We consider the problem of recovering a $k$-sparse signal ${\mbox{$β$}}_0\in\mathbb{R}^p$ from noisy observations $\bf y={\bf X}\mbox{$β$}_0+{\bf w}\in\mathbb{R}^n$. One of the most popular approaches is the $l_1$-regularized least squares, also known as LASSO. We analyze the mean square error of LASSO in the case of random designs in which each row of ${\bf X}$ is drawn from distribution $N(0,{\mbox{$Σ$}})$ with general ${\mbox{$Σ$}}$. We first derive the asymptotic risk of LASSO in the limit of $n,p\rightarrow\infty$ with $n/p\rightarrowδ$. We then examine conditions on $n$, $p$, and $k$ for LASSO to exactly reconstruct ${\mbox{$β$}}_0$ in the noiseless case ${\bf w}=0$. A phase boundary $δ_c=δ(ε)$ is precisely established in the phase space defined by $0\leδ,ε\le 1$, where $ε=k/p$. Above this boundary, LASSO perfectly recovers ${\mbox{$β$}}_0$ with high probability. Below this boundary, LASSO fails to recover $\mbox{$β$}_0$ with high probability. While the values of the non-zero elements of ${\mbox{$β$}}_0$ do not have any effect on the phase transition curve, our analysis shows that $δ_c$ does depend on the signed pattern of the nonzero values of $\mbox{$β$}_0$ for general ${\mbox{$Σ$}}\ne{\bf I}_p$. This is in sharp contrast to the previous phase transition results derived in i.i.d. case with $\mbox{$Σ$}={\bf I}_p$ where $δ_c$ is completely determined by $ε$ regardless of the distribution of $\mbox{$β$}_0$. Underlying our formalism is a recently developed efficient algorithm called approximate message passing (AMP) algorithm. We generalize the state evolution of AMP from i.i.d. case to general case with ${\mbox{$Σ$}}\ne{\bf I}_p$. Extensive computational experiments confirm that our theoretical predictions are consistent with simulation results on moderate size system.