FULL-LENGTH ORIGINAL RESEARCH
Copy number variations and susceptibility to lateral temporal epilepsy: A study of 21 pedigrees *Manuela Fanciulli, †Elena Pasini, ‡Sandro Malacrida, §Pasquale Striano, ¶Salvatore Striano, †Roberto Michelucci, #**Ruth Ottman, and ††Carlo Nobile Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
SUMMARY
Dr. Manuela Fanciulli, works in Porto Conte Ricerche Department of Neurosciences, Alghero (Sardinia), Italy.
Objective: Autosomal dominant lateral temporal epilepsy (ADLTE) is a focal epileptic syndrome characterized by auditory or aphasic auras. Mutations in the LGI1 gene account for 50% of ADLTE families do not have LGI1 mutations.8 During the past several years, significant progress has been made in designing arrays to detect CNVs. High-density single nucleotide polymorphism (SNP) arrays that include nonpolymorphic probes optimized for copy number measurements can detect CNVs as small as 10 kb.11 By using these high-density SNP platforms, we recently identified the first LGI1 microdeletion associated with ADLTE.12 In this study, we performed a genome-wide CNV analysis of 21 ADLTE families without LGI1 mutations to identify CNVs associated with susceptibility to ADLTE.
Methods Family selection The epilepsy phenotypes of affected members of 21 families with at least two patients with lateral temporal epilepsy were reassessed by R. M. and R. O. to confirm eligibility for the study based on clinical features and intrafamilial clinical homogeneity. Each family contained two or more affected members, with a history of focal epilepsy with auras characteristic of the syndrome, with auditory (ringing, humming, sounds, voices, or music) or receptive aphasic symptoms, and absence of identified structural or metabolic insults to the CNS. Each included family had previously been screened for sequence-based mutations in LGI1 and found to be negative. Written informed consent was obtained from all family members participating in the study. SNP-array genotyping and CNV analysis DNA suitable for SNP-array analysis was genotyped using the HumanOmni1-Quad v1.0 beadchip (Illumina, San Diego, CA, U.S.A.), which includes 1,140,419 SNP markers, on Illumina Iscan System according to the manufacturer’s instructions. The raw genotyping signal data were processed with the ILLUMINA GENOME STUDIO software and converted to signal intensity values, represented as Log R Ratio (LRR) and B Allele Frequency (BAF). The PennCNV algorithm13 was used to infer CNVs from signal intensity data. This algorithm takes into consideration the total signal intensity and allelic intensity ratio at each SNP Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
marker, the distance between neighboring SNPs, and the allele frequency of each SNP through a Hidden Markov Model. The gcmodel file was included in the analysis to correct for genomic waves in signal intensities due to GC-rich regions. The QC criteria recommended for PennCNV were used: standard deviation for autosomal log R ratio >0.28, a median B allele frequency of >0.55 or 0.002.13 Copy number calls were required to span at least 10 probes, a method that has been shown to control false-positive calls at a rate 10 kb. Occurrence of identical or overlapping CNVs in multiple members of one or more families was interpreted as confirmation of their existence at unique regions. CNV mapping was performed according to National Center for Biotechnology Information (NCBI) build 36. Using individual CNV calls, we defined CNV regions (CNVRs) as regions of overlap of CNVs occurring in multiple individuals from one or more families. When overlapping CNVs in the same region had different lengths, the breakpoints of each CNVR were defined by the largest region of overlap. Real time quantitative PCR Real time quantitative polymerase chain reaction (qPCR) was performed to validate occurrence of selected CNVs that affected genes possibly related to ADLTE or that had small sizes, to confirm accuracy of the CNV detection method. qPCR was performed in the 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, U.S.A.). Individual qPCR reactions were carried out in 20-ll volumes with 19 Sybr Fast qPCR Master Mix (29) (Kapa Biosystems, Wilmington, MA, U.S.A.), 200 nM for each primer, and 2.5 ng of DNA. PCR conditions were 3 min at 95°C followed by 40 cycles of 3 s at 95°C, 1 min at 60°C, and 1 min at 72°C. Standard curves were created by serial twofold dilution from 5 to 0.156 ng of a single genomic DNA sample, and run in triplicate. Samples were run in duplicate. Forkhead Box P2 (FOXP2) was used as the control gene and normalization against FOXP2 was done to give an estimation of CNVs. Selection of CNVRs possibly related to ADLTE To identify potentially ADLTE-related CNVRs, we selected CNVs common to all the affected members and obligate carriers of at least one family and classified them according to their frequencies in the Database of Genomic Variants (DGV; http://dgv.tcag.ca/dgv/app/home). For simplicity, all CNVs found in the DGV in a given region completely or partially overlapping with those found in this study were taken into account to estimate the frequency of that CNVR. Frequency estimates for CNVRs were obtained from studies of large (N > 400) groups of healthy controls as reported in DGV, which utilized high-density SNP platforms such as Illumina HumanHap610-Quad, Human-
1653 CNVs and Lateral Temporal Epilepsy Hap550, HumanHap1M-Duo, or Affimetrics HumanSNP6.0 beadchips, or comparative genome hybridization (CGH) array. CNVRs with frequencies 1% were considered polymorphic. All these CNVRs were inspected manually to confirm the positions of breakpoints and follow their segregation within families. Information on the identities, expression patterns, and annotations of protein coding genes lying within or close to CNVRs possibly related to ADLTE was obtained from the UCSC genome browser (http://genome-euro.ucsc.edu/), OMIM (http://www.omim.org/), and Genecards (http://www.genecards.org/) databases. Statistical tests The Modified Quasi-Likelihood Score (MQLS) test15 was used for case–control analysis of associations between CNVs and disease within our pedigrees. The MQLS test incorporates kinship coefficients to correct for correlated genotypes of all the pedigree relationships.
Results CNV overview The pedigrees of most of the included families have already been published.8 The total number of affected members, including 10 deceased, was 76 (30 male), 54 of whom (71%) had auditory/aphasic symptoms. We genotyped 176
DNAs (62 from living patients) using a SNP-array beadchip containing 1 million markers and analyzed the genotypes with the PENNCNV program.13 Occurrence of overlapping CNVs in multiple members of the same family or in multiple families at unique CNVRs reduced the risk of false positives. We confirmed occurrence of microdeletions or microduplications at eight CNVRs by real time qPCR (Fig. 1). In total, we identified 11,214 CNVs (6,667 deletions and 4,547 duplications) corresponding to 1,890 unique CNVRs, with an average size of 67.3 kb (Table 1). Nearly two thirds of all CNVs were 500 kb; only 10 CNVs were >1 Mb, nearly all of which were located in gene-free pericentromeric or subtelomeric regions. About 38% of the CNVs detected in our families were absent in the DGV database. This is not surprising given that the microchip we utilized allowed detection of CNVs as small as 10 kb, a resolution much higher than that of most arrays employed in previous studies. In some CNVRs, deletions and duplications showed variable sizes. More than half of these variable CNVRs were found in pericentromeric or subtelomeric regions. Rare CNVRs segregating with ADLTE To identify microdeletions and microduplications possibly related to ADLTE, we first focused on rare CNVRs that were found in all affected members and obligate carriers of
A
B
C
D
E
F
G
H
Figure 1. Validation of CNVs using qPCR. Validation results for eight CNVs detected by PENNCNV tool. Each panel shows a different CNV, identified by chromosome coordinates and the gene where a qPCR amplicon was located: (A) 1q21.1 (144337336–144474316; CD160); (B) 2p16.3 (51373143–51393241; near NRXN1); (C) 16p13.3-p13.2 (6241119–6332827; RBFOX1); (D) 3q26.31 (174722147–174772718; NLGN1); (E) 5q31.3(140202825–140218878; PCDHA9); (F) 8q21.2 (85420095–85431506; RALYL); (G) 12p13.31 (7868618–8017012; SLC2A3); and (H) 17q12 (31541945–31582324; CCL3L1). Chromosome positions refer to NCBI build 36. Del, deletion; Dup, duplication; Controls, two-copy control sample. Epilepsia ILAE Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
1654 M. Fanciulli et al. Table 1. Summary statistics of CNV loci Summary statistics of CNV analysis
Total
Number of CNV loci Number of unique CNV regions Maximum CNV length (bp) Minimum CNV length (bp) Average length per locus (kb) Average number of markers per locus CNVs encompassing genesa CNVs encompassing exons Size distribution ≥10 to
Copy number variations and susceptibility to lateral temporal epilepsy: A study of 21 pedigrees *Manuela Fanciulli, †Elena Pasini, ‡Sandro Malacrida, §Pasquale Striano, ¶Salvatore Striano, †Roberto Michelucci, #**Ruth Ottman, and ††Carlo Nobile Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
SUMMARY
Dr. Manuela Fanciulli, works in Porto Conte Ricerche Department of Neurosciences, Alghero (Sardinia), Italy.
Objective: Autosomal dominant lateral temporal epilepsy (ADLTE) is a focal epileptic syndrome characterized by auditory or aphasic auras. Mutations in the LGI1 gene account for 50% of ADLTE families do not have LGI1 mutations.8 During the past several years, significant progress has been made in designing arrays to detect CNVs. High-density single nucleotide polymorphism (SNP) arrays that include nonpolymorphic probes optimized for copy number measurements can detect CNVs as small as 10 kb.11 By using these high-density SNP platforms, we recently identified the first LGI1 microdeletion associated with ADLTE.12 In this study, we performed a genome-wide CNV analysis of 21 ADLTE families without LGI1 mutations to identify CNVs associated with susceptibility to ADLTE.
Methods Family selection The epilepsy phenotypes of affected members of 21 families with at least two patients with lateral temporal epilepsy were reassessed by R. M. and R. O. to confirm eligibility for the study based on clinical features and intrafamilial clinical homogeneity. Each family contained two or more affected members, with a history of focal epilepsy with auras characteristic of the syndrome, with auditory (ringing, humming, sounds, voices, or music) or receptive aphasic symptoms, and absence of identified structural or metabolic insults to the CNS. Each included family had previously been screened for sequence-based mutations in LGI1 and found to be negative. Written informed consent was obtained from all family members participating in the study. SNP-array genotyping and CNV analysis DNA suitable for SNP-array analysis was genotyped using the HumanOmni1-Quad v1.0 beadchip (Illumina, San Diego, CA, U.S.A.), which includes 1,140,419 SNP markers, on Illumina Iscan System according to the manufacturer’s instructions. The raw genotyping signal data were processed with the ILLUMINA GENOME STUDIO software and converted to signal intensity values, represented as Log R Ratio (LRR) and B Allele Frequency (BAF). The PennCNV algorithm13 was used to infer CNVs from signal intensity data. This algorithm takes into consideration the total signal intensity and allelic intensity ratio at each SNP Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
marker, the distance between neighboring SNPs, and the allele frequency of each SNP through a Hidden Markov Model. The gcmodel file was included in the analysis to correct for genomic waves in signal intensities due to GC-rich regions. The QC criteria recommended for PennCNV were used: standard deviation for autosomal log R ratio >0.28, a median B allele frequency of >0.55 or 0.002.13 Copy number calls were required to span at least 10 probes, a method that has been shown to control false-positive calls at a rate 10 kb. Occurrence of identical or overlapping CNVs in multiple members of one or more families was interpreted as confirmation of their existence at unique regions. CNV mapping was performed according to National Center for Biotechnology Information (NCBI) build 36. Using individual CNV calls, we defined CNV regions (CNVRs) as regions of overlap of CNVs occurring in multiple individuals from one or more families. When overlapping CNVs in the same region had different lengths, the breakpoints of each CNVR were defined by the largest region of overlap. Real time quantitative PCR Real time quantitative polymerase chain reaction (qPCR) was performed to validate occurrence of selected CNVs that affected genes possibly related to ADLTE or that had small sizes, to confirm accuracy of the CNV detection method. qPCR was performed in the 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, U.S.A.). Individual qPCR reactions were carried out in 20-ll volumes with 19 Sybr Fast qPCR Master Mix (29) (Kapa Biosystems, Wilmington, MA, U.S.A.), 200 nM for each primer, and 2.5 ng of DNA. PCR conditions were 3 min at 95°C followed by 40 cycles of 3 s at 95°C, 1 min at 60°C, and 1 min at 72°C. Standard curves were created by serial twofold dilution from 5 to 0.156 ng of a single genomic DNA sample, and run in triplicate. Samples were run in duplicate. Forkhead Box P2 (FOXP2) was used as the control gene and normalization against FOXP2 was done to give an estimation of CNVs. Selection of CNVRs possibly related to ADLTE To identify potentially ADLTE-related CNVRs, we selected CNVs common to all the affected members and obligate carriers of at least one family and classified them according to their frequencies in the Database of Genomic Variants (DGV; http://dgv.tcag.ca/dgv/app/home). For simplicity, all CNVs found in the DGV in a given region completely or partially overlapping with those found in this study were taken into account to estimate the frequency of that CNVR. Frequency estimates for CNVRs were obtained from studies of large (N > 400) groups of healthy controls as reported in DGV, which utilized high-density SNP platforms such as Illumina HumanHap610-Quad, Human-
1653 CNVs and Lateral Temporal Epilepsy Hap550, HumanHap1M-Duo, or Affimetrics HumanSNP6.0 beadchips, or comparative genome hybridization (CGH) array. CNVRs with frequencies 1% were considered polymorphic. All these CNVRs were inspected manually to confirm the positions of breakpoints and follow their segregation within families. Information on the identities, expression patterns, and annotations of protein coding genes lying within or close to CNVRs possibly related to ADLTE was obtained from the UCSC genome browser (http://genome-euro.ucsc.edu/), OMIM (http://www.omim.org/), and Genecards (http://www.genecards.org/) databases. Statistical tests The Modified Quasi-Likelihood Score (MQLS) test15 was used for case–control analysis of associations between CNVs and disease within our pedigrees. The MQLS test incorporates kinship coefficients to correct for correlated genotypes of all the pedigree relationships.
Results CNV overview The pedigrees of most of the included families have already been published.8 The total number of affected members, including 10 deceased, was 76 (30 male), 54 of whom (71%) had auditory/aphasic symptoms. We genotyped 176
DNAs (62 from living patients) using a SNP-array beadchip containing 1 million markers and analyzed the genotypes with the PENNCNV program.13 Occurrence of overlapping CNVs in multiple members of the same family or in multiple families at unique CNVRs reduced the risk of false positives. We confirmed occurrence of microdeletions or microduplications at eight CNVRs by real time qPCR (Fig. 1). In total, we identified 11,214 CNVs (6,667 deletions and 4,547 duplications) corresponding to 1,890 unique CNVRs, with an average size of 67.3 kb (Table 1). Nearly two thirds of all CNVs were 500 kb; only 10 CNVs were >1 Mb, nearly all of which were located in gene-free pericentromeric or subtelomeric regions. About 38% of the CNVs detected in our families were absent in the DGV database. This is not surprising given that the microchip we utilized allowed detection of CNVs as small as 10 kb, a resolution much higher than that of most arrays employed in previous studies. In some CNVRs, deletions and duplications showed variable sizes. More than half of these variable CNVRs were found in pericentromeric or subtelomeric regions. Rare CNVRs segregating with ADLTE To identify microdeletions and microduplications possibly related to ADLTE, we first focused on rare CNVRs that were found in all affected members and obligate carriers of
A
B
C
D
E
F
G
H
Figure 1. Validation of CNVs using qPCR. Validation results for eight CNVs detected by PENNCNV tool. Each panel shows a different CNV, identified by chromosome coordinates and the gene where a qPCR amplicon was located: (A) 1q21.1 (144337336–144474316; CD160); (B) 2p16.3 (51373143–51393241; near NRXN1); (C) 16p13.3-p13.2 (6241119–6332827; RBFOX1); (D) 3q26.31 (174722147–174772718; NLGN1); (E) 5q31.3(140202825–140218878; PCDHA9); (F) 8q21.2 (85420095–85431506; RALYL); (G) 12p13.31 (7868618–8017012; SLC2A3); and (H) 17q12 (31541945–31582324; CCL3L1). Chromosome positions refer to NCBI build 36. Del, deletion; Dup, duplication; Controls, two-copy control sample. Epilepsia ILAE Epilepsia, 55(10):1651–1658, 2014 doi: 10.1111/epi.12767
1654 M. Fanciulli et al. Table 1. Summary statistics of CNV loci Summary statistics of CNV analysis
Total
Number of CNV loci Number of unique CNV regions Maximum CNV length (bp) Minimum CNV length (bp) Average length per locus (kb) Average number of markers per locus CNVs encompassing genesa CNVs encompassing exons Size distribution ≥10 to
