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Preimplantation Genetic Testing of Aneuploidies (PGT-A)
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Daniela N. Bakalova, Darren K. Griffin, Maria E. Póo, Alan R. Thornhill
Single nucleotide polymorphisms (SNPs) are variations in DNA sequence found in various regions of the genome that are highly variable within the human population. As for aCGH, SNP microarray methodology requires DNA hybridization and fluorescence microscopy. However, SNP arrays interrogate specific polymorphisms of the 300 million present across the human genome (with microarray densities typically ranging from 100K to 1M); this provides a genotype for the test sample which is then compared to known maternal and paternal SNP patterns to identify a ploidy status based on predicted heterozygous patterns at specific loci [50]. When compared to classic microarray technologies for PGT-A analysis that can only detect gains and losses at specific chromosomal regions, SNP arrays can simultaneously identify the ploidy status of samples, the parental origin of chromosomal abnormalities, uniparental disomy (UPD), and specific artefactual errors such as allele dropout (ADO).
Biochemical Markers in Ophthalmology
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Abdus Samad Ansari, Pirro G. Hysi
All common ocular diseases are contributed to, in large parts, by genetic predisposition caused by sequence polymorphisms [5]. Different platforms that measure DNA variation can be used to identify polymorphic genomic markers of a disease. Typically, these platforms fall in one of three categories: microsatellite-based, high-throughput arrays that simultaneously target up to millions of polymorphisms, especially single-nucleotide polymorphisms (SNPs), and sequencing technologies that individually identify each nucleotide in the sequence of the entire genome or smaller regions of interest. Each platform suits the circumstances of the trait under study and makes a compromise between assay costs and information resolution. Microsatellites were often used in linkage analyses of multigenerational pedigrees to identify broad regions harboring polymorphisms conferring high relative risks that are often causative of rare diseases, whilst SNP arrays and genome sequencing are best suited to larger cross-sectional association studies [6]. Due to their low cost per unit of information, SNP arrays have become particularly popular and have driven the growth of genetic knowledge in the field of ophthalmology and other fields, by enabling the introduction and massive adaptation of designs such as genome-wide association studies (GWAS) [7].
The Meta-Analysis of Genetic Studies
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
Cosetta Minelli, John Thompson
The cost of the SNP array for measuring all of the genetic variants plus its processing has fallen considerably since large-scale GWAS were first performed in around 2007, and by 2018, the total cost varied between about $100 and $400 per participant depending on the number of variants. This cost is low enough that GWAS have become very popular, but it is still high enough to ensure that studies only recruit a few thousand participants. The result is that most studies are under-powered by themselves and researchers have been forced to form international consortia to meta-analyze studies of the same trait or disease outcome (Austin et al., 2012). Concern over confidentiality and a reluctance to lose control of the raw data have meant that most research groups will not release the raw SNP measurements and the consortia are forced to meta-analyze summary data.
Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder
Published in The World Journal of Biological Psychiatry, 2023
Vincenzo Dattilo, Sheila Ulivi, Alessandra Minelli, Martina La Bianca, Edoardo Giacopuzzi, Marco Bortolomasi, Stefano Bignotti, Massimo Gennarelli, Paolo Gasparini, Maria Pina Concas
All samples have been genotyped with Illumina 370 K/700K high-density SNP array (Illumina Inc., San Diego, CA, USA). Genotypes were called with Illumina GenomeStudio. Each batch was processed according to standard quality control (QC) procedures with the following criteria for inclusion: sample call rate ≥0.95, gender check, SNP call rate ≥0.95, Hardy-Weinberg Equilibrium (HWE) p-value >1 × 10−6, and minor allele frequency (MAF) ≥0.01. Genotype imputation was conducted using IMPUTE2 (Howie et al. 2009) considering as reference a custom panel generated merging the 1000 Genomes phase 3 (Altshuler et al. 2012) and whole-genome sequences of INGI samples (Cocca et al. 2020). After imputation, SNPs with MAF < 0.01 and Info Score < 0.4 were discarded from the statistical analyses.
Genome-wide analysis of runs of homozygosity in Pakistani controls with no history of speech or language-related developmental phenotypes
Published in Annals of Human Biology, 2023
Tahira Yasmin, Erin M. Andres, Komal Ashraf, Muhammad Asim Raza Basra, Muhammad Hashim Raza
We used DNA samples of 100 controls and performed SNP genotyping using the Illumina Infinium QC Array-24. The SNP genotyping was outsourced to the Johns Hopkins University School of Medicine, Genetic Resources Core Facility (https://grcf.jhmi.edu/genotyping/). The Illumina Infinium QC Array-24 array is a cost-effective, low-density SNP array, proven to be efficient in detecting sample-specific variant calls, consanguinity in samples, sex, and ethnicity (Ponomarenko et al. 2017). It has been widely used in association studies and proved efficient enough to find genetic linkage and associations (Ponomarenko et al. 2017; Andres et al. 2019; 2020; Pinese et al. 2020). This array contains 15,949 SNPs evenly distributed throughout the genome with an average density of 0.5 megabases (Mbs). There are 11,994 SNPs spread across autosomal chromosomes, and the rest are dispersed across sex chromosomes and mitochondrial chromosomes. The SNP genotyping data of 97 control individuals was available, and 4 CEPH samples were used as positive controls during genotyping. The SNP genotyping was unsuccessful for the three controls, one belonged to the related individuals, and the other two were unrelated. In the current study, we excluded the genotyping data of related individuals from the analysis and only the data of 86 unrelated individuals (39 males, 47 females) were used in the ROH analysis.
A 2020 update on the use of genetic testing for patients with primary immunodeficiency
Published in Expert Review of Clinical Immunology, 2020
Ivan K. Chinn, Jordan S. Orange
Two methods are commonly used for identification of CNVs. In array comparative genomic hybridization (CGH), patient DNA hybridization to a microarray of DNA oligonucleotide probes is compared to competitive reference DNA hybridization to the probes within the same assay. The probes are often designed specifically for the detection of CNVs. For single nucleotide polymorphism (SNP) arrays, the presence of common single base-pair changes across the genome is examined relative to controls also by means of microarray hybridization to probes. The best approach consists of a combination of both methods [15]. These techniques are still considered the gold standard for CNV testing compared to the use of bioinformatic tools. Factors that can affect the performance of the tests include probe design for array CGH and probe density for SNP arrays.