A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics

AbstractDespite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to multiple regression models when no interaction or haplotype effects are present. It has multiple applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by to or more regions. Using height and BMI data from the Health Retirement Study (N=7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.

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GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

Genome Biology ◽

10.1186/s13059-021-02403-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jisu Shin ◽

Sang Hong Lee

Keyword(s):

Complex Traits ◽

Error Rates ◽

Type I ◽

Phenotypic Variance ◽

Environment Interaction ◽

Summary Statistics ◽

Gxe Interaction ◽

Genome Wide ◽

Scale Data ◽

Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

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Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure

10.1101/562157 ◽

2019 ◽

Author(s):

Vincent Laville ◽

Timothy Majarian ◽

Yun J Sung ◽

Karen Schwander ◽

Mary F Feitosa ◽

...

Keyword(s):

Complex Traits ◽

Large Scale ◽

Biological Significance ◽

Genetic Effect ◽

Cell Types ◽

Interaction Effects ◽

Phenotypic Variance ◽

Genetic Associations ◽

Genetic Heritability ◽

Meta Analyses

AbstractTheCHARGE Gene-Lifestyle Interactions Working Groupis a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

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Quantifying the degree of sharing of genetic and non-genetic causes of gene expression variability across four tissues

10.1101/053355 ◽

2016 ◽

Cited By ~ 3

Author(s):

Alfonso Buil ◽

Ana Viñuela ◽

Andrew A. Brown ◽

Matthew N. Davies ◽

Ismael Padioleau ◽

...

Keyword(s):

Gene Expression ◽

Genetic Correlation ◽

Complex Traits ◽

Genetic Associations ◽

Tissue Specific ◽

Expression Variability ◽

Regulatory Interactions ◽

Shared Component ◽

In Trans ◽

Variance Explained

AbstractGene expression can provide biological mechanisms which underlie genetic associations with complex traits and diseases, but often the most relevant tissue for the trait is inaccessible and a proxy is the only alternative. Here, we investigate shared and tissue specific patterns of variability in expression in multiple tissues, to quantify the degree of sharing of causes (genetic or non-genetic) of variability in gene expression among tissues. Using gene expression in ~800 female twins from the TwinsUK cohort in skin, fat, whole blood and lymphoblastoid cell lines (LCLs), we identified 9166 significant cis-eQTLs in fat, 9551 in LCLs, 8731 in skin and 5313 in blood (1% FDR). We observed up to 80% of cis-eQTLs are shared in pairs of tissues. In addition, the cis genetic correlation between tissues is > 90% for 35% of the genes, indicating for these genes a largely tissue-shared component of cis regulation. However, variance components show that cis genetic signals explain only a small fraction of the variation in expression, with from 67–87% of the variance explained by environmental factors, and 53% of the genetic effects occurring in trans. We observe a trans genetic correlation of 0 for all genes except a few which show correlation between fat and skin expression. The environmental effects are also observed to be entirely tissue specific, despite related tissues largely sharing exposures. These results demonstrate that patterns of gene expression are largely tissue specific, strongly supporting the need to study higher order regulatory interactions in the appropriate tissue context with large samples sizes and diversity of environmental contexts.

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Improved genetic prediction of complex traits from individual-level data or summary statistics

Nature Communications ◽

10.1038/s41467-021-24485-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Qianqian Zhang ◽

Florian Privé ◽

Bjarni Vilhjálmsson ◽

Doug Speed

Keyword(s):

Complex Traits ◽

Prediction Models ◽

Phenotypic Variance ◽

Uk Biobank ◽

Genetic Prediction ◽

Individual Level ◽

Prediction Tools ◽

Data Prediction ◽

Level Data ◽

Variance Explained

AbstractMost existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.

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Loneliness Before and During the Covid-19 Pandemic: Associations with Chronic Illnesses and Relationship Quality

Innovation in Aging ◽

10.1093/geroni/igab046.2742 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 743-744

Author(s):

Jay Kayser ◽

Jacqui Smith

Keyword(s):

High Risk ◽

Relationship Quality ◽

Regression Models ◽

Chronic Illnesses ◽

The Social ◽

Final Data ◽

Multiple Regression Models ◽

Research Questions ◽

Predictive Strength ◽

Retirement Study

Abstract While self-reported loneliness generally declines after age 65, the likelihood of experiencing chronic illnesses increases. During the Covid-19 pandemic, social isolation measures have changed the social context of many people. We address three research questions: 1) What is the predictive strength of chronic illnesses, relationship quality, and their interaction on loneliness? 2) Has Covid-19 altered experienced loneliness relative to pre-pandemic? 3) Was loneliness during Covid-19 associated with the number of prior chronic illnesses in 2016? To answer these questions, we have analyzed data from participants in the Health and Retirement Study (HRS) included in the early 2020 release who also completed the 2016 wave (N = 1106). On average, in 2016, these participants were age 74.64 (SD = 6.66) and reported 2.57 (SD = 1.39) chronic illnesses. In 2016, unadjusted multiple regression models revealed that chronic illnesses (β = .38) and relationship quality (β = -.41) were associated with loneliness (R2 = .28). When covariates were added, these values were attenuated but remained statistically significant. In 2020 during the pandemic, 8% of these participants reported they often felt lonely and 26% reported feeling lonelier since the start of the Covid-19 pandemic. People who had more chronic illnesses in 2016 reported feeling lonelier in 2020 as did people whose relationships were poorer quality (p < .05). Further analyses with final data from HRS are needed to confirm these trends. These findings highlight the importance of having longitudinal information to identify individuals at high risk and most likely to benefit from interventions.

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242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

Journal of Animal Science ◽

10.1093/jas/skaa054.017 ◽

2020 ◽

Vol 98 (Supplement_3) ◽

pp. 10-11

Author(s):

Esther D McCabe ◽

Mike E King ◽

Karol E Fike ◽

Maggie J Smith ◽

Glenn M Rogers ◽

...

Keyword(s):

Multiple Regression ◽

Regression Models ◽

Average Weight ◽

Sale Price ◽

Feeder Cattle ◽

A Value ◽

Multiple Regression Models ◽

Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P < 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P < 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P < 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P < 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P < 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

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Characterization of Ultra-Thin Diamond-Like Carbon Films by SEM/EDX

Coatings ◽

10.3390/coatings11060729 ◽

2021 ◽

Vol 11 (6) ◽

pp. 729

Author(s):

Chanida Puttichaem ◽

Guilherme P. Souza ◽

Kurt C. Ruthe ◽

Kittipong Chainok

Keyword(s):

Regression Models ◽

Hard Disk Drives ◽

Carbon Films ◽

Diamond Like Carbon ◽

Disk Drives ◽

Cross Sectional ◽

Protective Films ◽

High Throughput Method ◽

Multiple Regression Models

A novel, high throughput method to characterize the chemistry of ultra-thin diamond-like carbon films is discussed. The method uses surface sensitive SEM/EDX to provide substrate-specific, semi-quantitative silicon nitride/DLC stack composition of protective films extensively used in the hard disk drives industry and at Angstrom-level. SEM/EDX output is correlated to TEM to provide direct, gauge-capable film thickness information using multiple regression models that make predictions based on film constituents. The best model uses the N/Si ratio in the films, instead of separate Si and N contributions. Topography of substrate/film after undergoing wear is correlatively and compositionally described based on chemical changes detected via the SEM/EDX method without the need for tedious cross-sectional workflows. Wear track regions of the substrate have a film depleted of carbon, as well as Si and N in the most severe cases, also revealing iron oxide formation. Analysis of film composition variations around industry-level thicknesses reveals a complex interplay between oxygen, silicon and nitrogen, which has been reflected mathematically in the regression models, as well as used to provide valuable insights into the as-deposited physics of the film.

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The Differential Effects of Caregiving Intensity on Overnight Hospitalization in the Previous 2 Years

Innovation in Aging ◽

10.1093/geroni/igaa057.249 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 76-76

Author(s):

Kylie Meyer ◽

Zachary Gassoumis ◽

Kathleen Wilber

Keyword(s):

Health Services ◽

Fixed Effects ◽

Regression Models ◽

P Value ◽

Logistic Regression Models ◽

Spousal Caregiving ◽

Health Insurance Status ◽

Race Ethnicity ◽

Self Assessed Health ◽

Retirement Study

Abstract Caregiving for a spouse is considered a major stressor many Americans will encounter during their lifetimes. Although most studies indicate caregiving is associated with experiencing diminished health outcomes, little is known about how this role affects caregivers’ use of acute health services. To understand how spousal caregiving affects the use of acute health services, we use data from the Health and Retirement Study. We apply fixed effects (FE) logistic regression models to examine odds of experiencing an overnight hospitalization in the previous two years according to caregiving status, intensity, and changes in caregiving status and intensity. Models controlled for caregiver gender, age, race, ethnicity, educational attainment, health insurance status, the number of household residents, and self-assessed health. Overall, caregivers were no more likely to experience an overnight hospitalization compared to non-caregivers (OR 0.92; CI 0.84 to 1.00; p-value=0.057). However, effects varied according to the intensity of caregiving and the time spent in this role. Compared to non-caregivers, for example, spouses who provided care to someone with no need for assistance with activities of daily living had lower odds of experiencing a hospitalization (OR 0.77; CI 0.66 to 0.89). In contrast, caregivers who provided care to someone with dementia for 4 to <6 years had 3.29 times the odds of experiencing an overnight hospitalization (CI 1.04 to 10.38; p-value=0.042). Findings indicate that, although caregivers overall appear to use acute health services about as much as non-caregivers, large differences exist between caregivers. Results emphasize the importance of recognizing diversity within caregiving experiences.

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The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

Genetics Research ◽

10.1017/s0016672311000097 ◽

2011 ◽

Vol 93 (3) ◽

pp. 203-219 ◽

Cited By ~ 51

Author(s):

KATHRYN E. KEMPER ◽

DAVID L. EMERY ◽

STEPHEN C. BISHOP ◽

HUTTON ODDY ◽

BENJAMIN J. HAYES ◽

...

Keyword(s):

Complex Traits ◽

Sampling Error ◽

Snp Markers ◽

Snp Marker ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Phenotypic Variance ◽

Breeding Values ◽

Genetic Merit ◽

Independent Population

SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0·48% (T. colubriformis) or 0·08% (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0·32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

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