Joint Effects Of Genetic Factors Research Articles

EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits.

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g. the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. Supplementary data are available at Bioinformatics online.

Differential effects of stress and African ancestry on preterm birth and related traits among US born and immigrant Black mothers.

Preterm birth (PTB, <37 weeks of gestation) is influenced by a wide range of environmental, genetic and psychosocial factors, and their interactions. However, the individual and joint effects of genetic factors and psychosocial stress on PTB have remained largely unexplored among U.S. born versus immigrant mothers.We studied 1121 African American women from the Boston Birth Cohort enrolled from 1998 to 2008. Regression-based analyses were performed to examine the individual and joint effects of genetic ancestry and stress (including lifetime stress [LS] and stress during pregnancy [PS]) on PTB and related traits among U.S. born and immigrant mothers.Significant associations between LS and PTB and related traits were found in the total study population and in immigrant mothers, including gestational age, birthweight, PTB, and spontaneous PTB; but no association was found in U.S. born mothers. Furthermore, significant joint associations of LS (or PS) and African ancestral proportion (AAP) on PTB were found in immigrant mothers, but not in U.S. born mothers.Although, overall, immigrant women had lower rates of PTB compared to U.S. born women, our study is one of the first to identify a subset of immigrant women could be at significantly increased risk of PTB and related outcomes if they have high AAP and are under high LS or PS. In light of the growing number of immigrant mothers in the U.S., our findings may have important clinical and public health implications.

Assessing Susceptibility to Age-Related Macular Degeneration With Genetic Markers and Environmental Factors

To evaluate the independent and joint effects of genetic factors and environmental variables on advanced forms of age-related macular degeneration (AMD), including geographic atrophy and choroidal neovascularization, and to develop a predictive model with genetic and environmental factors included. Demographic information, including age at onset, smoking status, and body mass index, was collected for 1844 participants. Genotypes were evaluated for 8 variants in 5 genes related to AMD. Unconditional logistic regression analyses were performed to generate a risk predictive model. All genetic variants showed a strong association with AMD. Multivariate odds ratios were 3.52 (95% confidence interval, 2.08-5.94) for complement factor H, CFH rs1061170 CC, 4.21 (2.30-7.70) for CFH rs2274700 CC, 0.46 (0.27-0.80) for C2 rs9332739 CC/CG, 0.44 (0.30-0.66) for CFB rs641153 TT/CT, 10.99 (6.04-19.97) for HTRA1/LOC387715 rs10490924 TT, and 2.66 (1.43-4.96) for C3 rs2230199 GG. Smoking was independently associated with advanced AMD after controlling for age, sex, body mass index, and all genetic variants. CFH confers more risk to the bilaterality of geographic atrophy, whereas HTRA1/LOC387715 contributes more to the bilaterality of choroidal neovascularization. C3 confers more risk for geographic atrophy than choroidal neovascularization. Risk models with combined genetic and environmental factors have notable discrimination power. Early detection and risk prediction of AMD could help to improve the prognosis of AMD and to reduce the outcome of blindness. Targeting high-risk individuals for surveillance and clinical interventions may help reduce disease burden.

Chromosomal regions involved in hybrid performance and heterosis: their AFLP®-based identification and practical use in prediction models

In this paper, a novel approach towards the prediction of hybrid performance and heterosis is presented. Here, we describe an approach based on: (i) the assessment of associations between AFLP(R) markers and hybrid performance and specific combining ability (SCA) across a set of hybrids; and (ii) the assumption that the joint effect of genetic factors (loci) determined this way can be obtained by addition. Estimated gene effects for grain yield varied from additive, partial dominance to overdominance. This procedure was applied to 53 interheterotic hybrids out of a 13 by 13 half-diallel among maize inbreds, evaluated for grain yield. The hybrid value, representing the joint effect of the genetic factors, accounted for up to 62.4% of the variation in the hybrid performance observed, whereas the corresponding efficiency of the SCA model was 36.8%. Efficiency of the prediction for hybrid performance was evaluated by means of a cross-validation procedure for grain yield of (i) the 53 interheterotic hybrids and (ii) 16 hybrids partly related to the 13 by 13 half-diallel. Comparisons in prediction efficiency with the 'distance' model were made. Because the map position of the selected markers is known, putative quantitative trait loci (QTL) affecting grain yield, in terms of hybrid performance or heterosis, are identified. Some QTL of grain yield detected in the present study were located in the vicinity of loci reported earlier as having quantitative effects on grain yield.