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Supplemental Data for "Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening"
<p>Data file accompanying: Haque IS, Lazarin GA, Kang HP, Evans EA, Goldberg JD, Wapner RJ. Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening. <em>JAMA. </em>2016;316(7):734-742. doi:10.1001/jama.2016.11139</p> <p>(SRC = self-reported racial/ethnic category; TG = targeted genotyping; NGS = next-generation sequencing)</p> <p>Data file includes:</p> <ul> <li><strong>Couple Data: </strong>Number of self-identified reproductive couples, separated by tandem or sequential screening status and by (mother SRC, father SRC)</li> <li><strong>Disease Severity</strong>: List of all diseases tested with severity rating as used in the manuscript.</li> <li><strong>Allele Data</strong>: Listing of all alleles considered pathogenic in manuscript's data analysis, with number of observations and number of tested chromosomes in each SRC.</li> <li><strong>Chromosome Frequencies</strong>: for each disease in each SRC: <ul> <li>Effective total chromosome count (effective sample size after integrating TG and NGS-only alleles).</li> <li>Beta posterior a,b: parameters a, b for the best-fit beta distribution approximating the probability that a random chromosome in this SRC carries a pathogenic allele (integrating both TG and NGS alleles).</li> <li># Chromosomes total/positive for TG alleles</li> <li># Chromosomes total/positive for NGS alleles</li> <li># Chromosomes total/positive for individuals tested by TG</li> <li># Chromosomes total/positive for individuals tested by NGS</li> </ul> </li> <li><strong>Disease Risks</strong>: for each disease in each pairing of SRCs <ul> <li>Father/Mother computed carrier frequency: probability that a random individual from father/mother's SRC is a carrier for the given disease</li> <li>Computed risk of affected conceptus (mean, 2.5, 97.5 percentiles): mean and CI of the posterior distribution over the probability that a random conceptus arising from the racial/ethnic pairing indicated would be homozygous or compound heterozygous for pathogenic alleles for the indicated disease.</li> <li>Computed carrier couple frequency: probability that a random couple from the given SRCs would be a carrier couple for the indicated disease (ie, that both members of the couple would be carriers for the indicated disease)</li> <li>Total couples: number of tandemly-tested couples of the indicated SRC pairing who both had the "routine carrier testing" indication for testing and were both tested for the given disease</li> <li>Number of carrier couple: from the set of "Total couples", the number of couples in which both members were carriers for the indicated disease</li> <li>Number of carrier couples expected: based on computed carrier couple frequency and number of tested couples, the expected number of carrier couples under the model described in sections 4.3.2 and 4.4 of the supplement.</li> <li>P-value: probability that the number of observed carrier couples or a more extreme count would have occurred by chance, given the posterior distribution over carrier couple counts (see section 4.4 of the supplement). One-tailed p-value.</li> </ul> </li> </ul>
Genetic risk score and age at onset
Open the record for dataset details and reuse information.
Figure 6 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 6. Manhattan plots showing SNP levels of ROH per autosome for A, All L. s. svecica individuals, B, Sve_Krk population. The Manhattan plot portrays ROH analysis across 28 autosomes. The height of the peak represents the percentage of individuals sharing homozygous SNP per ROH.
Figure 4 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 4. SNP-based analyses of population structure. A, discriminant analysis of the principal components (DAPC) analysis of genetic structure for two subspecies' genetic clusters (on the left) and, B, for seven populations (on the right). Each colour shade represents subspecies or population genetic clusters, respectively. Every point represents an individual, while inertia ellipses represent 67% of the individuals. Discriminant analysis eigenvalues are displayed by small insets. C, admixture analysis for K = 2. Each vertical bar shows an individual level of shared ancestry between the two subspecies. The two bands below the admixture plot mark individual's subspecies and population affiliation, respectively.
Figure 5 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 5. Pairwise FineRADStructure co-ancestry analysis of 148 genotyped specimens. Ancestral population labels are displayed on the vertical and horizontal axes. Upper horizontal bar stands for subspecies genetic clusters: L. s. svecica—left label, intermediate—centre, L. s. cyanecula—right. Lower horizontal bar depicts population origin if the individuals using the same coding as in Fig. 3.
Figure 3 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 3. The Cytb haplotype network of red-spotted and white-spotted bluethroat populations. Pie charts illustrate the haplotype variants shared among populations. Each circle represents a unique haplotype variant. Sizes of the circles are proportional to the number of individuals. Hatch marks on the branches represent the number of mutational steps that separate haplotypes. Black circles represent hypothetical haplotypes. Red-spotted bluethroat populations are: 1. Krkonoše Mountains (Sve_Krk); 2. Kola (Sve_Klp); 3. Abisko (Sve_Abi). Whitespotted populations are: 1. Třeboňsko (Cya_Trb); 2. St Petersburg (Cya_Stp); 3. Vomáčka (Cya_Vmk); 4. Krkonose Mountains (Cya_Krk). The haplotype marked with the red asterisk is a shared haplotype found in both subspecies.
Figure 1 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 1. Locations of sampled individuals of red-spotted (L. s. svecica) and white-spotted (L. s. cyanecula) bluethroat. L. s. svecica locations: Kola peninsula, Russia (Sve_Klp); Abisko, Sweden (Sve_Abi) and Krkonoše Mountains, Czech Republic (Sve_Krk). L. s. cyanecula locations are: Krkonoše Mountains, Czech Republic (Cya_Krk); Vomáčka, Czech Republic (Cya_Vmk);Třeboňsko, Czech Republic (Cya_Trb) and St. Petersburg, Russia (Cya_Stp). Inset: close-up of the populations in the Czech Republic.
Figure 2 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 2. Box plot comparison of genome-wide heterozygosity by segregating sites at subspecies level (left) and at population level (right). Mean values are marked by a horizontal bar.
Data from: A study on genetic variants of Fibroblast Growth Factor Receptor 2 (FGFR2) and the risk of breast cancer from North India
Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 (FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III (after Bonferroni correction, P<0.000694), respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk (OR = 1.645, 95%CI = 1.152–2.347, P = 0.006). In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype (OR = 1.365, 95% CI = 1.086–1.717, P = 0.008). Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.
Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer
<p>Polygenic risk score weights derived using LDpred for hypothyroidism and thyroid medication use.</p>
Genetic and Non-Genetic Breast Cancer Risk Prediction Evaluation in Indonesian Samples
ClinicalTrials.gov study NCT05570266. IPD Sharing: NO. Countries: 1. Publications: 3.
A New Clinic-Genetic Risk Score for Predicting Venous Thromboembolic Events in Cancer Patient
ClinicalTrials.gov study NCT03114618. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Genetic and Environmental Risk Factors of Type 1 Autoimmune Diabetes and Its Early Complications
ClinicalTrials.gov study NCT02212522. IPD Sharing: NO. Countries: 1. Publications: 1.
Genetic Drivers,Risk Factors and Management Strategies on Survival and Clinical Outcomes in Visceral Venous "Thrombosis"
ClinicalTrials.gov study NCT07329725. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Choices About Genetic Testing And Learning Your Risk With Smart Technology
ClinicalTrials.gov study NCT06184867. IPD Sharing: NO. Countries: 1. Publications: 0.
STudy to Assess Rapid Disease Progression by Clinical and Genetic Factors In Glaucoma patientS That Are High Risk
ClinicalTrials.gov study NCT01442896. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Using Visual Arrays to Support Understanding of Genetic Risk
ClinicalTrials.gov study NCT06994832. IPD Sharing: YES. Countries: 1. Publications: 0.
Genetic Risk for Attention Deficit Hyperactivity Disorder Expressed in Brain Functioning
ClinicalTrials.gov study NCT00143832. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Prostate Cancer Genetic Risk Evaluation and Screening Study
ClinicalTrials.gov study NCT05129605. IPD Sharing: NO. Countries: 1. Publications: 2.
Precise Stratification of Genetic Risk of Ovarian Function Impairment
ClinicalTrials.gov study NCT05665010. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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International Brain Laboratory public data
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