Improved estimation of SNP heritability using Bayesian multiple-phenotype models

Sample Sizes ◽

Narrow Sense Heritability ◽

Single Nucleotide ◽

Multiple Phenotype ◽

Expression Pathways ◽

Small Sample Sizes ◽

Boundary Estimates

AbstractLinear mixed models (LMM) are widely used to estimate narrow sense heritability explained by tagged single-nucleotide polymorphisms (SNPs). However, those estimates are valid only if large sample sizes are used. We propose a Bayesian matrix-variate model that takes into account the genetic correlation among phenotypes and genetic correlation among individuals. The use of multivariate Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP-based heritability estimates over univariate and likelihood-based methods, thus explaining why recent progress in eQTL identification has been limited.

Genetic Factors of Nitric Oxide’s System in Psychoneurologic Disorders

International Journal of Molecular Sciences ◽

10.3390/ijms21051604 ◽

2020 ◽

Vol 21 (5) ◽

pp. 1604 ◽

Cited By ~ 1

Author(s):

Regina F. Nasyrova ◽

Polina V. Moskaleva ◽

Elena E. Vaiman ◽

Natalya A. Shnayder ◽

Nataliya L. Blatt ◽

...

Keyword(s):

Nitric Oxide ◽

Neurological Diseases ◽

Small Sample ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Penile Erections ◽

Genetic And Environmental Factors ◽

Small Sample Sizes ◽

Disease Modifying Treatment

According to the recent data, nitric oxide (NO) is a chemical messenger that mediates functions such as vasodilation and neurotransmission, as well as displaying antimicrobial and antitumoral activities. NO has been implicated in the neurotoxicity associated with stroke and neurodegenerative diseases; neural regulation of smooth muscle, including peristalsis; and penile erections. We searched for full-text English publications from the past 15 years in Pubmed and SNPedia databases using keywords and combined word searches (nitric oxide, single nucleotide variants, single nucleotide polymorphisms, genes). In addition, earlier publications of historical interest were included in the review. In our review, we have summarized information regarding all NOS1, NOS2, NOS3, and NOS1AP single nucleotide variants (SNVs) involved in the development of mental disorders and neurological diseases/conditions. The results of the studies we have discussed in this review are contradictory, which might be due to different designs of the studies, small sample sizes in some of them, and different social and geographical characteristics. However, the contribution of genetic and environmental factors has been understudied, which makes this issue increasingly important for researchers as the understanding of these mechanisms can support a search for new approaches to pathogenetic and disease-modifying treatment.

Genetic predictors for stroke in children with sickle cell anemia

Blood ◽

10.1182/blood-2011-01-332205 ◽

2011 ◽

Vol 117 (24) ◽

pp. 6681-6684 ◽

Cited By ~ 78

Author(s):

Jonathan M. Flanagan ◽

Denise M. Frohlich ◽

Thad A. Howard ◽

William H. Schultz ◽

Catherine Driscoll ◽

...

Keyword(s):

Sickle Cell ◽

Sickle Cell Anemia ◽

Stroke Risk ◽

Small Sample ◽

Additional Patient ◽

Genetic Modifiers ◽

Single Nucleotide ◽

Genetic Predictors ◽

Abstract Stroke is a devastating complication of sickle cell anemia (SCA), affecting 5% to 10% of patients before adulthood. Several candidate genetic polymorphisms have been proposed to affect stroke risk, but few have been validated, mainly because previous studies were hampered by relatively small sample sizes and the absence of additional patient cohorts for validation testing. To verify the accuracy of proposed genetic modifiers influencing stroke risk in SCA, we performed genotyping for 38 published single nucleotide polymorphisms (SNPs), as well as α-thalassemia, G6PD A− variant deficiency, and β-globin haplotype in 2 cohorts of children with well-defined stroke phenotypes (130 stroke, 103 nonstroke). Five polymorphisms had significant influence (P < .05): SNPs in the ANXA2, TGFBR3, and TEK genes were associated with increased stroke risk, whereas α-thalassemia and a SNP in the ADCY9 gene were linked with decreased stroke risk. Further investigation at these genetic regions may help define mutations that confer stroke risk or protection in children with SCA.

Genetic Factors of Nitric Oxide’s System in Psychoneurologic Disorders

10.20944/preprints202002.0048.v1 ◽

2020 ◽

Author(s):

Regina F. Nasyrova ◽

Polina V. Moskaleva ◽

Elene E. Vaiman ◽

Natalya A. Shnayder ◽

Nataliya L. Blatt ◽

...

Keyword(s):

Nitric Oxide ◽

Neurological Diseases ◽

Small Sample ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Genetic And Environmental Factors ◽

Small Sample Sizes ◽

Disease Modifying Treatment ◽

Geographical Characteristics

According to the recent data, nitric oxide (NO) is a chemical messenger that mediates functions such as vasodilation and neurotransmission, it also possesses antimicrobial and antitumoral activities. Nitric oxide has been implicated in neurotoxicity associated with stroke and neurodegenerative diseases, neural regulation of smooth muscle, including peristalsis, and penile erection. We searched for full-text English publications in Pubmed and SNPedia databases using keywords and combined word searches (nitric oxide, single nucleotide variants, single nucleotide polymorphisms, genes) over the past 15 years. In addition, earlier publications of historical interest were included in the review. In our review, we have sum up all NOS1, NOS2, NOS3, and NOS1AP single nucleotide variants (SNVs) involved in the development of mental disorders and neurological diseases/conditions. The results of studies we have discussed in this review are contradictory, that might be due to different designs of the studies, small sample sizes in some of them, as well as different social and geographical characteristics. However, the contribution of genetic and environmental factors has been understudied, that makes this issue increasing for researchers as the understanding of these mechanisms can support a search for new approaches to pathogenetic and disease-modifying treatment.

Sweeps in time: leveraging the joint distribution of branch lengths

10.1101/2021.01.27.428367 ◽

2021 ◽

Author(s):

Gertjan Bisschop ◽

Konrad Lohse ◽

Derek Setter

Keyword(s):

Marginal Distribution ◽

Sequence Data ◽

Theoretical Models ◽

Small Sample ◽

Analytic Framework ◽

Single Nucleotide ◽

Branch Lengths ◽

Polymorphism Data ◽

AbstractCurrent methods of identifying positively selected regions of the genome are limited by their underlying model in two key ways: the model cannot account for the timing of the adaptive event and the analytic predictions are limited to single nucleotide polymorphisms. Here we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of the adaptive event. In addition, our framework allows us to go beyond simple polymorphism data. We are able to leverage information contained in patterns of linked variants, and even with very small sample sizes, our analytic framework has high power to identify historically adaptive regions of the genome and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between current theoretical models to recent advances in simulation procedures that have allowed researchers both to examine the evolution of genealogical histories at the level of full chromosomes and build methods that attempt to reconstruct full ancestries from genome sequence data.

Problems with small sample sizes in psychophysiological research

PsycEXTRA Dataset ◽

10.1037/e526132012-267 ◽

1996 ◽

Author(s):

Todd C. Riniolo ◽

Stephen W. Porges

Keyword(s):

Small Sample ◽

Sample Sizes ◽

Psychophysiological Research ◽

Bayesian Latent Growth Mixture-Modeling With Small Sample Sizes

PsycEXTRA Dataset ◽

10.1037/e568142014-001 ◽

2014 ◽

Author(s):

Sarah Depaoli

Keyword(s):

Growth Mixture Modeling ◽

Mixture Modeling ◽

Small Sample ◽

Sample Sizes ◽

Latent Growth ◽

Growth Mixture ◽

Latent Growth Mixture Modeling ◽

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

10.31234/osf.io/mvn9b ◽

2018 ◽

Cited By ~ 1

Author(s):

Christopher Chabris ◽

Patrick Ryan Heck ◽

Jaclyn Mandart ◽

Daniel Jacob Benjamin ◽

Daniel J. Simons

Keyword(s):

Null Hypothesis ◽

Small Sample ◽

Sample Sizes ◽

Double Blind ◽

Bayesian Analyses ◽

Physical Warmth ◽

Small Sample Sizes ◽

Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

Scientific Reports ◽

10.1038/s41598-021-81110-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Florent Le Borgne ◽

Arthur Chatton ◽

Maxime Léger ◽

Rémi Lenain ◽

Yohann Foucher

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Statistical Power ◽

Small Sample ◽

Causal Effects ◽

Small Samples ◽

Support Vector ◽

Sample Sizes ◽

Super Learner ◽

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.