Sample Size Calculation in Genetic Association Studies: A Practical Approach

Association between COMMD1 gene polymorphism rs11125908 and rheumatoid arthritis in the Cuban population

OBJECTIVE

To evaluate the association of the rs11125908 polymorphism in the COMMD1 gene in the Cuban population with rheumatoid arthritis (RA).

METHODS

In this case-control study, 161 RA patients and 150 control subjects were genotyped for rs11125908 by the allele-specific polymerase chain reaction method. DNA sequencing was used to verify the assignation of the polymorphism. The odds ratios (OR) and their 95% confidence interval were calculated by logistic regression to determine the associations between genotypes and RA using the SNPStats software.

RESULTS

An association of the single nucleotide polymorphism with the disease was found in the overdominant model (p=0.025; OR=1.91) for the AG genotype. Our analyses revealed an association between rs11125908 and the subgroup of patients with swollen joints < median under the codominant model for AG (p=0.034; OR=2.30) and GG genotype (p=0.034; OR=0.82) and with the overdominant model (p=0.01; OR=2.38). The subgroup of patients with an age of onset lower than the mean and AG genotype showed an association in the overdominant model (p=0.027; OR=2.27). Disease activity score 28 with erythrocyte sedimentation rate and disease duration variables were not associated with the rs11125908 polymorphism.

CONCLUSIONS

rs11125908 was associated with RA and with the number of swollen joints and age of onset subgroup analyses. We provide concepts for treatments for RA, based on pharmacological management of COMMD1 expression.

Association of Glycoprotein IIIa PlA1/A2 Polymorphism with Risk of Stroke: Updated Meta-Analysis

Cardiovascular diseases are the main cause of death in the world, with ischemic heart disease (i.e., myocardial infarction) and cerebrovascular disease (i.e., stroke) taking the highest toll. Advances in diagnosis and treatment have led to a significant alleviation of ischemic complications, specifically in the realm of pharmacotherapy and interventional devices, while pharmacogenomics has yet to be fully leveraged to improve the burden of disease. Atherothrombotic events might occur earlier or respond worse to treatment in patients with genetic variants of GP IIb/IIIa. Therefore, we aimed to quantitate the involvement of the PlA2 variant in the risk of cerebral stroke events. A systematic search and meta-analysis were performed by pooling the risks of individual studies. A total of 31 studies comprising 5985 stroke patients and 7886 controls were analyzed. A meta-analysis of four studies on hemorrhagic stroke patients showed no association with the PIA2 rs5918(C) polymorphism in both fixed-effect (OR = 0.90 95%CI [0.71; 1.14]; p = 0.398) and random-effect models (OR = 0.86 95%CI [0.62; 1.20]; p-value = 0.386). The power of this analysis was below <30%, indicating a limited ability to detect a true effect. An analysis of the 28 studies on ischemic stroke revealed a significant association with the PIA2 rs5918(C) allele in both fixed-effect (OR = 1.16 95%CI [1.06; 1.27]; p = 0.001) and random-effect models (OR = 1.20 95%CI [1.04; 1.38]; p-value = 0.012), with a power of >80%. The PIA2 allele was associated with an increased risk of ischemic stroke. No association was found with hemorrhagic stroke, most likely due to the small number of available studies, which resulted in a lack of power.

Genetic risk score for gestational weight gain

Gestational weight gain (GWG) involves health consequences for both mother and offspring. Genetic factors seem to play a role in the GWG trait. For small effect sizes of a single genetic polymorphism (SNP), a genetic risk score (GRS) summarizing risk-associated variation from multiple SNPs can serve as an effective approach to genetic association analysis. The aim of the study was to analyze the association between genetic risk score (GRS) and gestational weight gain (GWG). GWG was calculated for a total of 342 healthy Polish women of Caucasian origin, aged 19 to 45 years. The SNPs rs9939609 (FTO), rs6548238 (TMEM18), rs17782313 (MC4R), rs10938397 (GNPDA2), rs10913469 (SEC16B), rs1137101 (LEPR), rs7799039 (LEP), and rs5443 (GNB3) were genotyped using commercial TaqMan SNP assays. A simple genetic risk score was calculated into two ways: GRS1 based on the sum of risk alleles from each of the SNPs, while GRS2 based on the sum of risk alleles of FTO, LEPR, LEP, and GNB3. Positive association between GRS2 and GWG (β = 0.12, p = 0.029) was observed. Genetic risk variants of TMEM18 (p = 0.006, OR = 2.6) and GNB3 (p < 0.001, OR = 3.3) are more frequent in women with increased GWG, but a risk variant of GNPDA2 (p < 0.001, OR = 2.7) is more frequent in women with adequate GWG, and a risk variant of LEPR (p = 0.011, OR = 3.1) in women with decreased GWG. GRS2 and genetic variants of TMEM18, GNB3, GNPDA2, and LEPR are associated with weight gain during pregnancy.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

SNP in PTPN22, PADI4, and STAT4 but Not TRAF1 and CD40 Increase the Risk of Rheumatoid Arthritis in Polish Population

Single nucleotide polymorphisms in non-HLA genes are involved in the development of rheumatoid arthritis (RA). SNPS in genes: PADI4 (rs2240340), STAT4 (rs7574865), CD40 (rs4810485), PTPN22 (rs2476601), and TRAF1 (rs3761847) have been described as risk factors for the development of autoimmune diseases, including RA. This study aimed to assess the prevalence of polymorphisms of these genes in the Polish population of patients with rheumatoid arthritis as compared to healthy controls. 324 subjects were included in the study: 153 healthy subjects and 181 patients from the Department of Rheumatology, Medical University of Lodz who fulfilled the criteria of rheumatoid arthritis diagnosis. Genotypes were determined by Taqman SNP Genotyping Assay. rs2476601 (G/A, OR = 2.16, CI = 1.27-3.66; A/A, OR = 10.35, CI = 1.27-84.21), rs2240340 (C/T, OR = 4.35, CI = 2.55-7.42; T/T, OR = 2.80, CI = 1.43-4.10) and rs7574865 (G/T, OR = 1.97, CI = 1.21-3.21; T/T, OR = 3.33, CI = 1.01-11.02) were associated with RA in the Polish population. Rs4810485 was also associated with RA, however after Bonferroni's correction was statistically insignificant. We also found an association between minor alleles of rs2476601, rs2240340, and rs7574865 and RA (OR = 2.32, CI = 1.47-3.66; OR = 2.335, CI = 1.64-3.31; OR = 1.88, CI = 1.27-2.79, respectively). Multilocus analysis revealed an association between CGGGT and rare (below 0.02 frequency) haplotypes (OR = 12.28, CI = 2.65-56.91; OR = 3.23, CI = 1.63-6.39). In the Polish population, polymorphisms of the PADI4, PTPN22, and STAT4 genes have been detected, which are also known risk factors for RA in various other populations.