Dataset Information

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

ABSTRACT: Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

SUBMITTER: Brucker A

PROVIDER: S-EPMC7224564 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

Brucker Amanda A Lu Wenbin W Marceau West Rachel R Yu Qi-You QY Hsiao Chuhsing Kate CK Hsiao Tzu-Hung TH Lin Ching-Heng CH Magnusson Patrik K E PKE Sullivan Patrick F PF Szatkiewicz Jin P JP Lu Tzu-Pin TP Tzeng Jung-Ying JY

PLoS computational biology 20200504 5

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel assoc ...[more]

PMID: 32365089

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Project description:Endometriosis is a complex gynecological condition that affects 6-10% of women in their reproductive years and is defined by the presence of endometrial glands and stroma outside the uterus. Twin, family, and genome-wide association (GWA) studies have confirmed a genetic role, yet only a small part of the genetic risk can be explained by SNP variation. Copy number variants (CNVs) account for a greater portion of human genetic variation than SNPs and include more recent mutations of large effect. CNVs, likely to be prominent in conditions with decreased reproductive fitness, have not previously been examined as a genetic contributor to endometriosis. Here we employ a high-density genotyping microarray in a genome-wide survey of CNVs in a case-control population that includes 2,126 surgically confirmed endometriosis cases and 17,974 population controls of European ancestry. We apply stringent quality filters to reduce the false positive rate common to many CNV-detection algorithms from 77.7% to 7.3% without noticeable reduction in the true positive rate. We detected no differences in the CNV landscape between cases and controls on the global level which showed an average of 1.92 CNVs per individual with an average size of 142.3 kb. On the local level we identify 22 CNV-regions at the nominal significance threshold (P<0.05), which is greater than the 8.15 CNV-regions expected based on permutation analysis (P<0.001). Three CNV's passed a genome-wide P-value threshold of 9.3 × 10(-4); a deletion at SGCZ on 8p22 (P = 7.3 × 10(-4), OR = 8.5, Cl = 2.3-31.7), a deletion in MALRD1 on 10p12.31 (P = 5.6 × 10(-4), OR = 14.1, Cl = 2.7-90.9), and a deletion at 11q14.1 (P = 5.7 × 10(-4), OR = 33.8, Cl = 3.3-1651). Two SNPs within the 22 CNVRs show significant genotypic association with endometriosis after adjusting for multiple testing; rs758316 in DPP6 on 7q36.2 (P = 0.0045) and rs4837864 in ASTN2 on 9q33.1 (P = 0.0002). Together, the CNV-loci are detected in 6.9% of affected women compared to 2.1% in the general population.

Project description:BACKGROUND:1q21.1 Copy Number Variant (CNV) is associated with a highly variable phenotype ranging from congenital anomalies, learning deficits/intellectual disability (ID), to a normal phenotype. Hence, the clinical significance of this CNV can be difficult to evaluate. Here we described the consequences of the 1q21.1 CNV on genome-wide gene expression and function of selected candidate genes within 1q21.1 using cell lines from clinically well described subjects. METHODS AND RESULTS:Eight subjects from 3 families were included in the study: six with a 1q21.1 deletion and two with a 1q21.1 duplication. High resolution Affymetrix 2.7M array was used to refine the 1q21.1 CNV breakpoints and exclude the presence of secondary CNVs of pathogenic relevance. Whole genome expression profiling, studied in lymphoblast cell lines (LBCs) from 5 subjects, showed enrichment of genes from 1q21.1 in the top 100 genes ranked based on correlation of expression with 1q21.1 copy number. The function of two top genes from 1q21.1, CHD1L/ALC1 and PRKAB2, was studied in detail in LBCs from a deletion and a duplication carrier. CHD1L/ALC1 is an enzyme with a role in chromatin modification and DNA damage response while PRKAB2 is a member of the AMP kinase complex, which senses and maintains systemic and cellular energy balance. The protein levels for CHD1L/ALC1 and PRKAB2 were changed in concordance with their copy number in both LBCs. A defect in chromatin remodeling was documented based on impaired decatenation (chromatid untangling) checkpoint (DCC) in both LBCs. This defect, reproduced by CHD1L/ALC1 siRNA, identifies a new role of CHD1L/ALC1 in DCC. Both LBCs also showed elevated levels of micronuclei following treatment with a Topoisomerase II inhibitor suggesting increased DNA breaks. AMP kinase function, specifically in the deletion containing LBCs, was attenuated. CONCLUSION:Our studies are unique as they show for the first time that the 1q21.1 CNV not only causes changes in the expression of its key integral genes, associated with changes at the protein level, but also results in changes in their known function, in the case of AMPK, and newly identified function such as DCC activation in the case of CHD1L/ALC1. Our results support the use of patient lymphoblasts for dissecting the functional sequelae of genes integral to CNVs in carrier cell lines, ultimately enhancing understanding of biological processes which may contribute to the clinical phenotype.

Dataset Information

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

Publications

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

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