An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort

Abstract

The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the extent and willingness and to encourage and guide participants to contribute phenotypes, we developed an algorithm to score and rank the phenotypes and participants of the PGP. The scoring algorithm calculates the participation index (P-index) for every participant, where 0 indicates no reported phenotypes and 100 indicate complete phenotype reporting. We calculated the P-index for all 5,015 participants in the PGP and they ranged from 0 to 96.7. We found that participants mainly have either high scores (P-index > 90, 29.5%) or low scores (P-index < 10, 57.8%). While, there are significantly more males than female participants (1,793 versus 1,271), females tend to have on average higher P-indexes (P = 0.015). We also reported the P-indexes of participants based on demographics and states like Missouri and Massachusetts have better P-indexes than states like Utah and Minnesota. The P-index can therefore be used as an unbiased way to measure and rank participant’s phenotypic contribution towards the PGP.

Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

“…To test if our method can accurately estimate the proportions of actual human donor samples, we set up a system using a pool of immortalized B-lymphocytes from the Harvard PGP [ 12 – 14 ]. We combined five pools of PGP B-lymphocytes with ten individuals per pool at 1X, 2X, 3X, 4X, and 5X concentration, respectively (see “Methods”).…”

Reliable multiplex generation of pooled induced pluripotent stem cells for genetic testing

“…We obtained 120 Harvard Personal Genome Project (PGP) unique lymphoblastoid donor cell lines (LCLs) from the Coriell Institute for Medical Research (https://coriell.org/) (Ball et al, 2012(Ball et al, , 2014Chan et al, 2017;Mao et al, 2016). These PGP cell lines come with whole-genome sequencing genotype data as well as selected phenotypes associated with each donor (Table S1) (Ball et al, 2012(Ball et al, , 2014Chan et al, 2017;Mao et al, 2016).…”

“…Our approach generates iPSCs from many different donor lymphoblastoid cell lines (LCLs) from the Harvard Personal Genome Project (PGP). The PGP consists of participants with high coverage whole-genome sequencing (WGS) data, self-reported phenotypic data and LCLs available to scientists for research(Ball et al, 2012, 2014; Chan et al, 2017; Mao et al, 2016). With LCLs, it has been demonstrated that they can be successfully reprogrammed into iPSCs via nucleofection of reprogramming factors(Barrett et al, 2014; Choi et al, 2011; Kumar et al, 2016; Muñoz-López et al, 2016; Rajesh et al, 2011; Thomas et al, 2015).…”