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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
Linkage analysis of family data works by identifying stretches of DNA present in family members with disease and absent in those without disease, and relies on the tendency for parts of the genome to be inherited together as a consequence of their physical proximity (Attia et al., 2009). A measured genetic marker passed down through a family in a way that consistently accompanies disease therefore suggests the presence of a nearby gene with a functional effect (Dawn Teare and Barrett, 2005). Linkage studies can find chromosomal regions containing genes associated with disease but cannot point toward individual genetic variants, which can only be identified by further genetic association or functional studies.
Major histocompatibility complex
Published in Gabriel Virella, Medical Immunology, 2019
Ellen Klohe, Janardan P. Pandey
There are two major approaches to determining the genetic etiology of a disease: linkage analysis and association analysis. These terms are often mistakenly used synonymously. Linkage implies that the gene under consideration and the putative gene responsible for the disease are on the same chromosome. It is determined by co-segregation of the disease with a particular genetic variant in families consisting of affected and unaffected individuals. This approach has been useful in the identification of genes for diseases that follow simple Mendelian inheritance. Association implies that a specific allele is found more or less often in a group of unrelated individuals with a disease than in subjects without that disease. This approach is more powerful than linkage in detecting the genes for complex diseases that are polygenetic or are strongly influenced by other factors as in many autoimmune diseases. In some cases, HLA allele-specified structural and functional differences have been identified and are postulated to play an important role in disease susceptibility or resistance as in the case of HIV control.
The FAMMM Syndrome in the Netherlands
Published in Henry T. Lynch, Ramon M. Fusaro, Hereditary Malignant Melanoma, 2019
W. Bergman, A. van Haeringen, L. N. Went
Linkage analysis was carried out using the LINKAGE programs. The FAMMM gene frequency was estimated at 0.003 with a lifetime penetrance of 85% as was found in our families and described in Section II.B. As can be seen in Table 2, RH yields a strong negative lod score for all recombination frequencies, allowing exclusion of 23 centiMorgans on either side of the marker. Moreover, all the other seven markers gave negative lod scores. The combined linkage data thus definitely exclude the FAMMM gene from the short arm of chromosome 1 in our families.
Linkage analysis identifies novel genetic modifiers of microbiome traits in families with inflammatory bowel disease
Published in Gut Microbes, 2022
Arunabh Sharma, Silke Szymczak, Malte Rühlemann, Sandra Freitag-Wolf, Carolin Knecht, Janna Enderle, Stefan Schreiber, Andre Franke, Wolfgang Lieb, Michael Krawczak, Astrid Dempfle
Evidence for linkage at genome-wide significance level (i.e. logarithm of odds [LOD] score >3) was obtained between 12 different chromosomal regions and the abundance of one of seven microbial genera, namely Barnesiella (maximum LOD score 3.24 on chr4), Clostridium_XIVa (max LOD 3.02 on chr4, max LOD 4.39 for region 1 on chr14, max LOD 3.60 for region 2 on chr14), Pseudoflavonifractor (max LOD 3.13 on chr7), Parasutterella (max LOD 3.02 on chr14), Ruminococcus (max LOD 4.42 for region 1 on chr16, max LOD 3.11 for region 2 on chr16), Roseburia (max LOD 3.78 on chr19), and Odoribacter (max LOD 4.31 for region 1 on chr22, max LOD 4.26 for region 2 on chr22, max LOD 4.08 for region 3 on chr22), as well as the Shannon index of α diversity (max LOD 3.72 on chr3) (Table 2). A complete overview of the results of the genome-wide linkage analysis is provided in Supplementary Fig. 2.
Atrial fibrillation in young patients
Published in Expert Review of Cardiovascular Therapy, 2018
Jean-Baptiste Gourraud, Paul Khairy, Sylvia Abadir, Rafik Tadros, Julia Cadrin-Tourigny, Laurent Macle, Katia Dyrda, Blandine Mondesert, Marc Dubuc, Peter G. Guerra, Bernard Thibault, Denis Roy, Mario Talajic, Lena Rivard
Contribution of genetic testing has been disappointing since only rare genetic variants were identified in a small number of patients [31–33]. Few familial studies using linkage analysis have succeeded in identifying a genetic variant. Incomplete penetrance, phenotype variability and a complex mode of inheritance could explain this complex heritability. Finally, rare genetic variations in 36 genes have been associated with AF [34]. Some mutations affect sodium and potassium currents (Ikr, Iks, Ik1, Ikur, IkATP, IkAch, Ikur, IAHP, If and INa), known to be involved in other inherited arrhythmias. Other mutations affect cellular electrical coupling, sodium homeostasis, transcription factors, and the nuclear envelope [35]. However, rare variants in AF concern only a small fraction of patients. In contrast, to account for the missing inheritability of rare variants, genome-wide association studies have identified a total of 15 common genetic variants (single nucleotide polymorphism) linked to AF that could modify the phenotype and identify loci that have been previously linked to cardiac conduction [34,36]. Considering this complex genetic inheritance and the limited diagnostic and prognostic value, routine genetic testing is not currently indicated for lone AF [27].
Progress towards precision medicine for lupus: the role of genetic biomarkers
Published in Expert Review of Precision Medicine and Drug Development, 2018
Juan-Manuel Anaya, Kelly J. Leon, Manuel Rojas, Yhojan Rodriguez, Yovana Pacheco, Yeny Acosta-Ampudia, Diana M. Monsalve, Carolina Ramirez-Santana
Genetic markers are different forms of variations on DNA and include mainly SNPs, tandem repeats, insertions and deletions, copy number variations (CNVs), and chromosomal rearrangements [71]. Candidate-gene studies, pedigree-based linkage analysis, GWAS, and next-generation sequencing have been the primary techniques applied to discover current significant variants [72,73]. Over 100 variants individually or in combination explain nearly 50% of SLE heritability (i.e. the proportion of phenotypic variation in a population that is attributable to genetic variation among individuals) [69,74]. The missing heritability may be identified by new strategies including gene–gene interactions and analysis based on gene/protein networks and tools such as a gene ontology database [75,76].