Why does $P(\theta_1\mid D, \theta_2) \propto P(D \mid \theta_1, \theta_2)P(\theta_1)$ hold? Suppose that in a Bayesian framework we have observed data $D$, using independent prior distributions on the parameters of the model, denoted by $\theta_1, \theta_2$. Then, the joint posterior distribution of $\theta_1, \theta_2$ can be written as:
$$
P(\theta_1, \theta_2 \mid D) \propto P(D \mid \theta_1, \theta_2)p(\theta_1)p(\theta_2)
$$ 
One way to obtain estimates from the posterior is use Gibbs Sampling. This sampler requires simulating from the full conditional distributions for both $\theta_1$, $\theta_2$. One relation for the full conditional that caught my eye in a book is the following:
$$
P(\theta_1\mid D, \theta_2) \propto P(D \mid \theta_1, \theta_2)p(\theta_1)
$$ 
I am wondering how exactly this proportional relation is derived. Would anyone have any ideas? It seems that there are extra assumptions here that are implicit. 
 A: The starting point is the proportionality symbol$$\propto$$as it takes different meanings in the few formulas in the question. In
$$
P(\theta_1, \theta_2 \mid D) \propto P(D \mid \theta_1,\theta_2)p(\theta_1)p(\theta_2)
$$
the function of $(\theta_1, \theta_2)$ on the l.h.s. is proportional to the function of $(\theta_1, \theta_2)$ on the r.h.s., meaning that they differ by a multiplicative constant $\kappa$. This is a constant w.r.t. the functions of $(\theta_1, \theta_2)$, meaning that $\kappa$ may depend on (and hence be a function of) other factors, like $D$:
$$\kappa=\kappa(D)$$
In fact, since $P(\theta_1, \theta_2 \mid D)$ is a probability density, this constant $\kappa$ is uniquely defined as the normalisation of the r.h.s. that turns it into a probability density, with total mass equal to one:
$$\kappa=\kappa(D)=1\Big/\int_\Theta P(D \mid \theta_1,\theta_2)p(\theta_1)p(\theta_2)\,\text{d}\lambda(\theta_1,\theta_2)$$
for the appropriate measure $\text{d}\lambda$.
In
$$P(\theta_1\mid D, \theta_2) \propto P(D \mid \theta_1, \theta_2)p(\theta_1)\qquad\qquad(1)$$
the function of $\theta_1$ on the l.h.s. is proportional to the function of $\theta_1$ on the r.h.s., meaning that $\kappa$ may depend on (and hence be a function of) other factors, like $D$ and now $\theta_2$:
$$\kappa=\kappa(D,\theta_2)$$
Note that
$$
P(\theta_1 \mid \theta_2, D) \propto P(D \mid \theta_1,\theta_2)p(\theta_1)p(\theta_2)
$$
is equally correct.
That the result (1) holds follows from the standard definition of the conditional density
\begin{align*}
P(\theta_1\mid D, \theta_2) &= \dfrac{P(D \mid \theta_1, \theta_2)p(\theta_1)p(\theta_2)}{\int_{\Theta_1} P(D \mid \theta_1, \theta_2)p(\theta_1)p(\theta_2)\,\text{d}\lambda_1(\theta_1)}\\
&= \dfrac{P(D \mid \theta_1, \theta_2)p(\theta_1)\times p(\theta_2)}{\left\{\int_{\Theta_1} P(D \mid \theta_1, \theta_2)p(\theta_1)\,\text{d}\lambda_1(\theta_1)\right\}\times p(\theta_2)}\\
&= \dfrac{P(D \mid \theta_1, \theta_2)p(\theta_1)}{\int_{\Theta_1} P(D \mid \theta_1, \theta_2)p(\theta_1)\,\text{d}\lambda_1(\theta_1)}\\
&= \dfrac{P(D \mid \theta_1, \theta_2)p(\theta_1)}{\kappa(D,\theta_2)}\\
&\propto P(D \mid \theta_1, \theta_2)p(\theta_1)
\end{align*}
