Counts of non-synonymous variants in Baylor and Broad before filtering.

<p>Note: Single: count of singletons; Double: count of doubletons; RVs: count of variants with MAF and not singletons or doubletons; LFVs: count of variants with MAF ; CVs: count of variants with MAF .</p

Distribution of doubletons as a function of the eigen-map.

<p>The first eigen-vector versus second eigen-vector for (A) Baylor and (B) Broad samples. Eigen-vectors are obtained by applying PCA to all common variants. For each individual, we count the number of doubletons. To indicate the relative number of doubletons per individual, points are color-coded as follows: black (bottom : fewest doubletons), blue (next 25), green (next 25), and orange (top 25: most doubletons) within the Baylor and Broad samples, respectively.</p

PCA from common variants, low frequency variants, and both types of variants.

<p>Plotted are the first eigen-vector versus second eigen-vector for Broad samples. Eigen-vectors are obtained by applying PCA to all common variants that have no missingness (56,607 variants) (A), all low frequency variants that have no missingness (29,509 variants) (B), and both type of variants (C). The colors are obtained by clustering individuals based on their coordinates in panel (A) using model based clustering <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003443#pgen.1003443-Fraley1" target="_blank">[51]</a>.</p

-log10(observed p-values) versus -log10(expected p-values) of SKAT and Burden test for Mega-analysis.

<p>Panel (A) shows SKAT p-values, Panel (B) shows burden test p-values. and 1.047, for mega SKAT and burden test, respectively.</p

Q–Q plot of simulation tests under the assumption that linkage disequilibrium among rare variants has little impact on the distribution of the test statistic.

<p>144 genes are selected from the Broad data set. Each gene has exactly rare variants, . For each gene, we first randomly assign the phenotypes for 913 samples based on a coin toss, then calculate the test statistics , and corresponding p-value computed under the assumption that . We repeat this 100 times per gene, to obtain more than 10,000 p-values.</p

Distribution of rare variants per gene in Baylor and Broad data sets after filtering.

<p>Minor allele counts (MAC) are restricted to variants with minor allele frequency . Panel (A), distribution of mean MAC per sample, averaged over all genes. Panel (B), in the Baylor samples, genes were binned based on the counts of rare variants (which range from 1 to 30); for each bin the vertical axis shows the distribution of counts (boxplot) from the same genes in the Broad samples. The red line indicates an equal count in Broad and Baylor.</p

Number of significant genes (and expected number) under different filters.

<p>Note: These analyses are restricted to the genes that have more than 15 minor alleles in the samples used in each study. MAC columns show the number of minor alleles called per sample, Ba: Baylor, Br: Broad. Filter PASS includes all variants that score a “Pass” based on GATK, Filter MISS: missingness , Filter DpBal: missingness , depth balance for Baylor, for Broad.</p

Theoretical power comparison: Meta versus Mega.

<p>Theoretical power functions of meta- (red) and mega-analysis (blue) at significance level of . is the strength of signal per variant and is the number of rare variants. (A) ; (B) ; (C) ; and (D) .</p

Number of nominally significant genes before and after filtering.

<p>Note: Significance level is 0.01, not corrected for muliple testing. The analyses of the first two rows are for all genes that have at least one MAC in Baylor and Broad dataset. The last rows are restricted to the genes that have more than 15 minor alleles after combining Baylor and Broad datasets.</p

Genomic control and for all tests before and after PC adjustment.

<p>Note: These analyses are restricted to the genes that have more than 4 minor alleles in the samples used in each study. and are calculated based on the median and the 1st quantile of the p-value distribution, respectively. PC adjustment is based on the common variants (CVs) eigen-vectors.</p