Results 1 - 10
of
53
Using WholeGenome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster. PLoS Genet 8: e1002685
, 2012
"... Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we rep ..."
Abstract
-
Cited by 18 (1 self)
- Add to MetaCart
(Show Context)
Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using,2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.23960.008 (0.23060.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5 % SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these
Polygenic modeling with Bayesian sparse linear mixed models. PLOS Genetics 9:e1003264. doi
, 2013
"... Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, i ..."
Abstract
-
Cited by 16 (5 self)
- Add to MetaCart
(Show Context)
Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given dataset one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a ‘‘Bayesian sparse linear mixed model’ ’ (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from
CS: Localising loci underlying complex trait variation using regional genomic relationship mapping. PLoS One 2012
- Wild S, Hicks AA, Pramstaller PP, Hastie N, Wright AF, Haley
"... The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may co ..."
Abstract
-
Cited by 10 (3 self)
- Add to MetaCart
(Show Context)
The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship (‘‘identity by state’ ’ or IBS)
A genome-wide association study reveals loci influencing height and other conformation traits in horses. PLoS One 7: e37282
, 2012
"... The molecular analysis of genes influencing human height has been notoriously difficult. Genome-wide association studies (GWAS) for height in humans based on tens of thousands to hundreds of thousands of samples so far revealed,200 loci for human height explaining only 20 % of the heritability. In d ..."
Abstract
-
Cited by 8 (3 self)
- Add to MetaCart
(Show Context)
The molecular analysis of genes influencing human height has been notoriously difficult. Genome-wide association studies (GWAS) for height in humans based on tens of thousands to hundreds of thousands of samples so far revealed,200 loci for human height explaining only 20 % of the heritability. In domestic animals isolated populations with a greatly reduced genetic heterogeneity facilitate a more efficient analysis of complex traits. We performed a genome-wide association study on 1,077 Franches-Montagnes (FM) horses using,40,000 SNPs. Our study revealed two QTL for height at withers on chromosomes 3 and 9. The association signal on chromosome 3 is close to the LCORL/NCAPG genes. The association signal on chromosome 9 is close to the ZFAT gene. Both loci have already been shown to influence height in humans. Interestingly, there are very large intergenic regions at the association signals. The two detected QTL together explain,18.2 % of the heritable variation of height in horses. However, another large fraction of the variance for height in horses results from ECA 1 (11.0%), although the association analysis did not reveal significantly associated SNPs on this chromosome. The QTL region on ECA 3 associated with height at withers was also significantly associated with wither height, conformation of legs, ventral border of mandible, correctness of gaits, and expression of the head. The region on ECA 9 associated with height at withers was also associated with wither height, length of croup and length of back. In addition to these two QTL regions on ECA 3 and ECA 9 we detected another QTL on ECA 6 for correctness of gaits. Our
Genomewide association study identifies major loci for carcass weight on BTA14
- in Hanwoo (Korean Cattle). PLoS ONE 8(10): e74677
, 2013
"... This genome-wide association study (GWAS) was conducted to identify major loci that are significantly associated with carcass weight, and their effects, in order to provide increased understanding of the genetic architecture of carcass weight in Hanwoo. This genome-wide association study identified ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
This genome-wide association study (GWAS) was conducted to identify major loci that are significantly associated with carcass weight, and their effects, in order to provide increased understanding of the genetic architecture of carcass weight in Hanwoo. This genome-wide association study identified one major chromosome region ranging from 23 Mb to 25 Mb on chromosome 14 as being associated with carcass weight in Hanwoo. Significant Bonferroni-corrected genome-wide associations (P,1.5261026) were detected for 6 Single Nucleotide Polymorphic (SNP) loci for carcass weight on chromosome 14. The most significant SNP was BTB-01280026 (P = 4.02610211), located in the 25 Mb region on Bos taurus autosome 14 (BTA14). The other 5 significant SNPs were Hapmap27934-BTC-065223 (P = 4.04610211) in 25.2 Mb, BTB-01143580 (P = 6.35610211) in 24.3 Mb, Hapmap30932-BTC-011225 (P = 5.92610210) in 24.8 Mb, Hapmap27112-BTC-063342 (P = 5.1861029) in 25.4 Mb, and Hapmap24414-BTC-073009 (P = 7.3861028) in 25.4 Mb, all on BTA 14. One SNP (BTB-01143580; P = 6.35610211) lies independently from the other 5 SNPs. The 5 SNPs that lie together showed a large Linkage disequilibrium (LD) block (block size of 553 kb) with LD coefficients ranging from 0.53 to 0.89 within the block. The most significant SNPs accounted for 6.73 % to 10.55 % of additive genetic variance, which is quite a large proportion of the total additive genetic variance. The most significant SNP (BTB-01280026; P = 4.02610211) had 16.96 kg of allele substitution effect, and the second most significant SNP (Hapmap27934-BTC-065223; P = 4.04610211) had 18.06 kg of effect on carcass weight,
Fries R: Identification and dissection of four major QTL affecting milk fat content in the German Holstein-Friesian population. Plos One 2012
"... Milk composition traits exhibit a complex genetic architecture with a small number of major quantitative trait loci (QTL) explaining a large fraction of the genetic variation and numerous QTL with minor effects. In order to identify QTL for milk fat percentage (FP) in the German Holstein-Friesian (H ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
Milk composition traits exhibit a complex genetic architecture with a small number of major quantitative trait loci (QTL) explaining a large fraction of the genetic variation and numerous QTL with minor effects. In order to identify QTL for milk fat percentage (FP) in the German Holstein-Friesian (HF) population, a genome-wide association study (GWAS) was performed. The study population consisted of 2327 progeny-tested bulls. Genotypes were available for 44,280 SNPs. Phenotypes in the form of estimated breeding values (EBVs) for FP were used as highly heritable traits. A variance components-based approach was used to account for population stratification. The GWAS identified four major QTL regions explaining 46.18% of the FP EBV variance. Besides two previously known FP QTL on BTA14 (P = 8.916102198) and BTA20 (P = 7.03610212) within DGAT1 and GHR, respectively, we uncovered two additional QTL regions on BTA5 (P = 2.00610213) and BTA27 (P = 9.8361025) encompassing EPS8 and GPAT4, respectively. EPS8 and GPAT4 are involved in lipid metabolism in mammals. Re-sequencing of EPS8 and GPAT4 revealed 50 polymorphisms. Genotypes for five of them were inferred for the entire study population. Two polymorphisms affecting potential transcription factor binding sites of EPS8 (P = 1.40610212) and GPAT4 (P = 5.1861025), respectively, were highly significantly associated with the FP EBV. Our results provide evidence that
Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of GenomeWide Association Studies. PloS one
, 2014
"... Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well
Genome-Wide Genetic Diversity and Differentially Selected Regions among
- Polypay, and Targhee Sheep. PLoS One 8: e65942
, 2013
"... Sheep are among the major economically important livestock species worldwide because the animals produce milk, wool, skin, and meat. In the present study, the Illumina OvineSNP50 BeadChip was used to investigate genetic diversity and genome selection among Suffolk, Rambouillet, Columbia, Polypay, an ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
Sheep are among the major economically important livestock species worldwide because the animals produce milk, wool, skin, and meat. In the present study, the Illumina OvineSNP50 BeadChip was used to investigate genetic diversity and genome selection among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds from the United States. After quality-control filtering of SNPs (single nucleotide polymorphisms), we used 48,026 SNPs, including 46,850 SNPs on autosomes that were in Hardy-Weinberg equilibrium and 1,176 SNPs on chromosome6 for analysis. Phylogenetic analysis based on all 46,850 SNPs clearly separated Suffolk from Rambouillet, Columbia, Polypay, and Targhee, which was not surprising as Rambouillet contributed to the synthesis of the later three breeds. Based on pair-wise estimates of FST, significant genetic differentiation appeared between Suffolk and Rambouillet (FST = 0.1621), while Rambouillet and Targhee had the closest relationship (FST = 0.0681). A scan of the genome revealed 45 and 41 differentially selected regions (DSRs) between Suffolk and Rambouillet and among Rambouillet-related breed populations, respectively. Our data indicated that regions 13 and 24 between Suffolk and Rambouillet might be good candidates for evaluating breed differences. Furthermore, ovine genome v3.1 assembly was used as reference to link functionally known homologous genes to economically important traits covered by these differentially selected regions. In brief, our present study provides a comprehensive genome-wide view on within- and between-breed genetic differentiation, biodiversity, and evolution
Genome-wide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in Holstein cattle
, 2013
"... Heat stress compromises production, fertility, and health of dairy cattle. One mitigation strategy is to select individuals that are genetically resistant to heat stress. Most of the negative effects of heat stress on animal performance are a consequence of either physiological adaptations to regula ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Heat stress compromises production, fertility, and health of dairy cattle. One mitigation strategy is to select individuals that are genetically resistant to heat stress. Most of the negative effects of heat stress on animal performance are a consequence of either physiological adaptations to regulate body temperature or adverse consequences of failure to regulate body temperature. Thus, selection for regulation of body temperature during heat stress could increase thermotolerance. The objective was to perform a genome-wide association study (GWAS) for rectal temperature (RT) during heat stress in lactating Holstein cows and identify SNPs associated with genes that have large effects on RT. Records on afternoon RT where the temperature-humidity index was $78.2 were obtained from 4,447 cows sired by 220 bulls, resulting in 1,440 useable genotypes from the Illumina BovineSNP50 BeadChip with 39,759 SNP. For GWAS, 2, 3, 4, 5, and 10 adjacent SNP were averaged to identify consensus genomic regions associated with RT. The largest proportion of SNP variance (0.07 to 0.44%) was explained by markers flanking the region between 28,877,547 and 28,907,154 bp on Bos taurus autosome (BTA) 24. That region is flanked by U1 (28,822,883 to 28,823,043) and NCAD (28,992,666 to 29,241,119). In addition, the SNP at 58,500,249 bp on BTA 16 explained 0.08 % and 0.11 % of the SNP variance for 2- and 3-SNP analyses, respectively. That contig includes SNORA19, RFWD2 and SCARNA3. Other SNPs associated with RT were located on BTA 16 (close to CEP170 and PLD5), BTA 5 (near SLCO1C1 and PDE3A), BTA 4 (near KBTBD2 and LSM5), and BTA 26 (located in GOT1, a gene implicated in
A 660-Kb Deletion with Antagonistic Effects on Fertility and Milk Production Segregates at High Frequency in Nordic Red Cattle: Additional Evidence for the Common Occurrence of Balancing Selection in Livestock
"... In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed that this reduction is primarily due to the negativ ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed that this reduction is primarily due to the negative energy balance of high-producing cows at the peak of lactation. We herein describe the fine-mapping of a major fertility QTL in Nordic Red cattle, and identify a 660-kb deletion encompassing four genes as the causative variant. We show that the deletion is a recessive embryonically lethal mutation. This probably results from the loss of RNASEH2B, which is known to cause embryonic death in mice. Despite its dramatic effect on fertility, 13%, 23 % and 32 % of the animals carry the deletion in Danish, Swedish and Finnish Red Cattle, respectively. To explain this, we searched for favorable effects on other traits and found that the deletion has strong positive effects on milk yield. This study demonstrates that embryonic lethal mutations account for a non-negligible fraction of the decline in fertility of domestic cattle, and that associated positive effects on milk yield may account for part of the negative genetic correlation. Our study adds to the evidence that structural variants contribute to animal phenotypic variation, and that balancing selection might
