C-C motif Chemokine Ligand 3 Like 1 (CCL3L1) is characterized as a gene with copy number variable (CNV) and clustered at the segmental duplication on chromosome 17q12. CCL3L1 is responsible for the production of macrophage inflammatory protein (MIP) 1α that plays an important function in the immune system and host defense. Various copies range of CCL3L1 have been reported and associated with the diseases in different populations. Thus, this review aimed to summarise the distribution of CCL3L1 copy number from different populations according to the geographical region and highlighted the lacking of data from Malaysian population, which is one of the multi-ethnic countries due to the impacts of CCL3L1 copies on various diseases. Besides, we also outlined the methodologies available for the copy number typing. In overall, this review could provide significant information on the role of CCL3L1 copies in disease association and as well as providing evidence on the population diversity
Copy Number Variation

In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genotyping data have been developed; however, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs. In this study, we aimed to verify the effect of combining multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. For this purpose, we selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP; and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR. Through this analysis, we proposed that for reliable CNV-based genomewide association study using SNP array data, CNV calls must be performed with at least 3 different algorithms and that the CNVs consistently called from more than 2 algorithms must be used for association analysis, because they are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data.
Coat Protein Complex I ; DNA Copy Number Variations ; Genetic Variation ; Humans ; Polymerase Chain Reaction

DNA Copy Number Variations ; Epilepsy ; genetics ; Genetic Variation ; Humans ; Sequence Analysis, DNA

OBJECTIVE: Through a retrospective large sample analysis of copy number variants in single center, we explored the technical standards for the interpretation and reporting of constitutional copy-number variants (CNVs) jointly proposed by the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) in 2019, analyzing its impact on CNVs ratings and the improvement in the consistency of the classification of CNVs in clinical laboratories. METHODS: 236 CNVs that assessed as pathogenic, uncertain significant (including likely pathogenic, uncertain and likely benign) by the 2011 ACMG guidelines between August 2018 and December 2019 in our center were re-analyzed. Four working group members of the center reclassified and evaluated 235 CNVs according to 2019 ACMG guidelines. RESULTS: The consistency of clinical significance classification of CNVs was 91% and the α test coefficient was 0.98 among four working group members. Compared with the 2011 and 2019 ACMG technical standards for the CNVs classification, evaluation of pathogenicity and uncertain significant is basically consistent. 90% (45/50) of likely pathogenic and likely benign CNVs were Re-evaluated as variants of uncertain significance, and the difference is significant. CONCLUSION The new version ACMG/ClinGen guidelines for the evaluation of CNVs developed semi-quantitative point-based scoring system and help to improve the consistency in clinical classifications. It can also make the interpretation of CNVs more standardized and transparent.
DNA Copy Number Variations ; Genetic Testing ; Genetic Variation ; Genome, Human ; Humans ; Mutation ; Retrospective Studies

Asian populations contain a variety of ethnic groups that have ethnically specific genetic differences. Ethnic variants may be highly relevant in disease and human differentiation studies. Here, we identified ethnically specific variants and then investigated their distribution across Asian ethnic groups. We obtained 58,960 Pan-Asian single nucleotide polymorphisms of 1,953 individuals from 72 ethnic groups of 11 Asian countries. We selected 9,306 ethnic variant single nucleotide polymorphisms (ESNPs) and 5,167 ethnic variant copy number polymorphisms (ECNPs) using the nearest shrunken centroid method. We analyzed ESNPs and ECNPs in 3 hierarchical levels: superpopulation, subpopulation, and ethnic population. We also identified ESNP- and ECNP-related genes and their features. This study represents the first attempt to identify Asian ESNP and ECNP markers, which can be used to identify genetic differences and predict disease susceptibility and drug effectiveness in Asian ethnic populations.
Asian Continental Ancestry Group ; Classification ; Disease Susceptibility ; DNA Copy Number Variations ; Ethnic Groups ; Genetic Variation* ; Genotype ; Humans ; Polymorphism, Single Nucleotide