Case-parent trio studies considering genotype data from children affected by a disease and their parents are frequently used to detect single nucleotide polymorphisms (SNPs) associated with disease. The most popular statistical tests for this study design are transmission/disequilibrium tests (TDTs). Several types of these tests have been developed, for example, procedures based on alleles or genotypes. Therefore, it is of great interest to examine which of these tests have the highest statistical power to detect SNPs associated with disease. Comparisons of the allelic and the genotypic TDT for individual SNPs have so far been conducted based on simulation studies, since the test statistic of the genotypic TDT was determined numerically. Recently, however, it has been shown that this test statistic can be presented in closed form. In this article, we employ this analytic solution to derive equations for calculating the statistical power and the required sample size for different types of the genotypic TDT. The power of this test is then compared with the one of the corresponding score test assuming the same mode of inheritance as well as the allelic TDT based on a multiplicative mode of inheritance, which is equivalent to the score test assuming an additive mode of inheritance. This is, thus, the first time the power of these tests are compared based on equations, yielding instant results and omitting the need for time-consuming simulation studies. This comparison reveals that these tests have almost the same power, with the score test being slightly more powerful.
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http://dx.doi.org/10.1002/bimj.201300148 | DOI Listing |
bioRxiv
December 2024
Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ 08544, USA.
The process by which genes are transmitted from parent to child provides a source of randomization preceding all other factors that may causally influence any particular child phenotype. Because of this, it is natural to consider genetic transmission as a source of experimental randomization. In this work, we show how parent-child trio data can be leveraged to identify causal genetic loci by modeling the randomization during genetic transmission.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization variable selection (EMVS) method to simultaneously detect common and rare variants at the individual variant level using family trio data.
View Article and Find Full Text PDFArch Oral Biol
November 2024
Cell and Molecular Biology Facility, Jubilee Centre for Medical Research, Jubilee Mission Medical College and Research Institute, Thrissur, Kerala 680005, India. Electronic address:
Lancet
November 2024
Division of Hematology, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Transfusion-dependent β-thalassaemia (TDT) is a severe disease, resulting in lifelong blood transfusions, iron overload, and associated complications. Betibeglogene autotemcel (beti-cel) gene therapy uses autologous haematopoietic stem and progenitor cells (HSPCs) transduced with BB305 lentiviral vector to enable transfusion independence.
Methods: HGB-212 was a non-randomised, multicentre, single-arm, open-label, phase 3 study of beti-cel in patients with TDT conducted at eight centres in France, Germany, Greece, Italy, the UK, and the USA.
Transl Cancer Res
September 2024
Department of Laboratory Medicine, Maternal and Child Health Care Hospital of Linhe District, Bayannaoer City, China.
Background: Lymphoid enhancer-binding factor 1 (LEF1)/T cell factor (TCF) family members are key transcription factors in malignant tumors. In this study, the role of T cell factor 4 (TCF4) in the progression of gastric cancer (GC) cell migration and invasion was investigated.
Methods: Fifty-five pairs of GC tissues and adjacent non-tumor tissues were collected for evaluating the expression of LEF1/TCF family members, which were also evaluated by the Gene Expression Profiling Interactive Analysis (GEPIA) database, an online analysis platform based on The Cancer Genome Atlas and Genotype-Tissue Expression databases.
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