CalPen (Calculator of Penetrance), a web-based tool to estimate penetrance in complex genetic disorders

Addepalli, Aditya; Kalyani, Sakhare; Singh, Minali; Bandyopadhyay, Debashree; Mohan, K. Naga

doi:10.1371/journal.pone.0228156

Cited by 6 publications

(4 citation statements)

References 31 publications

(57 reference statements)

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“…P values of ≤0.05 were taken as significant. For a given genotype that is significantly associated with SZ, penetrance estimates were made using CalPen, an online tool [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Functional Analysis of DNMT1 SNPs (rs2228611 and rs2114724) Associated with Schizophrenia

et al. 2021

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A recent study showed the association of minor alleles of rs2228611 (T allele) and rs2114724 (T allele) of DNMT1 with schizophrenia (SZ) and suggested their effects on splicing of the transcripts. We performed a replication study using 310 controls and 304 SZ patients and confirmed the association of the homozygous minor allele genotypes with SZ ( P = 0.04 for rs2114724 and P = 0.007 for rs2228611). This significant association persisted after Bonferroni correction when the previously published data of 301 controls and 325 patients were also considered ( P ≤ 0.0002). In addition, we found that the proportion of male patients with homozygous minor alleles at rs2114724 was significantly higher than that of females ( P = 0.002). When haplotype analysis of both loci was performed, we observed a significant association of T/T–T/T and T/T–C/T ( P = 0.04) haplotypes with SZ. To gain insights into the functional effects of the two SNPs on the levels of DNMT1 transcripts, quantitative real-time PCR experiments were performed using peripheral blood monocytes from 10 individuals each with T/T–T/T (homozygous minor allele), C/T–C/T (heterozygous), and C/C–C/C (homozygous major allele) haplotypes. Independently, the levels of DNMT1 protein were also compared in three individuals each by immunofluorescence. These results suggest that neither DNMT1 transcript nor the protein levels were significantly different in the peripheral blood monocytes among the individuals studied for the three groups. Taken together, our results confirm that the two minor alleles in homozygosity are associated with SZ but with no discernible effects on transcript or protein levels of DNMT1 in the peripheral blood monocytes of the small number of samples tested.

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“…P values of ≤0.05 were taken as significant. For a given genotype that is significantly associated with SZ, penetrance estimates were made using CalPen, an online tool [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Functional Analysis of DNMT1 SNPs (rs2228611 and rs2114724) Associated with Schizophrenia

et al. 2021

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“…The observed deletion did not include DGCR2, ESS2, TSSK2, GSC2, SLC25A1 , and CLTCL1 ( Figure 1C ). Deletions of this region are considered to have higher penetrance for SZ ( Addepalli et al, 2020 ). For example, the data given in Table 2 gave a penetrance value of 0.272 with critical intervals of 0.079 to 0.818.…”

Section: Resultsmentioning

confidence: 99%

“…The number of controls and patients containing 22q11.2 deletions and the total number of samples tested were obtained from the SZDB 2.0. Using an incidence of 0.7%, penetrance and its critical intervals were calculated using CalPen software ( Addepalli et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Genome-scale copy number variant analysis in schizophrenia patients and controls from South India

Singh,

Pradhan,

Kkani

et al. 2023

Front. Mol. Neurosci.

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Copy number variants (CNVs) are among the main genetic factors identified in schizophrenia (SZ) through genome-scale studies conducted mostly in Caucasian populations. However, to date, there have been no genome-scale CNV reports on patients from India. To address this shortcoming, we generated, for the first time, genome-scale CNV data for 168 SZ patients and 168 controls from South India. In total, 63 different CNVs were identified in 56 patients and 46 controls with a significantly higher proportion of medium-sized deletions (100 kb–1 Mb) after multiple testing (FDR = 2.7E-4) in patients. Of these, 13 CNVs were previously reported; however, when searched against GWAS, transcriptome, exome, and DNA methylation studies, another 17 CNVs with candidate genes were identified. Of the total 30 CNVs, 28 were present in 38 patients and 12 in 27 controls, indicating a significantly higher representation in the former (p = 1.87E-5). Only 4q35.1-q35.2 duplications were significant (p = 0.020) and observed in 11 controls and 2 patients. Among the others that are not significant, a few examples of patient-specific and previously reported CNVs include deletions of 11q14.1 (DLG2), 22q11.21, and 14q21.1 (LRFN5). 16p13.3 deletion (RBFOX1), 3p14.2 duplication (CADPS), and 7p11.2 duplication (CCT6A) were some of the novel CNVs containing candidate genes. However, these observations need to be replicated in a larger sample size. In conclusion, this report constitutes an important foundation for future CNV studies in a relatively unexplored population. In addition, the data indicate that there are advantages in using an integrated approach for better identification of candidate CNVs for SZ and other mental health disorders.

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“…Subsequently, we examined the literature and included only programs capable of handling variant data, such as VCF files, rsID, or location in the genome, and providing evidence or predictions of the variant impacts. Overall, this resulted in the identification of 186 additional VIPs, augmenting the VIPdb to a total of 403 VIPs (16, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 9 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233) (11,18,20,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,…”

Section: Methodsmentioning

confidence: 99%

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Lin,

Menon,

et al. 2024

Preprint

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Background: Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions: VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability: VIPdb version 2 is available at https://genomeinterpretation.org/vipdb/

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CalPen (Calculator of Penetrance), a web-based tool to estimate penetrance in complex genetic disorders

Cited by 6 publications

References 31 publications

Functional Analysis of DNMT1 SNPs (rs2228611 and rs2114724) Associated with Schizophrenia

Functional Analysis of DNMT1 SNPs (rs2228611 and rs2114724) Associated with Schizophrenia

Genome-scale copy number variant analysis in schizophrenia patients and controls from South India

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

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