To detect how much genetic variance is accounted for by different genomic regions one first step is to work at the chromosomal level. We used a half sib data structure for two growth traits in sheep as a potentially powerful design to partition the genetic variance across chromosomes. Records for post weaning weight (PW) and scan C site back fat (CF) were used from 5,239 merino sheep. The model of analysis accounted for population structure by fitting genetic group effects as well as the numerator relationship matrix (A) or the first five principal components (PC). Different approximations were compared fitting the genomic relationship matrix (0) based on 48,599 markers, or on single nucleotide polymorphisms of an individual chromosome. The correlation between chromosome length (L) and variance explained per chromosome (uJ) was 0.53 and 0.70 for PW and CF correspondingly, however significant differences in (uJ/L) were found between chromosomes, ranging from 0% to 17.5%. Some chromosomes explained more variance and covariance than expected, under the assumption that it is proportional to the chromosome size; suggesting that some chromosomes clearly harbor more QTL. Some chromosomes show a covariance of opposite sign indicating they could be used in selection to 'break' an unfavourable correlation (e.g. chromosome 8). These results represent a powerful source of information for genomic selection.