How to reduce runtime of gene data processing program?

This program takes a huge data set as input, processes it, calculates and then writes the output to an array. Most calculations may be quite simple, such as summation. In the input file, there are about 100 million rows and 3 columns.

1. first column is the name of the gene (total 100 millions)
2. second column is the specific value
3. third column is another value of each gene

The problem I face is a long runtime. How can I reduce it?

I need to write all new values (from GenePair to RM_pval with header) I calculated from the new file.

fi = open ('1.txt')
fo = open ('2.txt','w')

import math
def log(x):
    return math.log(x)

from math import sqrt

import sys
sys.path.append('/tools/lib/python2.7/site-packages')
import numpy
import scipy
import numpy as np
from scipy.stats.distributions import norm

for line in fi.xreadlines():
    tmp = line.split('\t')

    GenePair = tmp[0].strip()

    PCC_A = float(tmp[1].strip())
    PCC_B = float(tmp[2].strip())

    ZVAL_A = 0.5 * log((1+PCC_A)/(1-PCC_A))
    ZVAL_B = 0.5 * log((1+PCC_B)/(1-PCC_B))

    ABS_ZVAL_A = abs(ZVAL_A)
    ABS_ZVAL_B = abs(ZVAL_B)

    Var_A = float(1) / float(21-3) #SAMPLESIZE - 3
    Var_B = float(1) / float(18-3) #SAMPLESIZE - 3

    WT_A = 1/Var_A #float
    WT_B = 1/Var_B #float

    ZVAL_A_X_WT_A = ZVAL_A * WT_A #float
    ZVAL_B_X_WT_B = ZVAL_B * WT_B #float

    SumofWT = (WT_A + WT_B) #float
    SumofZVAL_X_WT = (ZVAL_A_X_WT_A + ZVAL_B_X_WT_B) #float

    #FIXED MODEL
    meanES = SumofZVAL_X_WT / SumofWT #float
    Var = float(1) / SumofWT #float
    SE = math.sqrt(float(Var)) #float
    LL = meanES - (1.96 * SE) #float
    UL = meanES - (1.96 * SE) #float
    z_score = meanES / SE #float
    p_val = scipy.stats.norm.sf(z_score)

    #CAL
    ES_POWER_X_WT_A = pow(ZVAL_A,2) * WT_A #float
    ES_POWER_X_WT_B = pow(ZVAL_B,2) * WT_B #float
    WT_POWER_A = pow(WT_A,2)
    WT_POWER_B = pow(WT_B,2)
    SumofES_POWER_X_WT = ES_POWER_X_WT_A + ES_POWER_X_WT_B
    SumofWT_POWER = WT_POWER_A + WT_POWER_B

    #COMPUTE TAU
    tmp_A = ZVAL_A - meanES
    tmp_B = ZVAL_B - meanES
    temp = pow(SumofZVAL_X_WT,2)

    Q = SumofES_POWER_X_WT - (temp /(SumofWT))      
    if PCC_A !=0 or PCC_B !=0:
        df = 0
    else:
        df = 1

    c = SumofWT - ((pow(SumofWT,2))/SumofWT)
    if c == 0:
        tau_square = 0
    else:
        tau_square = (Q - df) / c

    #calculation
    Var_total_A = Var_A + tau_square
    Var_total_B = Var_B + tau_square

    WT_total_A = float(1) / Var_total_A
    WT_total_B = float(1) / Var_total_B

    ZVAL_X_WT_total_A = ZVAL_A * WT_total_A
    ZVAL_X_WT_total_B = ZVAL_B * WT_total_B

    Sumoftotal_WT = WT_total_A + WT_total_B
    Sumoftotal_ZVAL_X_WT= ZVAL_X_WT_total_A + ZVAL_X_WT_total_B


    #RANDOM MODEL
    RM_meanES = Sumoftotal_ZVAL_X_WT / Sumoftotal_WT
    RM_Var = float(1) / Sumoftotal_WT
    RM_SE = math.sqrt(float(RM_Var))
    RM_LL = RM_meanES - (1.96 * RM_SE)
    RM_UL = RM_meanES + (1.96 * RM_SE)
    RM_z_score = RM_meanES / RM_Var
    RM_p_val = scipy.stats.norm.sf(RM_z_score)