Unravelling complex trait architecture using DNA sequence data
Final Report Abstract
The heritability of a phenotype is crucial both for medical treatment of diseases and the progress that can be achieved in animal and plant breeding. An accurate determination of the heritability is therefore an important task in genetics. We showed that traditional estimators are biased and developed an essentially unbiased estimator. Our estimator accords with the Bayesian estimator that was recently developed by Lehermeier and co-authors, but is both numerically more precise and much faster to calculate. The evaluation of some first, real data sets shows that the deviation between a traditional estimator and an unbiased estimator can be 40 percentage points. This raises the question that significant deviations among the estimates can appear in any data set. Our estimator also allows that the relevance of a chromosome or a gene with respect to a certain phaenotype can be determined. The relevance is here a quantitative value that sums up to 100 % if the values of all chromosomes (or all genes within chromosome) are added. Such a value cannot be derived from traditional estimators. Our work also contributes to the so-called missing heritability, the observed discrepancy between the value of the heritability, estimated from purely genomic data, and estimates obtained from direct observations that are considered as more accurate. In fact, the use of an unbiased estimator removes or excludes some of the possible reasons for this gap, which have been discussed in literature.
Publications
- (2018) From estimation to prediction of genomic variances allowing for linkage disequilibrium and unbiasedness. PhD thesis. Univ. Mannheim
Schreck, N.M.
- (2019). Best prediction of the additive genomic variance in random-effects models. Genetics, 213(2), 379-394
Schreck, N.M., Piepho, H. P., & Schlather, M.
(See online at https://doi.org/10.1534/genetics.119.302324) - (2019). Empirical decomposition of the explained variation in the variance components form of the mixed model
Schreck, N.M.
(See online at https://doi.org/10.1101/2019.12.28.890061)
