China
Africa
Explore chapters and articles related to this topic
Genetic Counseling in Assisted Reproductive Technology
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Unlike with prenatal diagnosis, with PGT-M, a test must be custom designed for each reproductive couple such that the at-risk and low-risk haplotypes can be defined. Determining the haplotypes involves assessing a set of genetic linkage markers shared among affected and/or unaffected first-degree relatives. The linkage markers (e.g., short tandem repeats [STRs]) flank the disease-causing variant within and around the gene. Linkage markers shared between the patient and an affected/carrier relative define the at-risk haplotype; genetic markers shared between the patient and an unaffected/non-carrier relative define the low-risk haplotype. Where possible, embryo testing involves assessing a combination of linkage markers as well as the disease-associated variant directly. If direct testing of the variant is not possible, PGT-M testing can often be carried out using linkage markers only, provided that the necessary relatives are available to provide samples for linkage analysis. The reason for this elaborate testing methodology is the limited number of cells that can be biopsied from a preimplantation embryo. Such a small sample number precludes many of the robust technologies available to directly detect the disease-causing variant in prenatal or pediatric/adult samples. Utilizing linkage analysis in combination with direct detection of the disease-causing variant, where possible, increases the accuracy of genetic testing applied to the extraordinarily small sample that can be obtained from preimplantation embryos.
Familial Aggregation of Chronic Obstructive Pulmonary Disease
Published in Stephen D. Litwin, Genetic Determinants of Pulmonary Disease, 2020
Bernice H. Cohen, Gary A. Chase
Genetic linkage is another type of relationship that has been explored, as discussed above. The methodological procedures are very specialized. They have been well described [117] and are not discussed here.
Identification of Genes Underlying Polygenic Obesity in Animal Models
Published in Claude Bouchard, The Genetics of Obesity, 2020
Craig H. Warden, Janis S. Fisler
The ability to analyze phenotype and genotype information for QTLs in model systems is combined in the Mapmaker29 and Mapmaker/QTL programs.22 The QTL analysis requires the construction of genetic linkage maps of ordered markers which can be constructed from primary data using the Mapmaker program.29 This program is interactive and relatively easy to use; the linkage maps constructed can be used directly by the Mapmaker/QTL program. Mapmaker/QTL uses genetic linkage maps along with quantitative phenotype data to locate QTLs by an efficient interval mapping method that calculates log-of-the-odds (LOD) scores for each quantitative trait at 1- to 2-centimorgan (cM) intervals between each marker.23 This gives Mapmaker/QTL the ability to detect QTLs in between linked markers. LOD scores provide a measure of the significance of linkage of trait and genotype. A LOD score of 3.0 or greater is a statistically significant evidence of linkage. While Mapmaker/QTL is an easy-to-use interactive program, it nevertheless should be used with some care. For instance, since the program uses a missing data algorithm to estimate the genotypes between markers, in some cases the estimated genotypes may give false positive LOD score results. Thus, the Mapmaker/QTL program cannot be used to prove the existence of a QTL between two linked markers, but rather guides one in the selection of additional markers to genotype in a cross at the sites of putative QTLs.
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 diversity outbred F1 mouse model identifies host-intrinsic genetic regulators of response to immune checkpoint inhibitors
Published in OncoImmunology, 2022
Justin B. Hackett, James E. Glassbrook, Maria C. Muñiz, Madeline Bross, Abigail Fielder, Gregory Dyson, Nasrin Movahhedin, Jennifer McCasland, Claire McCarthy-Leo, Heather M. Gibson
To perform a GWAS, we selected tumor latency as the phenotype representing response to ICI. As described above, tumor latency is defined by the time (in days) for a tumor to reach a volume of 150 mm3 (Figure 2(a)). Genotyping was performed via GigaMUGA, and haplotype reconstruction and GWAS was performed using the R/qtl2 package.15,16 Genetic linkage analysis identifies peaks on Chr 2, 7, 13, and 14 with logarithm of odds (LOD) scores (an adjusted measurement of significance) greater than 6, with the strongest association in Chr13 (Figure 3(a)). Importantly, we did not detect a correlation of the proportion of host B6 genome to tumor latency (Figure 3(b)), suggesting variation in response does not result from “degree of foreignness” of the tumor. Similarly, the proportion of the other seven contributing host founder strains also lacked correlation to tumor latency (Fig. S3).
Precision medicine in cardiac electrophysiology: where we are and where we need to go
Published in Expert Review of Precision Medicine and Drug Development, 2020
Ashish Correa, Syed Waqas Haider, Wilbert S. Aronow
On the other hand, non-candidate gene approaches begin with a particular trait or phenotype and try to identify genes that are associated with it. Included in these approaches are genome-wide association studies (GWAS), genetic linkage analyses and so on. Genetic variants implicated by these studies are supported by robust statistical power and reproducibility. But most importantly, these studies enable the identification of previously unknown culprit genes [14]. A GWAS is a phenotype-first approach study where DNA of many individuals with and without a particular trait are analyzed and single-nucleotide polymorphisms (SNPs) that are associated with the trait are identified by statistical techniques [18]. Genetic linkage is phenomenon wherein genes physically located near each other on a chromosome are inherited together during meiosis. Linkage analyses are genetic association studies whereby culprit genes for a given condition are localized due to genetic linkage, i.e. due to their co-segregation with genes responsible for easily identifiable traits that tend to be inherited with the condition under consideration [19]. Like GWAS, these studies have been used to identify genes and DNA segments that were previously never implicated in the causation of a given condition. Beyond understanding the pathophysiology of arrhythmias, various genetic analyses have helped us unravel the molecular mechanisms of normal cardiac electrophysiologic function [14]. (Figure 1)