Power of IRT in GWAS: Successful QTL Mapping of Sum Score Phenotypes Depends on Interplay Between Risk Allele Frequency, Variance Explained by the Risk Allele, and Test Characteristics

2012 ◽  
pp. n/a-n/a
Author(s):  
Stéphanie M. van den Berg ◽  
Susan K. Service
Keyword(s):  
Qtl Mapping ◽  
Allele Frequency ◽  
Risk Allele ◽  
Risk Allele Frequency ◽  
Test Characteristics ◽  
Sum Score ◽  
Variance Explained

scholarly journals Risk assessment of FLT3 and PAX5 variants in B-acute lymphoblastic leukemia: a case–control study in a Pakistani cohort

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7195 ◽  
Author(s):  
Ammara Khalid ◽  
Sara Aslam ◽  
Mehboob Ahmed ◽  
Shahida Hasnain ◽  
Aimen Aslam
Keyword(s):  
Risk Assessment ◽  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Allele Frequency ◽  
Risk Allele ◽  
Lymphoblastic Leukemia ◽  
Genotype Frequency ◽  
Risk Allele Frequency ◽  
Link Type ◽  
Cell Acute Lymphoblastic Leukemia

AIMS B-cell acute lymphoblastic leukemia (B-ALL) is amongst the most prevalent cancers of children in Pakistan. Genetic variations in FLT3 are associated with auto-phosphorylation of kinase domain that leads to increased proliferation of blast cells. Paired box family of transcription factor (PAX5) plays a critical role in commitment and differentiation of B-cells. Variations in PAX5 are associated with the risk of B-ALL. We aimed to analyze the association of FLT3 and PAX5 polymorphisms with B cell leukemia in Pakistani cohort. METHODS We collected 155 B-ALL subject and 155 control blood samples. For analysis, genotyping was done by tetra ARMS-PCR. SPSS was used to check the association of demographic factors of SNPs present in the population with the risk of B-ALL. RESULTS Risk allele frequency A at locus 13q12.2 (rs35958982, FLT3) was conspicuous and showed positive association (OR = 2.30, CI [1.20–4.50], P = 0.005) but genotype frequency (OR = 3.67, CI [0.75–18.10], P = 0.088) failed to show any association with the disease. At locus 9p13.2 (rs3780135, PAX5), the risk allele frequency was significantly higher in B-ALL subjects than ancestral allele frequency (OR = 2.17, CI [1.37–3.43], P = 0.000). Genotype frequency analysis of rs3780135 polymorphism exhibited the protective effect (OR = 0.55, CI [0.72–1.83], P = 0.029). At locus 13q12.2 (rs12430881, FLT3), the minor allele frequency G (OR = 1.15, CI [1.37–3.43], P = 0.043) and genotype frequency (OR = 2.52, P = 0.006) reached significance as showed p < 0.05. CONCLUSION In the present study, a strong risk of B-cell acute lymphoblastic leukemia was associated with rs35958982 and rs12430881 polymorphisms. However, rs3780135 polymorphism showed the protective effect. Additionally, other demographic factors like family history, smoking and consanguinity were also found to be important in risk assessment. We anticipate that the information from genetic variations in this study can aid in therapeutic approach in the future.

What can we learn from genetic studies of systemic lupus erythematosus? Implications of genetic heterogeneity among populations in SLE

Lupus ◽  
2010 ◽  
Vol 19 (12) ◽  
pp. 1452-1459 ◽  
Author(s):  
H-S. Lee ◽  
S-C. Bae
Keyword(s):  
Immune Response ◽  
Allele Frequency ◽  
Genetic Heterogeneity ◽  
Lupus Erythematosus ◽  
Genetic Factors ◽  
Risk Allele ◽  
Adaptive Immune Response ◽  
Environment Interaction ◽  
Risk Allele Frequency ◽  
Hla Dr

Recent progress in genetics has expanded the number of the genes associated with SLE to more than 20 in the past 2 years. One might assign these candidate genetic factors into several pre-existing biological pathways: (i) innate immune response including TLR/interferon signaling pathways (IRF5, STAT4, TNFAIP3, and TREX1); (ii) adaptive immune response (HLA-DR, PTPN22, PDCD1, STAT4, LYN, BLK, and BANK1) including B, T cells, and antigen-presenting cells; and (iii) immune complex clearance mechanism (FCGRs, CRP, and ITGAM). In addition, there are also several genes and loci that could not be assigned into previous known pathways (KIAA1542, PXK, XKR6, ATG5, etc), providing possible novel mechanisms in SLE. It has also been evident that there are similarities and differences in SLE susceptibility loci across ethnic groups. Here we categorize the susceptible genes into four groups. The first group is the consistently associated genes with similar risk allele frequency between multiple ethnic populations such as STAT4, TNFAIP3, BANK1, and IRAK1/MECP2. The second group is the genes that are consistently associated but show marked difference in risk allele frequency (BLK, IRF5). The third group is the genes in which different risk variants exist within a gene or genetic loci (allelic heterogeneity) such as HLA-DR, FCGRs, and IRF5. The fourth group is the genes that show consistently discrepancy between populations such as PTPN22 and possibly ITGAM, PXK, and LYN (genetic heterogeneity). The possible explanations for differences of susceptible genetic factors between populations could be different genetic backgrounds, contribution of gene—gene or gene—environment interaction, and the relation between marker and causal variants. Therefore, efforts to identify ethnic-specific genetic factors or disease causing variants should be necessary for individualized therapy for SLE in future.

scholarly journals Drusenoid Pigment Epithelial Detachment: Genetic and Clinical Characteristics

2021 ◽  
Vol 22 (8) ◽  
pp. 4074
Author(s):  
Taiyo Shijo ◽  
Yoichi Sakurada ◽  
Koji Tanaka ◽  
Akiko Miki ◽  
Seigo Yoneyama ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Risk Allele ◽  
Age Related Macular Degeneration ◽  
Pigment Epithelial Detachment ◽  
Risk Allele Frequency ◽  
Genetic Characteristics ◽  
Pigment Epithelial ◽  
Age Related ◽  
Arms2 A69s ◽  
Cfh I62v

Few studies report drusenoid pigment epithelial detachment (DPED) in Asians. In this multicenter study, we report the clinical and genetic characteristics of 76 patients with DPED, and, for comparison, 861 patients with exudative age-related macular degeneration (AMD) were included. On the initial presentation, the mean best-corrected visual acuity was 0.087 ± 0.17 (logMAR unit), and mean DPED height and width were 210 ± 132 and 1633 ± 1114 µm, respectively. Fifty-one (67%) patients showed macular neovascularization in the contralateral eye. The risk allele frequency of both ARMS2 A69S and CFH I62V was significantly higher in DPED than in typical AMD and polypoidal choroidal vasculopathy (PCV) (ARMS2 A69S risk allele frequency: DPED 77% vs. typical AMD 66% vs. PCV 57%, CFH I62V risk allele frequency: DPED 87% vs. typical AMD 73% vs. PCV 73%), although the risk allele frequency of both genes was similar between the DPED group and retinal angiomatous proliferation (RAP) group (ARMS2 A69S: p = 0.32, CFH I62V, p = 0.11). The prevalence of reticular pseudodrusen (RPD) was highest in RAP (60%), followed by DPED (22%), typical AMD (20%), and PCV (2%). Although the prevalence of RPD differs between DPED and RAP, these entities share a similar genetic background in terms of ARMS2 and CFH genes.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

The association of FKBP5 polymorphism with asthma susceptibility in asthmatic patients

2021 ◽  
Vol 32 (4) ◽  
pp. 479-484
Author(s):  
Sura F. Alsaffar ◽  
Haider A. Rasheed ◽  
Jabbar H. Yenzeel ◽  
Haider F. Ghazi
Keyword(s):  
Allele Frequency ◽  
Risk Allele ◽  
Inhaled Corticosteroids ◽  
Control Group ◽  
The Novel ◽  
Nucleotide Polymorphisms ◽  
Asthmatic Patients ◽  
Asthma Susceptibility ◽  
Group Members ◽  
Novel Snp

Abstract Objectives Inhaled corticosteroids are the most effective controllers of asthma, although asthmatics vary in their response. FKBP51 is a major component of the glucocorticoid receptor which regulates its responses to corticosteroids. Therefore, the present study aims to identify the role of FKBP5 gene polymorphism in asthma susceptibility and corticosteroid resistance. Methods DNA was extracted from the blood of 68 asthmatic and 40 control subjects. FKBP5 gene fragments were amplified by PCR and sequenced by the Sanger method. The sequencing results were aligned by mapping on the reference sequences of National center of Biotechnology Information (NCBI) and single nucleotide polymorphisms (SNPs) which were checked. Finally, the genotype, allele frequency and odds ratio (OR) were calculated. Results The FKBP5 fragment sequencing revealed the presence of rs1360780 and one novel SNP found in 17 samples taken from asthmatic patients as compared to db SNP data in the NCBI database. The FKBP5 variant (rs1360780) indicated that the allele frequency of risk allele T was 41.18% in patients and 20% in control group members p<0.001 and OR=2.8 when compared to a wild C allele frequency of 58.82% in patients and 64% in the control group members. The novel SNP FKBP5 was compared to the SNP database in the NCBI database in which wild T allele was substituted with G. The novel SNP was submitted to the ClinVar Submission Portal at NCBI with accession number: rs1581842283 and confirmed an asthma susceptibility risk factor with allele G frequency of 11.76% in asthmatics and 2.5% in the control group members (OR=5.2, p<0.05), as compared to a wild T allele frequency of 88.24% in asthmatics and 97.5% in the control group members. Conclusions The risk allele T of rs1360780 and the novel SNP rs1581842283 risk allele G predict asthma susceptibility but show no association with corticosteroid resistant.

scholarly journals Simulation of model overfit in variance explained with genetic data

2019 ◽  
Author(s):  
Jaime Derringer
Keyword(s):  
Machine Learning ◽  
Genetic Architecture ◽  
Learning Algorithm ◽  
Genetic Data ◽  
Effect Sizes ◽  
Machine Learning Algorithm ◽  
Random Data ◽  
Sum Score ◽  
Target Sample ◽  
Variance Explained

AbstractTwo recent papers, and an author response to prior commentary, addressing the genetic architecture of human temperament and character claimed that “The identified SNPs explained nearly all the heritability expected”. The authors’ method for estimating heritability may be summarized as: Step 1: Pre-select SNPs on the basis of GWAS p<0.01 in the target sample. Step 2: Enter target sample genotypes (the pre-selected SNPs from Step 1) and phenotypes into an unsupervised machine learning algorithm (Phenotype-Genotype Many-to-Many Relations Analysis, PGMRA) for further reduction of the set of SNPs. Step 3: Test the sum score of the SNPs identified from Step 2, weighted by the GWAS regression weights estimated in Step 1, within the same target sample. The authors interpreted the linear regression model R2 obtained from Step 3 as a measure of successfully identified heritability. Regardless of the method applied to select SNPs in Step 2, the combination of Steps 1 and 3, as described, causes inflation of the estimated effect size. The extent of this inflation is demonstrated here, where random SNP selection and polygenic scoring from simulated random data recovered effect sizes similar to those reported in the original empirical papers.

scholarly journals HLA-DPB1*03 as Risk Allele and HLA-DPB1*04 as Protective Allele for Both Early- and Adult-Onset Multiple Sclerosis in a Hellenic Cohort

2020 ◽  
Vol 10 (6) ◽  
pp. 374 ◽  
Author(s):  
Maria Anagnostouli ◽  
Artemios Artemiadis ◽  
Maria Gontika ◽  
Charalampos Skarlis ◽  
Nikolaos Markoglou ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Allele Frequency ◽  
Risk Allele ◽  
Laboratory Data ◽  
Protective Role ◽  
Adult Onset ◽  
Protective Allele ◽  
Hla Dr ◽  
Hla Dq

Background: Human Leucocyte Antigens (HLA) represent the genetic loci most strongly linked to Multiple Sclerosis (MS). Apart from HLA-DR and HLA–DQ, HLA-DP alleles have been previously studied regarding their role in MS pathogenesis, but to a much lesser extent. Our objective was to investigate the risk/resistance influence of HLA-DPB1 alleles in Hellenic patients with early- and adult-onset MS (EOMS/AOMS), and possible associations with the HLA-DRB1*15:01 risk allele. Methods: One hundred MS-patients (28 EOMS, 72 AOMS) fulfilling the McDonald-2010 criteria were enrolled. HLA genotyping was performed with standard low-resolution Sequence-Specific Oligonucleotide techniques. Demographics, clinical and laboratory data were statistically processed using well-defined parametric and nonparametric methods and the SPSSv22.0 software. Results: No significant HLA-DPB1 differences were found between EOMS and AOMS patients for 23 distinct HLA-DPB1 and 12 HLA-DRB1 alleles. The HLA-DPB1*03 allele frequency was found to be significantly increased, and the HLA-DPB1*02 allele frequency significantly decreased, in AOMS patients compared to controls. The HLA-DPB1*04 allele was to be found significantly decreased in AOMS and EOMS patients compared to controls. Conclusions: Our study supports the previously reported risk susceptibility role of the HLA-DPB1*03 allele in AOMS among Caucasians. Additionally, we report for the first time a protective role of the HLA-DPB1*04 allele among Hellenic patients with both EOMS and AOMS.

scholarly journals Contribution of common risk variants to multiple sclerosis in Orkney and Shetland

2021 ◽  
Author(s):  
Catriona L. K. Barnes ◽  
Caroline Hayward ◽  
David J. Porteous ◽  
Harry Campbell ◽  
Peter K. Joshi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Risk Allele ◽  
Risk Scores ◽  
Data Sets ◽  
Common Variants ◽  
Risk Allele Frequency ◽  
Polygenic Risk ◽  
Risk Variants ◽  
Generation Scotland ◽  
High Prevalence

AbstractOrkney and Shetland, the population isolates that make up the Northern Isles of Scotland, are of particular interest to multiple sclerosis (MS) research. While MS prevalence is high in Scotland, Orkney has the highest global prevalence, higher than more northerly Shetland. Many hypotheses for the excess of MS cases in Orkney have been investigated, including vitamin D deficiency and homozygosity: neither was found to cause the high prevalence of MS. It is possible that this excess prevalence may be explained through unique genetics. We used polygenic risk scores (PRS) to look at the contribution of common risk variants to MS. Analyses were conducted using ORCADES (97/2118 cases/controls), VIKING (15/2000 cases/controls) and Generation Scotland (30/8708 cases/controls) data sets. However, no evidence of a difference in MS-associated common variant frequencies was found between the three control populations, aside from HLA-DRB1*15:01 tag SNP rs9271069. This SNP had a significantly higher risk allele frequency in Orkney (0.23, p value = 8 × 10–13) and Shetland (0.21, p value = 2.3 × 10–6) than mainland Scotland (0.17). This difference in frequency is estimated to account for 6 (95% CI 3, 8) out of 150 observed excess cases per 100,000 individuals in Shetland and 9 (95% CI 8, 11) of the observed 257 excess cases per 100,000 individuals in Orkney, compared with mainland Scotland. Common variants therefore appear to account for little of the excess burden of MS in the Northern Isles of Scotland.

scholarly journals FOXN3 hyperglycemic risk allele and insulin sensitivity in humans

2019 ◽  
Vol 7 (1) ◽  
pp. e000688 ◽  
Author(s):  
Melissa L Erickson ◽  
Santhosh Karanth ◽  
Eric Ravussin ◽  
Amnon Schlegel
Keyword(s):  
Blood Glucose ◽  
Insulin Sensitivity ◽  
Allele Frequency ◽  
Risk Allele ◽  
Fasting Blood Glucose ◽  
Fat Free Mass ◽  
Glucose Disposal ◽  
Oral Glucose ◽  
Allele Carrier ◽  
Euglycemic Hyperinsulinemic Clamp

ObjectiveThe rs8004664 variation within the FOXN3 gene is significantly and independently associated with fasting blood glucose in humans. We have previously shown that the hyperglycemia risk allele (A) increases FOXN3 expression in primary human hepatocytes; over-expression of human FOXN3 in zebrafish liver increases fasting blood glucose; and heterozygous deletion of the zebrafish orthologfoxn3decreases fasting blood glucose. Paralleling these model organism findings, we found that rs8004664 A|A homozygotes had blunted glucagon suppression during an oral glucose tolerance test. Here, we test associations between insulin sensitivity and the rs8004664 variation.Research design and methods92 participants (49±13 years, body mass index: 32±6 kg/m2, 28 with and 64 without type 2 diabetes mellitus) were genotyped at rs8004664. Insulin sensitivity was measured by the euglycemic-hyperinsulinemic clamp technique.ResultsThe “A” allele frequency was 59%; the protective (G) allele frequency was 41% (A|A: n=29; G|G: n=12; A|G: n=50). Clamp-measured glucose disposal rate (GDR) was not different by genotype (F=0.046, p=0.96) or by “A” allele carrier (p=0.36). Female G|G homozygotes had better insulin sensitivity compared to female “A” allele carriers (GDR; G|G: 9.9±3.0 vs A|A+A|G: 7.1±3.0 mg/kg fat-free mass+17.7/min; p=0.04). Insulin sensitivity was not different by genotype or by “A” allele carriers.ConclusionThe rs8004664 variation within the FOXN3 gene may modulate insulin sensitivity in women.

Sign in / Sign up

Export Citation Format

Share Document