A public resource facilitating clinical use of genomes.
Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain-we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Madeleine P
MP
Ball
Joseph V
JV
Thakuria
Alexander Wait
AW
Zaranek
Tom
T
Clegg
Abraham M
AM
Rosenbaum
Xiaodi
X
Wu
Misha
M
Angrist
Jong
J
Bhak
Jason
J
Bobe
Matthew J
MJ
Callow
Carlos
C
Cano
Michael F
MF
Chou
Wendy K
WK
Chung
Shawn M
SM
Douglas
Preston W
PW
Estep
Athurva
A
Gore
Peter
P
Hulick
Alberto
A
Labarga
Je-Hyuk
JH
Lee
Jeantine E
JE
Lunshof
Byung Chul
BC
Kim
Jong-Il
JI
Kim
Zhe
Z
Li
Michael F
MF
Murray
Geoffrey B
GB
Nilsen
Brock A
BA
Peters
Anugraha M
AM
Raman
Hugh Y
HY
Rienhoff
Kimberly
K
Robasky
Matthew T
MT
Wheeler
Ward
W
Vandewege
Daniel B
DB
Vorhaus
Joyce L
JL
Yang
Luhan
L
Yang
John
J
Aach
Euan A
EA
Ashley
Radoje
R
Drmanac
Seong-Jin
SJ
Kim
Jin Billy
JB
Li
Leonid
L
Peshkin
Christine E
CE
Seidman
Jeong-Sun
JS
Seo
Kun
K
Zhang
Heidi L
HL
Rehm
George M
GM
Church
D002460:Cell Line; D003625:Data Collection; D030541:Databases, Genetic; D014644:Genetic Variation; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D010641:Phenotype; D057285:Precision Medicine / Q000379:methods* / Q000639:trends; D017422:Sequence Analysis, DNA; D012984:Software