read_json should support usecols option (or hook for arbitrary transformation after each line is read)
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
pd.read_json(path_to_big_file, lines=True, usecols=some_subset_of_cols)

```
#### Problem description

The `read_csv` function's `usecols` argument is very helpful in reading a subset of columns from a very large file. Unfortunately it does not exist in `read_json` so I have to manually read the file line by line and parse out the fields I am interested in to avoid memory issues. This comes at a big cost of slowness to loading json files. 

One possible implementation that may be worth considering might be to have hooks to allow for user-defined transformations after each line read and after json transformation. In that way, we could also support an additional use case of applying custom decode function to lines when dealing with hybrid proprietary file types.

```python
def strip_preamble(line):
   '''Example line transformation before valid json object'''
    return line.split("|", 1)[1]

def select_subset(json_rec):
   '''Example selection of columns from all possible columns in json lines'''
   return {k: json_rec.get(k) for k in ["col_A", "col_B"]}

pd.read_json(path_to_big_file, lines=True, line_loaded_hook=strip_preamble, json_created_hook=select_subset)

```
