scholarly journals Testing the accuracy of 3D automatic landmarking via genome-wide association studies

2021 ◽  
Author(s):  
Yoland Savriama ◽  
Diethard Tautz
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Careful Consideration ◽  
Genome Wide Association Studies ◽  
Automatic Method ◽  
Geometric Morphometric ◽  
Manual Adjustment ◽  
Genome Wide ◽  
Morphometric Methods ◽  
Automatic Phenotyping

Abstract Various advances in 3D automatic phenotyping and landmark-based geometric morphometric methods have been made. While it is generally accepted that automatic landmarking compromises the capture of the biological variation, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to extract an actual biological signal adequately. Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semi-automatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. We find that most significant SNPs and QTLs are not recovered based on the data derived from the automatic landmarking method. Our results thus confirm the notion that information is lost in the automated landmarking procedure although somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question.

scholarly journals Testing the accuracy of 3D automatic landmarking via genome-wide association studies

2021 ◽  
Author(s):  
Yoland Savriama ◽  
Diethard Tautz
Keyword(s):  
Association Studies ◽  
Careful Consideration ◽  
Genome Wide Association Studies ◽  
Automatic Method ◽  
Geometric Morphometric ◽  
Manual Adjustment ◽  
Genome Wide ◽  
Morphometric Analyses ◽  
Cautious Interpretation ◽  
Automatic Phenotyping

Background: Various advances in 3D automatic phenotyping and particularly in landmark-based geometric morphometric methods have been made, but only a few studies have tested the reliability of such automatic procedures in morphometric analyses. It is generally accepted that automatic landmarking compromises the capture of the actual biological variation, and this not only affects its performance to effectively detect differences among sample means but also the structure of covariance matrices. However, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to capture an actual biological signal adequately. Results: Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semi-automatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. In the univariate mapping, the automatic approach failed to recover the same SNPs and found only 1 out of 17 previously identified QTLs for the skull, but found one new QTL. Similarly, for the lower jaws, the automatic approach failed to recover the same SNPs but found 2 neighbouring SNPs for 1 out of 8 previously identified QTLs. For centroid size, the same general results were recovered by the automatic method for both the skull and lower jaws, with the same peak SNP being found for the lower jaws. In the multivariate mapping, the automatic approach did not detect the same markers nor QTLs having their regions overlapping with the ones identified with the semi-automatic procedure for the skull, while the same marker, which is also the peak SNP and sole QTL, was recovered by the automatic pipeline for lower jaws. Conclusion: Our results confirm the notion that information is lost in the automated landmarking procedure but somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question and the cautious interpretation of their results.

scholarly journals Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

2015 ◽  
Author(s):  
Dominic Holland ◽  
Yunpeng Wang ◽  
Wesley K Thompson ◽  
Andrew Schork ◽  
Chi-Hua Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Sample Sizes ◽  
Genetic Components ◽  
Complex Phenotypes ◽  
Genome Wide ◽  
Z Scores

Genome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities that does not require raw genotype data, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype -- the proportion of SNPs (after uniform pruning, so that large LD blocks are not over-represented) likely to be in strong LD with causal/mechanistically associated SNPs -- and predicting the proportion of chip heritability explainable by genome wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and additionally, for purposes of illustration, putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We estimate the degree to which effect sizes are over-estimated when based on linear regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

scholarly journals Genome-Wide Association Studies for the Concentration of Albumin in Colostrum and Serum in Chinese Holstein

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2211
Author(s):  
Shan Lin ◽  
Zihui Wan ◽  
Junnan Zhang ◽  
Lingna Xu ◽  
Bo Han ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Albumin Concentration ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Chinese Holstein ◽  
Genome Wide ◽  
Chinese Holstein Cows ◽  
Newborn Calves

Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips (containing 140,668 single nucleotide polymorphisms; SNPs). After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs (false discovery rate (FDR) at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.

scholarly journals Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants

2019 ◽  
Author(s):  
Daniel B. Rosoff ◽  
Toni-Kim Clarke ◽  
Mark J. Adams ◽  
Andrew M. McIntosh ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Educational Attainment ◽  
Alcohol Dependence ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Genome Wide ◽  
Genetic Instruments

Abstract Observational studies suggest that lower educational attainment (EA) may be associated with risky alcohol use behaviors; however, these findings may be biased by confounding and reverse causality. We performed two-sample Mendelian randomization (MR) using summary statistics from recent genome-wide association studies (GWAS) with >780,000 participants to assess the causal effects of EA on alcohol use behaviors and alcohol dependence (AD). Fifty-three independent genome-wide significant SNPs previously associated with EA were tested for association with alcohol use behaviors. We show that while genetic instruments associated with increased EA are not associated with total amount of weekly drinks, they are associated with reduced frequency of binge drinking ≥6 drinks (ßIVW = −0.198, 95% CI, −0.297 to –0.099, PIVW = 9.14 × 10−5), reduced total drinks consumed per drinking day (ßIVW = −0.207, 95% CI, −0.293 to –0.120, PIVW = 2.87 × 10−6), as well as lower weekly distilled spirits intake (ßIVW = −0.148, 95% CI, −0.188 to –0.107, PIVW = 6.24 × 10−13). Conversely, genetic instruments for increased EA were associated with increased alcohol intake frequency (ßIVW = 0.331, 95% CI, 0.267–0.396, PIVW = 4.62 × 10−24), and increased weekly white wine (ßIVW = 0.199, 95% CI, 0.159–0.238, PIVW = 7.96 × 10−23) and red wine intake (ßIVW = 0.204, 95% CI, 0.161–0.248, PIVW = 6.67 × 10−20). Genetic instruments associated with increased EA reduced AD risk: an additional 3.61 years schooling reduced the risk by ~50% (ORIVW = 0.508, 95% CI, 0.315–0.819, PIVW = 5.52 × 10−3). Consistency of results across complementary MR methods accommodating different assumptions about genetic pleiotropy strengthened causal inference. Our findings suggest EA may have important effects on alcohol consumption patterns and may provide potential mechanisms explaining reported associations between EA and adverse health outcomes.

scholarly journals Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

2020 ◽  
Author(s):  
Ali Jalil Sarghale ◽  
Mohammad Moradi Shahrebabak ◽  
Hossein Moradi Shahrebabak ◽  
Ardeshir Nejati Javaremi ◽  
Mahdi Saatchi ◽  
...  
Keyword(s):  
Methane Emission ◽  
Feed Intake ◽  
Volatile Fatty Acids ◽  
Sequence Data ◽  
Association Studies ◽  
Olfactory Receptors ◽  
Significant Snps ◽  
Valeric Acid ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background: Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help the livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle. Results: Based on the significant-association threshold (p < 5 × 10−8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n=2), PME per kg fat (n=14), and valeric acid (n=17). Besides, 69 genes were identified for valeric acid (n=18), PME per kg milk (n=4) and PME per kg fat (n=47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10-2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10-10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs. Conclusions: Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to food preference and feed intake. Identified SNPs in this study were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of the reference population required in genome-wide association studies and genomic selection.

scholarly journals Genome-wide Identification of Powdery Mildew Resistance in Common Bean

2020 ◽  
Author(s):  
Papias Hongera Binagwa ◽  
Sy M. Traore ◽  
Marceline Egnin ◽  
Gregory C. Bernard ◽  
Inocent Ritte ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Common Bean ◽  
Resistance Genes ◽  
Agronomic Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Resistance Protein ◽  
Resistance Protein

Abstract Background: Genome-wide association studies (GWAS) was utilized to detect genetic variations related to the powdery mildew (PM) resistance and several agronomic traits in common bean. However, its application in common bean and the PM interactions to identify genes and their location in the common bean genome has not been fully addressed. Results: Genome-wide association studies (GWAS) through marker-trait association are useful molecular tools for the identification of disease resistance and other agronomic traits. SNP genotyping with a BeadChip containing 5398 SNPs was used to detect genetic variations related to resistance to PM disease in a panel of 206 genotypes grown under field conditions for two consecutive years. Significant SNPs identified on chromosomes 4 and 10 (Pv04 and Pv10) were repeatable, confirming the reliability of the phenotypic data scored from the genotypes grown in two locations within two years. A cluster of resistance genes was revealed on the chromosome 4 of common bean genome among which CNL and TNL like resistance genes were identified. Furthermore, two resistance genes Phavu_010G1320001g and Phavu_010G136800g were also identified on Pv10; further sequence analysis showed that these genes were homologs to the Arabidopsis disease resistance protein (RLM1A-like) and the putative disease resistance protein (At4g11170.1), respectively. Two LRR receptor-like kinases (RLK) were also identified on Pv11 in samples collected in 2018 only. Many genes encoding auxin-responsive protein, TIFY10A protein, growth-regulating factor 5-like, ubiquitin-like protein, cell wall protein RBR3-like protein related to PM resistance were identified nearby significant SNPs. These results suggested that the resistance to PM pathogen involves a network of many genes constitutively co-expressed and may generate several layers of defense barriers or inducible reactions.Conclusion: Our results provide new insights into common bean and PM interactions, and revealed putative resistance genes as well as their location on common bean genome that could be used for marker-assisted selection, functional genomic study approaches to confirm the role of these putative genes; hence, developing common bean resistance lines to the PM disease.

scholarly journals A Comprehensive Genome-wide and Phenome-wide Examination of BMI and Obesity in a Northern Nevadan Cohort

2019 ◽  
Author(s):  
Karen A. Schlauch ◽  
Robert W. Read ◽  
Vincent C. Lombardi ◽  
Gai Elhanan ◽  
William J Metcalf ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Extreme Obesity ◽  
Illumina Platform ◽  
Health Records ◽  
Genome Wide ◽  
Obese Class ◽  
Novel Variants

AbstractThe aggregation of Election Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS excludes DM2-diagnosed individuals and focuses on associations with BMI exclusively. The second GWAS examines the interplay between Type 2 Diabetes (DM2) and BMI. The intersection of significant variants of these two studies is surprising. The third complementary case-control GWAS, with cases defined as extremely obese (Class 2 or 3 obesity), identifies strong associations with extreme obesity, including established variants in theFTOandNEGR1genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

scholarly journals The power of a multivariate approach to genome-wide association studies: an example with Drosophila melanogaster wing shape

2017 ◽  
Author(s):  
William Pitchers ◽  
Jessica Nye ◽  
Eladio J. Márquez ◽  
Alycia Kowalski ◽  
Ian Dworkin ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Significant Snps ◽  
Wing Shape ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Phenotypic Effect ◽  
Genome Wide ◽  
Univariate Analyses

AbstractDue to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and more informative than a series of univariate analyses. In most cases, however, studies of genotype-phenotype relationships have analyzed only one trait at a time, even as the rapid advances in molecular tools have expanded our view of the genotype to include whole genomes. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different labs. We found 2,396 significant SNPs using a 5% FDR cutoff in the multivariate analyses, but just 4 significant SNPs in univariate analyses of scores on the first 20 principal component axes. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. Exploiting this feature, we show that the directions of effects were on average replicable in an unrelated panel of inbred lines. Effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. Association studies that take a phenomic approach in considering many traits simultaneously are an important complement to the power of genomics. Multivariate analyses of such data are more powerful, more informative, and allow the unbiased study of pleiotropy.

scholarly journals A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort

2019 ◽  
Vol 10 (2) ◽  
pp. 645-664 ◽  
Author(s):  
Karen A. Schlauch ◽  
Robert W. Read ◽  
Vincent C. Lombardi ◽  
Gai Elhanan ◽  
William J. Metcalf ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Extreme Obesity ◽  
Illumina Platform ◽  
Genome Wide ◽  
Type 2 Diabetics ◽  
Obese Class ◽  
Novel Variants

The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

scholarly journals Quantifying genetic heterogeneity between continental populations for human height and body mass index

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Guo ◽  
Andrew Bakshi ◽  
Ying Wang ◽  
Longda Jiang ◽  
Loic Yengo ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Complex Trait ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Population Difference ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ($$r_{g}$$ r g ) or genome-wide significant SNPs ($$r_{{g\left( {GWS} \right)}}$$ r g GWS ) for height and body mass index (BMI) in samples of European (EUR; $$n = 49,839$$ n = 49 , 839 ) and African (AFR; $$n = 17,426$$ n = 17 , 426 ) ancestry. The $$\hat{r}_{g}$$ r ^ g between EUR and AFR was 0.75 ($${\text{s}}.{\text{e}}. = 0.035$$ s . e . = 0.035 ) for height and 0.68 ($${\text{s}}.{\text{e}}. = 0.062$$ s . e . = 0.062 ) for BMI, and the corresponding $$\hat{r}_{{g\left( {GWS} \right)}}$$ r ^ g GWS was 0.82 ($${\text{s}}.{\text{e}}. = 0.030$$ s . e . = 0.030 ) for height and 0.87 ($${\text{s}}.{\text{e}}. = 0.064$$ s . e . = 0.064 ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\hat{r}_{g}$$ r ^ g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

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