Untitled

Quade, Shannon R E; Elston, Robert C.; Goddard, Katrina A.B.

doi:10.1186/1471-2156-6-25

Cited by 18 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practice, pooling will add noise to the data. One way to simulate this noise is to use the binomial model proposed by Quade et al [41]. To be specific, suppose that the pool size is k, and the true total allele count at a particular locus is t (i.e., in the pool of K D 2k haplotypes, t of them have the allele '1' at that locus, and the remaining 2k t haplotypes have the allele '0').…”

Section: Discussion and Extensionsmentioning

confidence: 86%

A fast collapsed data method for estimating haplotype frequencies from pooled genotype data with applications to the study of rare variants

Kuk

2012

Statistics in Medicine

View full text Add to dashboard Cite

Haplotype information could lead to more powerful tests of genetic association than single-locus analyses but it is not easy to estimate haplotype frequencies from genotype data due to phase ambiguity. The challenge is compounded when individuals are pooled together to save costs or to increase sample size, which is crucial in the study of rare variants. Existing expectation-maximization type algorithms are slow and cannot cope with large pool size or long haplotypes. We show that by collapsing the total allele frequencies of each pool suitably, the maximum likelihood estimates of haplotype frequencies based on the collapsed data can be calculated very quickly regardless of pool size and haplotype length. We provide a running time analysis to demonstrate the considerable savings in time that the collapsed data method can bring. The method is particularly well suited to estimating certain union probabilities useful in the study of rare variants. We provide theoretical and empirical evidence to suggest that the proposed estimation method will not suffer much loss in efficiency if the variants are rare. We use the method to analyze re-sequencing data collected from a case control study involving 148 obese persons and 150 controls. Focusing on a region containing 25 rare variants around the MGLL gene, our method selects three rare variants as potentially causal. This is more parsimonious than the 12 variants selected by a recently proposed covering method. From another set of 32 rare variants around the FAAH gene, we discover an interesting potential interaction between two of them.

show abstract

Section: Discussion and Extensionsmentioning

confidence: 86%

A fast collapsed data method for estimating haplotype frequencies from pooled genotype data with applications to the study of rare variants

Kuk

2012

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…The low-frequency mutant allele for some variants in some populations suggests the mutant allele called in those animals originated from either a de novo mutation, crossbreeding, or a genotyping error. Genotyping errors are known to impact allele and haplotype frequency estimates ( Quade et al, 2005 ; Govindarajulu et al, 2006 ; Zhu et al, 2007 ), and are inevitable when genotyping a large number of animals ( Kirk and Cardon, 2002 ). All animals included in the study herein were checked for Mendelian errors where possible.…”

Section: Discussionmentioning

confidence: 99%

Associations between polymorphisms in the myostatin gene with calving difficulty and carcass merit in cattle

Ryan,

Purfield,

Naderi

et al. 2023

Journal of Animal Science

View full text Add to dashboard Cite

A fully functional myostatin gene inhibits muscle fiber growth. The objective of the present study was to quantify the association between 21 known myostatin mutations with both calving and carcass traits in 12 cattle breeds. The myostatin genotypes of 32,770 dam-progeny combinations were used in the association analysis of calving dystocia, with the genotypes of 129,803 animals used in the mixed model association analyses of carcass weight, conformation, and fat score. The mixed model included additive genetic, maternal, and permanent environmental effects where appropriate. The mutant genotypes of nt821, Q204X, and F94L were all associated (P < 0.01) with more calving difficulty when present in either the dam or the progeny. The nt821 deletion had the greatest association with calving difficulty when the homozygous deletion was present in either the calf (0.37 points greater calving difficulty score relative to calves carrying no copies of the deletion based on a one to four scale) or the dam (1.30 points greater calving difficulty score relative to dams carrying no copies of the deletion), although the association between the calf’s nt821 genotype and calving difficulty differed depending on the nt821 genotype of the dam. With the exception of nt748_78, nt414, and nt374_51, all other seven segregating myostatin variants were associated (range of allele substitution effect size relative to animals with no copies of the mutant allele) with carcass weight (2.36 kg lighter to 15.56 kg heavier), all 10 segregating variants with conformation (0.15 units less conformed to 2.24 units more conformed assessed on a scale of 1 to 15), and all segregating variants other than E226X with carcass fat (0.23 units less carcass fat cover to 3.85 units more carcass fat cover assessed on a scale of 1 to 15). Of these, the F94L, Q204X, and nt821 mutations generally had the greatest association with all three carcass traits, giving rise to heavier and more conformed carcasses. Despite the antagonistic genetic relationship between calving difficulty and carcass traits, the nt374_51, F94L, and E226X mutations were all associated with improved carcass merit while having minimal expected consequences on calving difficulty. Thus, animals carrying these mutation(s) may have favorable genetic merit for calving difficulty and carcass merit. Furthermore, depending on the dam genotype, a bull with two copies of the nt821 mutation can produce progeny with improved carcass merit while minimizing calving problems.

show abstract

Haplotype inference using a Bayesian Hidden Markov model

Sun

Greenwood

Neal

2007

Genetic Epidemiology

View full text Add to dashboard Cite

Knowledge of haplotypes is useful for understanding block structure in the genome and disease risk associations. Direct measurement of haplotypes in the absence of family data is presently impractical, and hence, several methods have been developed for reconstructing haplotypes from population data. We have developed a new population-based method using a Bayesian Hidden Markov model for the source of the ancestral haplotype segments. In our Bayesian model, a higher order Markov model is used as the prior for ancestral haplotypes, to account for linkage disequilibrium. Our model includes parameters for the genotyping error rate, the mutation rate, and the recombination rate at each position. Computation is done by Markov Chain Monte Carlo using the forward-backward algorithm to efficiently sum over all possible state sequences of the Hidden Markov model. We have used the model to reconstruct the haplotypes of 129 children at a region on chromosome 5 in the data set of Daly et al. [2001] (for which true haplotypes are obtained based on parental genotypes) and of 30 children at selected regions in the CEU and YRI data of the HAPMAP project. The results are quite close to the family-based reconstructions and comparable with the state-of-the-art PHASE program. Our haplotype reconstruction method does not require division of the markers into small blocks of loci. The recombination rates inferred from our model can help to predict haplotype block boundaries, and estimate recombination hotspots.

show abstract

Untitled

Cited by 18 publications

References 18 publications

A fast collapsed data method for estimating haplotype frequencies from pooled genotype data with applications to the study of rare variants

A fast collapsed data method for estimating haplotype frequencies from pooled genotype data with applications to the study of rare variants

Associations between polymorphisms in the myostatin gene with calving difficulty and carcass merit in cattle

Haplotype inference using a Bayesian Hidden Markov model

Contact Info

Product

Resources

About