Estimating genetic correlations in natural populations in the absence of pedigree information: Accuracy and precision of the Lynch method Export

@article{citeulike:5015110, abstract = {Abstract. Usually, genetic correlations are estimated from breeding designs in the laboratory or greenhouse. However, estimates of the genetic correlation for natural populations are lacking, mostly because pedigrees of wild individuals are rarely known. Recently Lynch (1999) proposed a formula to estimate the genetic correlation in the absence of data on pedigree. This method has been shown to be particularly accurate provided a large sample size and a minimum (20\%) proportion of relatives. Lynch (1999) proposed the use of the bootstrap to estimate standard errors associated with genetic correlations, but did not test the reliability of such a method. We tested the bootstrap and showed the jackknife can provide valid estimates of the genetic correlation calculated with the Lynch formula. The occurrence of undefined estimates, combined with the high number of replicates involved in the bootstrap, means there is a high probability of obtaining a biased upward, incomplete bootstrap, even when there is a high fraction of related pairs in a sample. It is easier to obtain complete jackknife estimates for which all the pseudovalues have been defined. We therefore recommend the use of the jackknife to estimate the genetic correlation with the Lynch formula. Provided data can be collected for more than two individuals at each location, we propose a group sampling method that produces}, author = {R\'{e}ale, Denis and Roff, Derek A.}, citeulike-article-id = {5015110}, citeulike-linkout-0 = {http://dx.doi.org/10.1554/0014-3820(2001)055%5B1249:EGCINP%5D2.0.CO;2}, doi = {10.1554/0014-3820(2001)055%5B1249:EGCINP%5D2.0.CO;2}, issn = {0014-3820}, journal = {Evolution}, keywords = {bootstrap, correlation, genetic, jacknife}, number = {6}, pages = {1249+}, posted-at = {2009-06-29 20:48:36}, priority = {0}, title = {Estimating genetic correlations in natural populations in the absence of pedigree information: Accuracy and precision of the Lynch method}, url = {http://dx.doi.org/10.1554/0014-3820(2001)055%5B1249:EGCINP%5D2.0.CO;2}, volume = {55}, year = {2001} }

TY - JOUR ID - citeulike:5015110 L3 - citeulike-article-id:5015110 N2 - Abstract. Usually, genetic correlations are estimated from breeding designs in the laboratory or greenhouse. However, estimates of the genetic correlation for natural populations are lacking, mostly because pedigrees of wild individuals are rarely known. Recently Lynch (1999) proposed a formula to estimate the genetic correlation in the absence of data on pedigree. This method has been shown to be particularly accurate provided a large sample size and a minimum (20%) proportion of relatives. Lynch (1999) proposed the use of the bootstrap to estimate standard errors associated with genetic correlations, but did not test the reliability of such a method. We tested the bootstrap and showed the jackknife can provide valid estimates of the genetic correlation calculated with the Lynch formula. The occurrence of undefined estimates, combined with the high number of replicates involved in the bootstrap, means there is a high probability of obtaining a biased upward, incomplete bootstrap, even when there is a high fraction of related pairs in a sample. It is easier to obtain complete jackknife estimates for which all the pseudovalues have been defined. We therefore recommend the use of the jackknife to estimate the genetic correlation with the Lynch formula. Provided data can be collected for more than two individuals at each location, we propose a group sampling method that produces IS - 6 JF - Evolution SN - 0014-3820 TI - Estimating genetic correlations in natural populations in the absence of pedigree information: Accuracy and precision of the Lynch method VL - 55 SP - 1249 KW - bootstrap KW - correlation KW - genetic KW - jacknife AU - Réale, Denis AU - Roff, Derek PY - 2001/// UR - http://dx.doi.org/10.1554/0014-3820(2001)055%5B1249:EGCINP%5D2.0.CO;2 ER -