Large-scale deletions of the ABCA1 gene in patients with hypoalphalipoproteinemia

Copy-number variations (CNVs) have been studied in the context of familial hypercholesterolemia but have not yet been evaluated in patients with extreme levels of HDL cholesterol. We evaluated targeted, next-generation sequencing data from patients with very low levels of HDL cholesterol (i.e., hypoalphalipoproteinemia) with the VarSeq-CNV® caller algorithm to screen for CNVs that disrupted the ABCA1, LCAT, or APOA1 genes. In four individuals, we found three unique deletions in ABCA1: a heterozygous deletion of exon 4, a heterozygous deletion that spanned exons 8 to 31, and a heterozygous deletion of the entire ABCA1 gene. Breakpoints were identified with Sanger sequencing, and the full-gene deletion was confirmed by using exome sequencing and the Affymetrix CytoScan HD array. Previously, large-scale deletions in candidate HDL genes had not been associated with hypoalphalipoproteinemia; our findings indicate that CNVs in ABCA1 may be a previously unappreciated genetic determinant of low levels of HDL cholesterol. By coupling bioinformatic analyses with next-generation sequencing data, we can successfully assess the spectrum of genetic determinants of many dyslipidemias, including hypoalphalipoproteinemia. Copy-number variations (CNVs) have been studied in the context of familial hypercholesterolemia but have not yet been evaluated in patients with extreme levels of HDL cholesterol. We evaluated targeted, next-generation sequencing data from patients with very low levels of HDL cholesterol (i.e., hypoalphalipoproteinemia) with the VarSeq-CNV® caller algorithm to screen for CNVs that disrupted the ABCA1, LCAT, or APOA1 genes. In four individuals, we found three unique deletions in ABCA1: a heterozygous deletion of exon 4, a heterozygous deletion that spanned exons 8 to 31, and a heterozygous deletion of the entire ABCA1 gene. Breakpoints were identified with Sanger sequencing, and the full-gene deletion was confirmed by using exome sequencing and the Affymetrix CytoScan HD array. Previously, large-scale deletions in candidate HDL genes had not been associated with hypoalphalipoproteinemia; our findings indicate that CNVs in ABCA1 may be a previously unappreciated genetic determinant of low levels of HDL cholesterol. By coupling bioinformatic analyses with next-generation sequencing data, we can successfully assess the spectrum of genetic determinants of many dyslipidemias, including hypoalphalipoproteinemia. Extremely low levels of HDL cholesterol, clinically characterized as “hypoalphalipoproteinemia”, can result from various molecular etiologies. DNA sequencing of candidate genes has shown that between ∼10–35% of affected individuals have rare heterozygous missense, nonsense, or splicing variants in ABCA1, APOA1, and LCAT genes, encoding ABCA1, apo A-I, and lecithin:cholesterol acyl transferase, respectively (1.Dron J.S. Wang J. Low-Kam C. Khetarpal S.A. Robinson J.F. McIntyre A.D. Ban M.R. Cao H. Rhainds D. Dube M.P. et al.Polygenic determinants in extremes of high-density lipoprotein cholesterol.J. Lipid Res. 2017; 58: 2162-2170Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar, 2.Cohen J.C. Kiss R.S. Pertsemlidis A. Marcel Y.L. McPherson R. Hobbs H.H. Multiple rare alleles contribute to low plasma levels of HDL cholesterol.Science. 2004; 305: 869-872Crossref PubMed Scopus (901) Google Scholar, 3.Kiss R.S. Kavaslar N. Okuhira K. Freeman M.W. Walter S. Milne R.W. McPherson R. Marcel Y.L. Genetic etiology of isolated low HDL syndrome: Incidence and heterogeneity of efflux defects.Arterioscler. Thromb. Vasc. 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Singaraja R.R. et al.Targeted next-generation sequencing to diagnose disorders of HDL cholesterol.J. Lipid Res. 2015; 56: 1993-2001Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar). We recently found that another ∼18% of affected individuals have an extreme polygenic accumulation of common variants, as quantified by a polygenic trait score that considers several common SNPs associated with HDL cholesterol levels (1.Dron J.S. Wang J. Low-Kam C. Khetarpal S.A. Robinson J.F. McIntyre A.D. Ban M.R. Cao H. Rhainds D. Dube M.P. et al.Polygenic determinants in extremes of high-density lipoprotein cholesterol.J. Lipid Res. 2017; 58: 2162-2170Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar). However, the genetic basis of low HDL cholesterol in the majority of individuals with hypoalphalipoproteinemia remains to be characterized. Copy-number variations (CNVs) are deletions and duplications of genomic material that are much larger than single nucleotide variations (SNVs); by convention, “CNVs” are deletions or duplications >50 bp in size (7.Zarrei M. MacDonald J.R. Merico D. Scherer S.W. A copy number variation map of the human genome.Nat. Rev. Genet. 2015; 16: 172-183Crossref PubMed Scopus (492) Google Scholar). While CNVs have been commonly identified throughout the genome, there has been a surging focus on CNVs that are rare within the population, and their relationship to certain phenotypes and diseases (8.Iacocca M.A. Hegele R.A. Role of DNA copy number variation in dyslipidemias.Curr. Opin. Lipidol. 2018; 29: 125-132Crossref PubMed Scopus (26) Google Scholar). This redefined focus has been due to improvements in bioinformatic tools and targeted next-generation sequencing (NGS) panels designed for clinical utility. Previously, specialized molecular methods, such as multiplex ligation-dependent probe amplification (MLPA), have been required to detect CNVs, and had to be performed concurrently with other genetic methods. Now, through the development of new bioinformatic methods, CNVs can be easily screened for in patient groups using data generated by a single genetic approach; namely, NGS. We recently reported that data generated by a targeted NGS panel designed to detect SNVs in genes related to familial hypercholesterolemia (FH) could be processed with dedicated bioinformatic tools to diagnose the presence of CNVs in LDLR, encoding the LDL receptor. Results of our NGS-based CNV detection method showed 100% concordance with traditional MLPA of LDLR, with no false negative or false positive results (9.Iacocca M.A. Wang J. Dron J.S. Robinson J.F. McIntyre A.D. Cao H. Hegele R.A. Use of next-generation sequencing to detect LDLR gene copy number variation in familial hypercholesterolemia.J. Lipid Res. 2017; 58: 2202-2209Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). CNVs disrupting ABCA1, APOA1, or LCAT in individuals with hypoalphalipoproteinemia have not yet been reported. Here, we applied our novel bioinformatic approach to previously generated targeted NGS data from patients with hypoalphalipoproteinemia, with particular interest in patients without rare variants in HDL-associated genes or without an extreme polygenic accumulation of common variants (10.Dron J.S. Hegele R.A. Polygenic influences on dyslipidemias.Curr. Opin. Lipidol. 2018; 29: 133-143Crossref PubMed Scopus (42) Google Scholar). Out of 288 patients screened, we found four patients who had one of three novel heterozygous CNVs within the ABCA1 gene; the variants were confirmed using independent methods. Our findings not only demonstrate the usefulness of applying bioinformatically-based CNV calling algorithms to NGS data, but we also provide the first example of large-scale CNV deletions that are likely causing hypoalphalipoproteinemia. Patients who were referred to the Lipid Genetics Clinic at the London Health Sciences Centre, University Hospital (London ON, Canada) for “low HDL cholesterol” or “hypoalphalipoproteinemia” were considered for this screening study. Patients provided signed consent with approval from the Western University ethics review board (no. 07290E). Genomic DNA isolation, sample preparation, and targeted sequencing using our “LipidSeq” panel have been described in detail previously (1.Dron J.S. Wang J. Low-Kam C. Khetarpal S.A. Robinson J.F. McIntyre A.D. Ban M.R. Cao H. Rhainds D. Dube M.P. et al.Polygenic determinants in extremes of high-density lipoprotein cholesterol.J. Lipid Res. 2017; 58: 2162-2170Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar, 11.Johansen C.T. Dube J.B. Loyzer M.N. MacDonald A. Carter D.E. McIntyre A.D. Cao H. Wang J. Robinson J.F. Hegele R.A. Lipidseq: a next-generation clinical resequencing panel for monogenic dyslipidemias.J. Lipid Res. 2014; 55: 765-772Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar). DNA sequence data in the form of FASTQ files were imported into CLC Bio Genomics Workbench (version 8.5; CLC Bio, Aarhus, Denmark) for bioinformatic processing. The sequencing data was aligned to the human reference genome (build hg19), depth of coverage was exported as a BAM file, and any identified variants were exported to VCF files for each patient. The BAM and VCF files generated for each patient were imported into VarSeq® (version 1.4.8; Golden Helix, Inc., Bozeman, MT) for annotation of each genetic variant. SNVs were identified following methods that have been described in detail previously (1.Dron J.S. Wang J. Low-Kam C. Khetarpal S.A. Robinson J.F. McIntyre A.D. Ban M.R. Cao H. Rhainds D. Dube M.P. et al.Polygenic determinants in extremes of high-density lipoprotein cholesterol.J. Lipid Res. 2017; 58: 2162-2170Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar). Assessment of CNVs in ABCA1, APOA1, and LCAT was performed using the VarSeq-CNV® caller algorithm. To identify CNVs, the algorithm uses depth-of-coverage information contained within each patient BAM file and compares it to coverage information from a set of “reference” samples that have been previously confirmed to not carry any CNVs. CNVs were called based on comparative increases in read-depth, indicating a duplication of genetic material, and comparative decreases in read-depth, indicating a deletion of genetic material. The criteria in which CNVs were called has been described in detail previously (9.Iacocca M.A. Wang J. Dron J.S. Robinson J.F. McIntyre A.D. Cao H. Hegele R.A. Use of next-generation sequencing to detect LDLR gene copy number variation in familial hypercholesterolemia.J. Lipid Res. 2017; 58: 2202-2209Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). To identify the presence of partial gene deletions, primers were designed to flank regions surrounding the putative deletions and were used for PCR amplification (Expand 20 kbplus PCR System; Roche, Mannheim, Germany, catalog no. 11811002001). Forward (F) and reverse (R) primers flanking the deletion junctions were: F1 5′-AGCACGATAGGAAGCATCTTC-3′ and R1 5′-ATCACTGTCTGTGGCAACCAG-3′ (exon 4 deletion); F2 5′- GACCCAGCTTCCAATCTTCATAA-3′ and R2 5′- TAGACAGAATCAGGCCATAATCTG-3′ (exons 8-31 deletion). Gel elec­trophoresis of the PCR products was used as a visual confirmation of the mutant alleles. Sanger sequencing and primer-walking of the PCR products were performed to identify the deletion breakpoints. Once deletion breakpoints were identified, screening primers spanning the upstream or downstream breakpoint were designed for PCR and Sanger sequencing (supplemental Table S1) to confirm the deletion breakpoint sequences. Patients with expected full-gene deletions had their DNA samples indexed and pooled using the TruSeq Rapid Exome Kit (Illumina, San Diego, CA; catalog no. 20020616) in preparation for exome sequencing. Sequencing was then performed at the London Regional Genomics Centre (www.lrgc.on.ca; London, ON, Canada), using a NextSeq 500 (Illumina). The same bioinformatic approach described above was used to replicate the CNV call made by the VarSeq-CNV® caller algorithm. Patients with expected full-gene deletions had their DNA samples assessed with the Affymetrix CytoScanTM HD Array (Thermo Fisher Scientific, Waltham, MA) for the genomic region containing the CNV. With >2 million probes on the array, deletions >25 kb can be detected. The microarray was performed following the manufacturer's instructions at Victoria Hospital (London, ON, Canada), and the resultant data were analyzed using the Chromosome Analysis Suite (ChAS) (version 3.2; Thermo Fisher Scientific). The regions between adjacent probes that differed in copy-number state were marked as containing the approximate breakpoints of the CNV and were used to gauge the approximate size of the deletion. Once the magnitude of the deletion was established, the approximate location of each breakpoint was estimated. Primers flanking the deletion junction were: F3 5′- CCTGGCTGCTTCTAAGAGCCTATGATC-3′ and R3 5′- TGTCTCTACATGGTCCTCCTTCTGTGC-3′, and were used for PCR amplification (Expand 20 kbplus PCR System, Roche; cat. no. 11811002001). Gel electrophoresis of the PCR products was used as a visual confirmation of the mutant allele. Sanger sequencing and primer-walking of the PCR product were performed to identify the deletion breakpoints. Once deletion breakpoints were identified, screening primers spanning the upstream or downstream breakpoint were designed for PCR and Sanger sequencing (supplemental Table S1) to confirm the deletion breakpoint sequences. A total of 288 patients with “low HDL cholesterol” or “hypoalphalipoproteinemia” were sequenced with LipidSeq and screened for CNVs disrupting ABCA1, APOA1, and LCAT. Clinical and biochemical characteristics of the four patients identified as carriers for CNVs are shown in Table 1.TABLE 1Clinical and demographic features of subjects with ABCA1 copy-number variationsPatient 1Patient 2Patient 3Patient 4Age37345940GenderFemaleFemaleMaleFemaleWeight (kg)56.572—71.4Height (cm)155156.9—172Waist circumference (cm)73.781.5—66EthnicityNorthern European, ItalianFrench-CanadianFrench-CanadianGermanTotal cholesterol (mmol/L)8.163.305.464.71Triglyceride (mmol/L)2.521.014.485.13HDL cholesterol (mmol/L)0.810.560.470.03LDL cholesterol (mmol/L)6.202.423.283.54apo A1 (g/L)—0.590.600.09apo B (g/L)—0.811.33—Creatine kinase (U/L)79—78113Fasting glucose (mmol/L)4.05.45.34.7Aspartate transaminase (U/L)23202133Alanine transaminase (U/L)—151523Alkaline phosphatase (U/L)4662—61Lp(a) (nmol/L)——290363Co-morbidities• heterozygous FH (LDLR NM_000527 p.V523M)• obesity• hypertension• TIA at age 37• minor carotid intimal thickening• TIA• cerebral arteriosclerotic microangiopathy• smoking• hypertension• aortic valvular• juvenile myoclonic epilepsy• stenosis• diffuse nonHodgkin's lymphoma stage IIIValues provided are from first presentation to specialist lipid clinic, or date first obtained. Lp(a) conversions from g/l to nmol/l were done following the conversion factor described by Brown et al. (41.Brown W.V. Ballantyne C.M. Jones P.H. Marcovina S. Management of lp(a).J. Clin. Lipidol. 2010; 4: 240-247Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar). FH, familial hypercholesterolemia; Lp(a), lipoprotein(a); TIA, transient ischemic attack. Open table in a new tab Values provided are from first presentation to specialist lipid clinic, or date first obtained. Lp(a) conversions from g/l to nmol/l were done following the conversion factor described by Brown et al. (41.Brown W.V. Ballantyne C.M. Jones P.H. Marcovina S. Management of lp(a).J. Clin. Lipidol. 2010; 4: 240-247Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar). FH, familial hypercholesterolemia; Lp(a), lipoprotein(a); TIA, transient ischemic attack. Analysis of LipidSeq with the VarSeq-CNV® caller algorithm identified four hypoalphalipoproteinemia patients as carriers of large-scale deletions in ABCA1 (supplemental had a heterozygous deletion spanning exon and a of had a heterozygous deletion spanning exons 8 to and 4 had a heterozygous deletion spanning the entire ABCA1 gene. of patients rare SNVs in ABCA1, APOA1, or LCAT. were no CNVs in APOA1 or LCAT for any patients in this study. To the size of the deletion in 4, the VarSeq-CNV® caller algorithm for exome data was used to confirm the heterozygous of ABCA1 (supplemental the CytoScanTM confirmed and the heterozygous of this CNV Exome sequencing and that the CNV was in and genes, including and Sanger sequencing the CNV breakpoints in Patients and and 4 the genomic in the deletion and to the size of each CNV primers spanning breakpoints were used to between and as in and and breakpoints of ABCA1 copy-number to 8 to to to are in the with genomic based on the human genome reference copy-number Open table in a new tab The are in the with genomic based on the human genome reference copy-number In 288 patients with hypoalphalipoproteinemia, we identified three large-scale deletions in ABCA1 in four individuals by applying specialized bioinformatic tools to NGS While it not the first CNVs have been in ABCA1 C. T. S. S. et number variation in 2014; PubMed Scopus Google Scholar, G.M. N. T. N. C. 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Lipidol. 2018; 29: 125-132Crossref PubMed Scopus (26) Google improvements to bioinformatic tools have of NGS data, to of of genetic tools likely of the genetic basis for other and their low in our patient we that large-scale CNVs, deletions or likely be patients with dyslipidemias, but to be in to rare genetic variants and polygenic The to and the patients in this study. with files copy-number variation familial hypercholesterolemia multiplex ligation-dependent probe amplification next-generation sequencing single nucleotide variation

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