What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)? I read not too long ago Nelder and McCullagh's book Generalized Linear Models and thought the book was fantastic and I consider it a useful manual on the subject. Not surprising that's the case, considering Nelder's one of the authors.
However, the book is 40 years old, and surely things have changed since when the book came out. Simon Wood's book, Generalized Additive Models, is very good, but the book focuses on GAMs, not GLMs. (Yes, GAMs generalize GLMs, but I do think focusing on GLMs specifically is worthwhile.)
Hence, what would be the most important developments since Nelder and McCullagh's book came out regarding GLM theory and application? What am I missing from just reading that book? How should I supplement my knowledge?

In addition to Ben's great answer  (+1):
Penalised regression models ($L_1$, $L_2$, elastic net, SCAD (Smoothly clipped absolute deviation), LARS (least-angle regression), MCP (Multiple Change Points), PCR (Principal Component Regression), PLS (Partial least squares)  etc.) really came to the fore and became especially relevant for $n\ll p$ applications. While it can be argued that Hoerl & Kennard's Ridge regression: Biased estimation for nonorthogonal problems came out in 1970  (and actually Wold's work on  PLS was in  1966), it was until the last 20 to 25 years that most of the adoption and implications of these techniques became prominent within Statistics programmes and curricula.
