Targeted genetic testing for familial hypercholesterolaemia using next generation sequencing:a population-based study

Background<p></p> Familial hypercholesterolaemia (FH) is a common Mendelian condition which, untreated, results in premature coronary heart disease. An estimated 88% of FH cases are undiagnosed in the UK. We previously validated a method for FH mutation detection in a lipid clinic population using next generation sequencing (NGS), but this did not address the challenge of identifying index cases in primary care where most undiagnosed patients receive healthcare. Here, we evaluate the targeted use of NGS as a potential route to diagnosis of FH in a primary care population subset selected for hypercholesterolaemia.<p></p> Methods<p></p> We used microfluidics-based PCR amplification coupled with NGS and multiplex ligation-dependent probe amplification (MLPA) to detect mutations in LDLR, APOB and PCSK9 in three phenotypic groups within the Generation Scotland: Scottish Family Health Study including 193 individuals with high total cholesterol, 232 with moderately high total cholesterol despite cholesterol-lowering therapy, and 192 normocholesterolaemic controls.<p></p> Results<p></p> Pathogenic mutations were found in 2.1% of hypercholesterolaemic individuals, in 2.2% of subjects on cholesterol-lowering therapy and in 42% of their available first-degree relatives. In addition, variants of uncertain clinical significance (VUCS) were detected in 1.4% of the hypercholesterolaemic and cholesterol-lowering therapy groups. No pathogenic variants or VUCS were detected in controls.<p></p> Conclusions<p></p> We demonstrated that population-based genetic testing using these protocols is able to deliver definitive molecular diagnoses of FH in individuals with high cholesterol or on cholesterol-lowering therapy. The lower cost and labour associated with NGS-based testing may increase the attractiveness of a population-based approach to FH detection compared to genetic testing with conventional sequencing. This could provide one route to increasing the present low percentage of FH cases with a genetic diagnosis

Identification and biochemical analysis of a novel APOB mutation that causes autosomal dominant hypercholesterolemia

Patients with autosomal dominant hypercholesterolemia (ADH) have a high risk of developing cardiovascular disease that can be effectively treated using statin drugs. Molecular diagnosis and family cascade screening is recommended for early identification of individuals at risk, but up to 40% of families have no mutation detected in known genes. This study combined linkage analysis and exome sequencing to identify a novel variant in exon 3 of APOB (Arg50Trp). Mass spectrometry established that low-density lipoprotein (LDL) containing Arg50Trp APOB accumulates in the circulation of affected individuals, suggesting defective hepatic uptake. Previously reported mutations in APOB causing ADH have been located in exon 26. This is the first report of a mutation outside this region causing this phenotype, therefore, more extensive screening of this large and highly polymorphic gene may be necessary in ADH families. This is now feasible due to the high capacity of recently available sequencing platforms

Identification of novel risk loci and causal insights for sporadic Creutzfeldt-Jakob disease: a genome-wide association study

Background: Human prion diseases are rare and usually rapidly fatal neurodegenerative disorders, the most common being sporadic Creutzfeldt-Jakob disease (sCJD). Variants in the PRNP gene that encodes prion protein are strong risk factors for sCJD but, although the condition has similar heritability to other neurodegenerative disorders, no other genetic risk loci have been confirmed. We aimed to discover new genetic risk factors for sCJD, and their causal mechanisms. Methods: We did a genome-wide association study of sCJD in European ancestry populations (patients diagnosed with probable or definite sCJD identified at national CJD referral centres) with a two-stage study design using genotyping arrays and exome sequencing. Conditional, transcriptional, and histological analyses of implicated genes and proteins in brain tissues, and tests of the effects of risk variants on clinical phenotypes, were done using deep longitudinal clinical cohort data. Control data from healthy individuals were obtained from publicly available datasets matched for country. Findings: Samples from 5208 cases were obtained between 1990 and 2014. We found 41 genome-wide significant single nucleotide polymorphisms (SNPs) and independently replicated findings at three loci associated with sCJD risk; within PRNP (rs1799990; additive model odds ratio [OR] 1·23 [95% CI 1·17-1·30], p=2·68 × 10-15; heterozygous model p=1·01 × 10-135), STX6 (rs3747957; OR 1·16 [1·10-1·22], p=9·74 × 10-9), and GAL3ST1 (rs2267161; OR 1·18 [1·12-1·25], p=8·60 × 10-10). Follow-up analyses showed that associations at PRNP and GAL3ST1 are likely to be caused by common variants that alter the protein sequence, whereas risk variants in STX6 are associated with increased expression of the major transcripts in disease-relevant brain regions. Interpretation: We present, to our knowledge, the first evidence of statistically robust genetic associations in sporadic human prion disease that implicate intracellular trafficking and sphingolipid metabolism as molecular causal mechanisms. Risk SNPs in STX6 are shared with progressive supranuclear palsy, a neurodegenerative disease associated with misfolding of protein tau, indicating that sCJD might share the same causal mechanisms as prion-like disorders. Funding: Medical Research Council and the UK National Institute of Health Research in part through the Biomedical Research Centre at University College London Hospitals National Health Service Foundation Trust

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease

Alzheimer’s disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals—16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD

Differential CpG methylation between the BN and SHR strains.

<p>A) Dendrogram showing the clustering the CpG methylation profiles obtained in four BN and four SHR rats. B) Projection of methylation profiles onto the first principal component. Numbers after the strain label denote biological replicates.</p

Comparison of differential methylation in parental strains and allele-specific methylation in F1 crosses.

<p>(A) Venn diagram showing the number of CpGs tested for differential methylation in the parental strains ONLY or for allele-specific methylation ONLY (grey areas) and the fraction of CpGs tested for BOTH (intersection, blue). The light blue area shows the number of CpGs significantly differentially methylated between the parentals AND/OR showing allele-specific methylation. Areas are not proportional. CpGs on the X chromosome and the mitochondrial chromosome have been excluded. (B) Overlap of differential methylation between the parental strains and allele specific methylation in the F1s. In total, 52,410 out of the 1,705,718 CpG dinucleotides analysed in both the parentals and the F1s (intersection, blue in (A)) showed significant differential methylation. The Venn diagram shows the fraction of CpGs that were differentially methylated between the parents ONLY (purple), between the alleles in the F1s ONLY (orange) or in BOTH (green). The theoretical distribution of frequencies assuming random overlap between the parental and the F1 data sets are shown in italics (<i>X²</i> = 135276.2, <i>P</i><4.9×10⁻³²⁴). The overlap of the two sets is >15 fold higher than expected by chance. (C) Frequency of CpGs with a SNP within 50 base pairs for CpGs showing differential methylation between the parentals only (purple), the F1 alleles only (orange) and both (green) compared to all CpGs analysed in the parentals and the F1s (blue) and the whole genome (red).</p

Hierarchical clustering and principal component analysis of CpG methylation profiles obtained in the F1 reciprocal crosses.

<p>Dendrograms showing the results of clustering the CpG methylation profiles obtained in each of the F1 animals before (A) and after (C) phasing the read data by parental genotype. The prefix of the phased F1 profiles in (C) denotes the reciprocal cross (bnxshr, shrxbn) and the suffix the genotype of the phased read set (bn = Brown Norway, shr = Spontaneously Hypertensive Rat). Numbers after the reciprocal cross prefix denote biological replicates. Profiles were clustered by the Ward's method using the pairwise euclidean distance between the profiles as the distance metric. Panels B and D show the projection of methylation profiles onto the 1^st principal component (PC). Replicates for each cross-genotype combination are separated along the y-axis. The prefix of the label denotes the reciprocal cross (bnxshr, shrxbn), the suffix denotes the parental genotype (bn, shr). While the 1^st PC does not separate crosses in the unphased data it provides complete separation by parental genotype in the phased data. Only CpGs with at least 5× coverage in each replicate/phased read set were included in the analysis and CpG positions affected by SNPs/indels were removed prior to clustering and principal component analysis.</p

Summary of differential methylation by source of variation.

<p>*Strand specific methylation.</p><p>Summary of differential methylation by source of variation.</p