An Empirical Method for Establishing Positional Confidence Intervals Tailored for Composite Interval Mapping of QTL

BACKGROUND: Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods. RESULTS: Based on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high. CONCLUSIONS: Extensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved by accounting for the procedural complexity of CIM via an empirical approach, CIM-NPCI. Confidence intervals are a critical measure of QTL utility, but have received inadequate treatment due to a perception that QTL mapping is not sufficiently precise for procedural improvements to matter. Technological advances will continue to challenge this assumption, creating even more need for the current improvement to be refined

The HIV Genomic Incidence Assay Meets False Recency Rate and Mean Duration of Recency Infection Performance Standards.

HIV incidence is a primary metric for epidemic surveillance and prevention efficacy assessment. HIV incidence assay performance is evaluated via false recency rate (FRR) and mean duration of recent infection (MDRI). We conducted a meta-analysis of 438 incident and 305 chronic specimens' HIV envelope genes from a diverse global cohort. The genome similarity index (GSI) accurately characterized infection stage across diverse host and viral factors. All except one chronic specimen had GSIs below 0.67, yielding a FRR of 0.33 [0-0.98] %. We modeled the incidence assay biomarker dynamics with a logistic link function assuming individual variabilities in a Beta distribution. The GSI probability density function peaked close to 1 in early infection and 0 around two years post infection, yielding MDRI of 420 [361, 467] days. We tested the assay by newly sequencing 744 envelope genes from 59 specimens of 21 subjects who followed from HIV negative status. Both standardized residuals and Anderson-Darling tests showed that the test dataset was statistically consistent with the model biomarker dynamics. This is the first reported incidence assay meeting the optimal FRR and MDRI performance standards. Signatures of HIV gene diversification can allow precise cross-sectional surveillance with a desirable temporal range of incidence detection

Genetic variation in FADS genes is associated with maternal long-chain PUFA status but not with cognitive development of infants in a high fish-eating observational study

AbstractLong-chain n-6 and n-3 PUFA (LC-PUFA), arachidonic acid (AA) (20:4n-6) and DHA (22:6n-3), are critical for optimal brain development. These fatty acids can be consumed directly from the diet, or synthesized endogenously from precursor PUFA by Δ-5 (encoded by FADS1) and Δ-6 desaturases (encoded by FADS2). The aim of this study was to determine the potential importance of maternal genetic variability in FADS1 and FADS2 genes to maternal LC-PUFA status and infant neurodevelopment in populations with high fish intakes. The Nutrition Cohorts 1 (NC1) and 2 (NC2) are longitudinal observational mother-child cohorts in the Republic of Seychelles. Maternal serum LC-PUFA was measured at 28 weeks gestation and genotyping for rs174537 (FADS1), rs174561 (FADS1), rs3834458 (FADS1-FADS2) and rs174575 (FADS2) was performed in both cohorts. The children completed the Bayley Scales of Infant Development II (BSID-II) at 30 months in NC1 and at 20 months in NC2. Complete data were available for 221 and 1310 mothers from NC1 and NC2 respectively. With increasing number of rs3834458 minor alleles, maternal concentrations of AA were significantly decreased (NC1 p=0.004; NC2 p<0.001) and precursor:product ratios for linoleic acid (LA) (18:2n-6)-to-AA (NC1 p<0.001; NC2 p<0.001) and α-linolenic acid (ALA) (18:3n-3)-to-DHA were increased (NC2 p=0.028). There were no significant associations between maternal FADS genotype and BSID-II scores in either cohort. A trend for improved PDI was found among infants born to mothers with the minor rs3834458 allele.In these high fish-eating cohorts, genetic variability in FADS genes was associated with maternal AA status measured in serum and a subtle association of the FADS genotype was found with neurodevelopment