Genome-wide association studies, which are based on the common disease/common variants assumption, have successfully identified susceptibility loci for complex traits. However, the variants discovered through these studies explain only a modest portion of the trait variability [

1]. With the new technological advances, it has been suggested that it is time to shift the search from common variants of modest effect to rarer variants of large effect by effectively searching the full genome [

2]. Rare variants may hold promise to predict individual risk for personalized medicine because of their large effect, although it has been argued that common variants illuminate the biologic pathways that underlie diseases [

3].

Bodmer and Tomlinson [

4] suggested that a set of low-frequency variants from different genes can account for a significant proportion of the variability of relatively common diseases. To achieve reasonable statistical power, it is critical to define the rare variants and test them collectively. The existing statistical methods in the literature mainly collapse rare variants [

5]. Madsen and Browning [

6] proposed using the inverse of the variance of the minor allele frequency (MAF) in control subjects as a weight and then collapsing the weighted rare variants.

Briefly, for the

*i*th individual Madsen and Browning [

6] define a genetic score:

where

*L* is the number of variants,

*g*_{ij} is the genotypic score, and

*w*_{j} is the weight for the

*j*th single-nucleotide polymorphism (SNP). The weight

*w*_{j} is defined as the inverse of the

*j*th SNP’s standard deviation estimated in control subjects when the corresponding MAF is less than

*α* (such as 0.02) and 0 otherwise. Then the Wilcoxon rank sum test is applied to do the association test. Madsen and Browning rank the genetic scores, calculate the sum of the ranks for case subjects as:

and calculate the

*p*-value using a permutation strategy. That is, they permute disease status among individuals 1,000 times to compute 1,000 statistics

*X*, denoted

,

, …,

. Then they use the sample mean

and the standard deviation

of

,

, …,

to calculate the test statistic:

which follows approximately a standard normal distribution under the null hypothesis. Madsen and Browning [

6] demonstrated that this weighted-sum method is more powerful than the collapsing method [

5].

Recently, we have demonstrated that family data are useful for searching for rare variants [

7,

8], because the rare variants can be substantially enriched among segregating family members. Here, we present a statistical method to test rare variants by using both family and unrelated case-control sequencing data.