scholarly journals Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects

Genetics ◽  
2021 ◽  
Author(s):  
Jonas Wallin ◽  
Małgorzata Bogdan ◽  
Piotr A Szulc ◽  
R W Doerge ◽  
David O Siegmund
Keyword(s):  
Recombinant Inbred Lines ◽  
Simultaneous Detection ◽  
Selection Strategy ◽  
Strongly Correlated ◽  
Mixed Effect ◽  
Novel Approach ◽  
Effect Model ◽  
Multiple Qtl ◽  
Sample Correlation ◽  
False Discoveries

Abstract Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection.

scholarly journals Ghost QTL and hotspots in experimental crosses - novel solution by mixed model with nonzero mean

2019 ◽  
Author(s):  
Piotr A. Szulc ◽  
Jonas Wallin ◽  
Małgorzata Bogdan ◽  
R.W. Doerge ◽  
David O. Siegmund
Keyword(s):  
Mixed Model ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Simultaneous Detection ◽  
Correlation Structure ◽  
Selection Strategy ◽  
Polygenic Effect ◽  
Mixed Effect ◽  
Sample Correlation ◽  
Nonzero Mean

Abstract“Ghost-QTL” are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome which are strongly correlated with the summary polygenic effect depend on a specific sample correlation structure determined by the genotype at all loci. During the analysis of e-QTL data or recombinant inbred lines this correlation structure is preserved for all traits under consideration, and may lead to the so called “hot-spots” via the detection of the summary polygenic effect at exactly the same positions for most of the considered traits. We illustrate that the problem can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach allows to eliminate ghost-QTL/false hot spot effects, while preserving a high power of detection of true QTL effects.

scholarly journals Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population

2019 ◽  
Author(s):  
Guilherme da Silva Pereira ◽  
Dorcus C. Gemenet ◽  
Marcelo Mollinari ◽  
Bode A. Olukolu ◽  
Joshua C. Wood ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Ipomoea Batatas ◽  
Random Effect ◽  
Random Effect Model ◽  
Phenotypic Data ◽  
Mixed Effect ◽  
Gene Search ◽  
Effect Model ◽  
Multiple Qtl ◽  
Model Approach

ABSTRACTIn developing countries, the sweetpotato,Ipomoea batatas(L.) Lam. (2n= 6x= 90), is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-effect models can only fit a single QTL and are generally hard to interpret. Here we report the use of a random-effect model approach to map multiple QTL based on score statistics in a sweetpotato bi-parental population (‘Beauregard’בTanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly predicted means were obtained using mixed-effect models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every cM position. Multiple interval mapping was performed using our R package QTLPOLY and detected a total of 41 QTL, ranging from one to ten QTL per trait. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits and provided basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions allowed us to characterize additive allele effects as well as to compute QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.

scholarly journals Multiple QTL Mapping in Autopolyploids: A Random-Effect Model Approach with Application in a Hexaploid Sweetpotato Full-Sib Population

Genetics ◽  
2020 ◽  
Vol 215 (3) ◽  
pp. 579-595 ◽  
Author(s):  
Guilherme da Silva Pereira ◽  
Dorcus C. Gemenet ◽  
Marcelo Mollinari ◽  
Bode A. Olukolu ◽  
Joshua C. Wood ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Ipomoea Batatas ◽  
Random Effect ◽  
Random Effect Model ◽  
Phenotypic Data ◽  
Mixed Effect ◽  
Gene Search ◽  
Effect Model ◽  
Multiple Qtl ◽  
Model Approach

In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. (2n=6x=90), is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-effect models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-effect model approach to map multiple QTL based on score statistics in a sweetpotato biparental population (‘Beauregard’ × ‘Tanzania’) with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-effect models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

scholarly journals Altered speech patterns in subjects with post-traumatic headache due to mild traumatic brain injury

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Catherine D. Chong ◽  
Jianwei Zhang ◽  
Jing Li ◽  
Teresa Wu ◽  
Gina Dumkrieger ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Speech Production ◽  
Mild Traumatic Brain Injury ◽  
Mobile Application ◽  
Speaking Rate ◽  
Mixed Effect ◽  
Effect Model ◽  
Vowel Space ◽  
Post Traumatic

Abstract Background/objective Changes in speech can be detected objectively before and during migraine attacks. The goal of this study was to interrogate whether speech changes can be detected in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI) and whether there are within-subject changes in speech during headaches compared to the headache-free state. Methods Using a series of speech elicitation tasks uploaded via a mobile application, PTH subjects and healthy controls (HC) provided speech samples once every 3 days, over a period of 12 weeks. The following speech parameters were assessed: vowel space area, vowel articulation precision, consonant articulation precision, average pitch, pitch variance, speaking rate and pause rate. Speech samples of subjects with PTH were compared to HC. To assess speech changes associated with PTH, speech samples of subjects during headache were compared to speech samples when subjects were headache-free. All analyses were conducted using a mixed-effect model design. Results Longitudinal speech samples were collected from nineteen subjects with PTH (mean age = 42.5, SD = 13.7) who were an average of 14 days (SD = 32.2) from their mTBI at the time of enrollment and thirty-one HC (mean age = 38.7, SD = 12.5). Regardless of headache presence or absence, PTH subjects had longer pause rates and reductions in vowel and consonant articulation precision relative to HC. On days when speech was collected during a headache, there were longer pause rates, slower sentence speaking rates and less precise consonant articulation compared to the speech production of HC. During headache, PTH subjects had slower speaking rates yet more precise vowel articulation compared to when they were headache-free. Conclusions Compared to HC, subjects with acute PTH demonstrate altered speech as measured by objective features of speech production. For individuals with PTH, speech production may have been more effortful resulting in slower speaking rates and more precise vowel articulation during headache vs. when they were headache-free, suggesting that speech alterations were related to PTH and not solely due to the underlying mTBI.

scholarly journals Limited not lazy: a quasi-experimental secondary analysis of evidence quality evaluations by those who hold implausible beliefs

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Kristy A. Martire ◽  
Bethany Growns ◽  
Agnes S. Bali ◽  
Bronte Montgomery-Farrer ◽  
Stephanie Summersby ◽  
...  
Keyword(s):  
Secondary Analysis ◽  
Past Research ◽  
Linear Mixed Effect Model ◽  
High Quality ◽  
Mixed Effect ◽  
High Quality Evidence ◽  
Effect Model ◽  
Evidence Quality ◽  
Quasi Experimental ◽  
Quality Evidence

AbstractPast research suggests that an uncritical or ‘lazy’ style of evaluating evidence may play a role in the development and maintenance of implausible beliefs. We examine this possibility by using a quasi-experimental design to compare how low- and high-quality evidence is evaluated by those who do and do not endorse implausible claims. Seven studies conducted during 2019–2020 provided the data for this analysis (N = 746). Each of the seven primary studies presented participants with high- and/or low-quality evidence and measured implausible claim endorsement and evaluations of evidence persuasiveness (via credibility, value, and/or weight). A linear mixed-effect model was used to predict persuasiveness from the interaction between implausible claim endorsement and evidence quality. Our results showed that endorsers were significantly more persuaded by the evidence than non-endorsers, but both groups were significantly more persuaded by high-quality than low-quality evidence. The interaction between endorsement and evidence quality was not significant. These results suggest that the formation and maintenance of implausible beliefs by endorsers may result from less critical evidence evaluations rather than a failure to analyse. This is consistent with a limited rather than a lazy approach and suggests that interventions to develop analytical skill may be useful for minimising the effects of implausible claims.

scholarly journals Association between Anxiety Levels and Weight Change in the Multiethnic Study of Atherosclerosis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Katherine Rieke ◽  
Ramon Durazo-Arvizu ◽  
Kiang Liu ◽  
Erin D. Michos ◽  
Amy Luke ◽  
...  
Keyword(s):  
Weight Change ◽  
Inclusion Criteria ◽  
Mixed Effect ◽  
Effect Model ◽  
Lower Baseline ◽  
Multiethnic Cohort ◽  
Baseline Weight ◽  
Study Population ◽  
The Relationship

Objective. To examine the association between anxiety and weight change in a multiethnic cohort followed for approximately 10 years.Methods. The study population consisted of participants of the multiethnic study of atherosclerosis who met specified inclusion criteria (n= 5,799). Weight was measured at baseline and four subsequent follow-up exams. Anxiety was analyzed as sex-specific anxiety quartiles (QANX). The relationship between anxiety level and weight change was examined using a mixed-effect model with weight as the dependent variable, anxiety and time as the independent variables, and adjusted for covariates.Results. Average annual weight change (range) was −0.17 kg (−6.04 to 4.38 kg) for QANX 1 (lowest anxiety), −0.16 kg (−10.71 to 4.45 kg) for QANX 2, −0.15 kg (−8.69 to 6.39 kg) for QANX 3, and −0.20 kg (−7.12 to 3.95 kg) for QANX 4 (highest anxiety). No significant association was noted between QANX and weight change. However, the highest QANX was associated with a −2.48 kg (95% CI = −3.65, −1.31) lower baseline weight compared to the lowest QANX after adjustment for all covariates.Conclusions. Among adults, age 45–84, higher levels of anxiety, defined by the STPI trait anxiety scale, are associated with lower average baseline weight but not with weight change.

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