Copy number variants in siblings of Mexican origin concordant for schizophrenia or bipolar disorder

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

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.

Genetic substrates of bipolar disorder risk in Latino families

Genetic studies of bipolar disorder (BP) have been conducted in the Latin American population, to date, in several countries, including Mexico, the United States, Costa Rica, Colombia, and, to a lesser extent, Brazil. These studies focused primarily on linkage-based designs utilizing families with multiplex cases of BP. Significant BP loci were identified on Chromosomes 18, 5 and 8, and fine mapping suggested several genes of interest underlying these linkage peaks. More recently, studies in these same pedigrees yielded significant linkage loci for BP endophenotypes, including measures of activity, sleep cycles, and personality traits. Building from findings in other populations, candidate gene association analyses in Latinos from Mexican and Central American ancestry confirmed the role of several genes (including CACNA1C and ANK3) in conferring BP risk. Although GWAS, methylation, and deep sequencing studies have only begun in these populations, there is evidence that CNVs and rare SNPs both play a role in BP risk of these populations. Large segments of the Latino populations in the Americas remain largely unstudied regarding BP genetics, but evidence to date has shown that this type of research can be successfully conducted in these populations and that the genetic underpinnings of BP in these cohorts share at least some characteristics with risk genes identified in European and other populations.

A Polynesian-specific copy number variant encompassing the MHC Class I Polypeptide-related Sequence A (MICA) gene associates with gout

Gout is of particularly high prevalence in the Māori and Pacific (Polynesian) populations of Aotearoa New Zealand (NZ). Here, we investigated the contribution of common population-specific copy number variation (CNV) to gout in the Aotearoa NZ Polynesian population. Microarray-generated genome-wide genotype data from Aotearoa NZ Polynesian individuals with (n = 1196) and without (n = 1249) gout were analyzed. Comparator population groups were 552 individuals of European ancestry and 1962 of Han Chinese ancestry. Levels of circulating major histocompatibility complex (MHC) class I polypeptide-related sequence A (MICA) were measured by enzyme-linked immunosorbent assay. Fifty-four CNV regions (CNVRs) appearing in at least 10 individuals were detected, of which seven common (>2%) CNVRs were specific to or amplified in Polynesian people. A burden test of these seven revealed associations of insertion/deletion with gout (odds ratio (OR) 95% confidence interval [CI] = 1.80 [1.01; 3.22], P = 0.046). Individually testing of the seven CNVRs for association with gout revealed nominal association of CNVR1 with gout in Western Polynesian (Chr6: 31.36–31.45 Mb, OR = 1.72 [1.03; 2.92], P = 0.04), CNVR6 in the meta-analyzed Polynesian sample sets (Chr1: 196.75–196.92 Mb, OR = 1.86 [1.16; 3.00], P = 0.01) and CNVR9 in Western Polynesian (Chr1: 189.35–189.54 Mb, OR = 2.75 [1.15; 7.13], P = 0.03). Analysis of European gout genetic association data demonstrated a signal of association at the CNVR1 locus that was an expression quantitative trait locus for MICA. The most common CNVR (CNVR1) includes deletion of the MICA gene, encoding an immunomodulatory protein. Expression of MICA was reduced in the serum of individuals with the deletion. In summary, we provide evidence for the association of CNVR1 containing MICA with gout in Polynesian people, implicating class I MHC-mediated antigen presentation in gout.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Association of MGAT4C with major neurocognitive disorder in the Mexican population

BACKGROUND

Neurocognitive disorders (NCDs) are characterized by cognitive decline. Most genetic studies of NCDs have been focused on single-nucleotide polymorphism; other genetic variations, such as copy number variants (CNV), have been less explored. The aim of the present study was to explore CNVs associated with NCDs in a small sample of Mexican individuals and search for the frequency in a larger replication sample of individuals at high-risk for or diagnosed with NCDs.

METHOD

The exploratory analysis analyzed whole-genome CNVs associated with NCDs in 1335 individuals, of whom 35 were diagnosed with NCDs and 1300 were population-based controls. Whole-genome CNVs were derived from PsychArray and the PennCNV algorithm. The frequency of associated CNVs in a sample of 277 individuals diagnosed with NCDs and 70 high-risk individuals was then determined using RT-PCR.

RESULTS

The exploratory analysis identified one deletion associated with NCDs (p = 0.007) affecting the gene MGAT4C (Mannosyl (Alpha-1,3-)-Glycoprotein Beta-1,4-N-Acetylglucosaminyltransferase, Isozyme C). In the replication sample, a frequency of 3.97% was found in individuals diagnosed with NCDs and 1.43% in high-risk individuals.

CONCLUSIONS

An association between a rare CNV on MGAT4C and cognitive impairment was found in this sample of the Mexican population. Nevertheless, studies with larger sample sizes are needed in order to further explore the association.