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Introduction Lamb production is the largest part of income in sheep industry. Therefore, the mortality rate of lambs is a key factor in profit of the sheep breeding. Mortality rate of lambs (or Lamb mortality rate) in different breeds of sheep under different climatic conditions is varying from 15% to 50% and an average of 9% to 20% has been reported. Survival rate is a combination trait that is influenced by various factors such as management, weather condition, and behavior of dam and lamb, as well as genetic effects. Quantification of non-genetic effects on mortality rate can be useful in controlling lamb survival rate and increasing profitability of sheep breeding. Therefore, identification of genetic and environmental factors affecting the productive capacity of indigenous breeds in different area is the main priority that should be considered in breeding programmes. Therefore, the objective of this study was to estimate genetic and environmental factors of growth traits and mortality in Karakul lambs. To estimate the genetic and environmental parameters of Karakul lambs before weaning growth and mortality records of 4929 lambs from 207 rams and 1856 ewes at Sarakhs Karakul sheep breeding station, from 1994 to 2009 were used. Materials and Methods The data were used in this study included a total of 4929 record of lamb birth weight, 1 and 3 months of age, average daily gain from birth to weaning (growth traits before weaning) and mortality rate of lambs from birth to 1, 2, 4, 8 and 14 weeks (mortality rate of lambs before weaning). Data were collected during the years 1994 to 2010 in karakul breeding station in Sarakhs. The data were edited and pedigree file and data file were prepared. Uni-variate animal model was used to estimate the genetic parameters as following: where is the vector of record, b is the vector of fixed effects (year, sex, type of birth, age of dam), a is the vector of direct additive genetic effects, m the vector of maternal additive genetic effects, X, Z1 and Z2 are the matrix of coefficients (0 and 1) that relate b, a and m to records and e is the vector of residuals. Analysis of each trait was performed considering the significant factors in the model including sex, birth-type, birth-year, dam-age and ewe weight at birth. In the analysis of maternal effect model for growth traits and Cox regression for mortality traits were used. Cox Regression in SPSS software was also used to calculating the survival function. (Co) variance components were estimated by Restricted Maximum Likelihood (REML) with uni-variate animal model. The genetic, phenotypic and environmental trends were estimated as regression of average breeding values on year of birth, regression of average phenotypic values on year of birth and, difference between genetic and phenotypic trends, respectively. Results and discussion The results showed that year of birth had a significant effect on all traits (P