rs882951, with RegulomeDB scores 1d and 1f respectively and eQTL effect on CCL25 expression in small intestine, were associated with gastrointestinal symptoms and malabsorption. The CCL25 SNP rs2303165 identified in sequencing followed by imputation was associated with partial villous atrophy. However, the association did not pass the permutation based multiple testing correction (PEMP2 > 0.05). CONCLUSIONS: We conclude that SNPs in the region of CCR9 and CCL25 with predicted functional effect or exonic localization likely contribute only modestly to various celiac disease phenotypes.

Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes.

The phenotype of coeliac disease varies considerably for incompletely understood reasons. We investigated whether established coeliac disease susceptibility variants (SNPs) are individually or cumulatively associated with distinct phenotypes. We also tested whether a polygenic risk score (PRS) based on genome-wide associated (GWA) data could explain the phenotypic variation. The phenotypic association of 39 non-HLA coeliac disease SNPs was tested in 625 thoroughly phenotyped coeliac disease patients and 1817 controls. To assess their cumulative effects a weighted genetic risk score (wGRS39) was built, and stratified by tertiles. In our PRS model in cases, we took the summary statistics from the largest GWA study in coeliac disease and tested their association at eight P value thresholds (PT) with phenotypes. Altogether ten SNPs were associated with distinct phenotypes after correction for multiple testing (PEMP2 ≤ 0.05). The TLR7/TLR8 locus was associated with disease onset before and the SH2B3/ATXN2, ITGA4/UBE2E3 and IL2/IL21 loci after 7 years of age. The latter three loci were associated with a more severe small bowel mucosal damage and SH2B3/ATXN2 with type 1 diabetes. Patients at the highest wGRS39 tertiles had OR > 1.62 for having coeliac disease-related symptoms during childhood, a more severe small bowel mucosal damage, malabsorption and anaemia. PRS was associated only with dermatitis herpetiformis (PT = 0.2, PEMP2 = 0.02). Independent coeliac disease-susceptibility loci are associated with distinct phenotypes, suggesting that genetic factors play a role in determining the disease presentation. Moreover, the increased number of coeliac disease susceptibility SNPs might predispose to a more severe disease course.

Phenome-wide association studies across large population cohorts support drug target validation.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data.

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise.