{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:5ZROZYTROQ7RWD35DSCTXBDXRK","short_pith_number":"pith:5ZROZYTR","canonical_record":{"source":{"id":"1209.1341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2012-09-06T16:48:45Z","cross_cats_sorted":["q-bio.GN","stat.AP","stat.ME"],"title_canon_sha256":"e3c8d80b928f0594a16d82f55057b7b509f24b73f75cc34a0221ae63db6b65c3","abstract_canon_sha256":"11182ffe7788f67ec414906c01c1fc21656e60c7f0c7999cb95dbacded648c1b"},"schema_version":"1.0"},"canonical_sha256":"ee62ece271743f1b0f7d1c853b84778a9c6a437da20241d26477fb4f0db37cb4","source":{"kind":"arxiv","id":"1209.1341","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.1341","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"arxiv_version","alias_value":"1209.1341v2","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.1341","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"pith_short_12","alias_value":"5ZROZYTROQ7R","created_at":"2026-05-18T12:26:56Z"},{"alias_kind":"pith_short_16","alias_value":"5ZROZYTROQ7RWD35","created_at":"2026-05-18T12:26:56Z"},{"alias_kind":"pith_short_8","alias_value":"5ZROZYTR","created_at":"2026-05-18T12:26:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:5ZROZYTROQ7RWD35DSCTXBDXRK","target":"record","payload":{"canonical_record":{"source":{"id":"1209.1341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2012-09-06T16:48:45Z","cross_cats_sorted":["q-bio.GN","stat.AP","stat.ME"],"title_canon_sha256":"e3c8d80b928f0594a16d82f55057b7b509f24b73f75cc34a0221ae63db6b65c3","abstract_canon_sha256":"11182ffe7788f67ec414906c01c1fc21656e60c7f0c7999cb95dbacded648c1b"},"schema_version":"1.0"},"canonical_sha256":"ee62ece271743f1b0f7d1c853b84778a9c6a437da20241d26477fb4f0db37cb4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:40:49.037479Z","signature_b64":"nFPC53arNsEobK4HErkh5y8AyU0RGM0OvwQvqdelHAH31/PdEVBifLpNWTenRDH+mrJFEqHq5FvHHb6MIYQkAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee62ece271743f1b0f7d1c853b84778a9c6a437da20241d26477fb4f0db37cb4","last_reissued_at":"2026-05-18T03:40:49.036681Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:40:49.036681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1209.1341","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:40:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hybaip7V+hJha9aoTAM3bv5BBicbEijCVXfj/T25lF3gMC2tFC6ank+179QRPEll0u8FH4VTV33Vmin3tY21CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T22:37:22.722526Z"},"content_sha256":"67bb1441eae2154eb21fb96ac8f2dbd758618afeb152fb198d322432e887f2e1","schema_version":"1.0","event_id":"sha256:67bb1441eae2154eb21fb96ac8f2dbd758618afeb152fb198d322432e887f2e1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:5ZROZYTROQ7RWD35DSCTXBDXRK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Polygenic Modeling with Bayesian Sparse Linear Mixed Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.GN","stat.AP","stat.ME"],"primary_cat":"q-bio.QM","authors_text":"Matthew Stephens, Peter Carbonetto, Xiang Zhou","submitted_at":"2012-09-06T16:48:45Z","abstract_excerpt":"Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given data set one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a \"Bayesian sparse linear mixed model\" (BSLMM) that includes both these models as special cases. We address several key compu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.1341","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:40:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0SZl91mXZAJueVEhpMT+0L1O+mAxsGFVscAeIoUPciebuKzUTRteWkq1IxLCkfj+J7zvWzbO+fvt0+gofakeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T22:37:22.722869Z"},"content_sha256":"386a01073ceedd84bf431fbb353847f931b0c57243bcd4f449c3cb02deef5c69","schema_version":"1.0","event_id":"sha256:386a01073ceedd84bf431fbb353847f931b0c57243bcd4f449c3cb02deef5c69"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/bundle.json","state_url":"https://pith.science/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-25T22:37:22Z","links":{"resolver":"https://pith.science/pith/5ZROZYTROQ7RWD35DSCTXBDXRK","bundle":"https://pith.science/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/bundle.json","state":"https://pith.science/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5ZROZYTROQ7RWD35DSCTXBDXRK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:5ZROZYTROQ7RWD35DSCTXBDXRK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"11182ffe7788f67ec414906c01c1fc21656e60c7f0c7999cb95dbacded648c1b","cross_cats_sorted":["q-bio.GN","stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2012-09-06T16:48:45Z","title_canon_sha256":"e3c8d80b928f0594a16d82f55057b7b509f24b73f75cc34a0221ae63db6b65c3"},"schema_version":"1.0","source":{"id":"1209.1341","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.1341","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"arxiv_version","alias_value":"1209.1341v2","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.1341","created_at":"2026-05-18T03:40:49Z"},{"alias_kind":"pith_short_12","alias_value":"5ZROZYTROQ7R","created_at":"2026-05-18T12:26:56Z"},{"alias_kind":"pith_short_16","alias_value":"5ZROZYTROQ7RWD35","created_at":"2026-05-18T12:26:56Z"},{"alias_kind":"pith_short_8","alias_value":"5ZROZYTR","created_at":"2026-05-18T12:26:56Z"}],"graph_snapshots":[{"event_id":"sha256:386a01073ceedd84bf431fbb353847f931b0c57243bcd4f449c3cb02deef5c69","target":"graph","created_at":"2026-05-18T03:40:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given data set one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a \"Bayesian sparse linear mixed model\" (BSLMM) that includes both these models as special cases. We address several key compu","authors_text":"Matthew Stephens, Peter Carbonetto, Xiang Zhou","cross_cats":["q-bio.GN","stat.AP","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2012-09-06T16:48:45Z","title":"Polygenic Modeling with Bayesian Sparse Linear Mixed Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.1341","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:67bb1441eae2154eb21fb96ac8f2dbd758618afeb152fb198d322432e887f2e1","target":"record","created_at":"2026-05-18T03:40:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"11182ffe7788f67ec414906c01c1fc21656e60c7f0c7999cb95dbacded648c1b","cross_cats_sorted":["q-bio.GN","stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2012-09-06T16:48:45Z","title_canon_sha256":"e3c8d80b928f0594a16d82f55057b7b509f24b73f75cc34a0221ae63db6b65c3"},"schema_version":"1.0","source":{"id":"1209.1341","kind":"arxiv","version":2}},"canonical_sha256":"ee62ece271743f1b0f7d1c853b84778a9c6a437da20241d26477fb4f0db37cb4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee62ece271743f1b0f7d1c853b84778a9c6a437da20241d26477fb4f0db37cb4","first_computed_at":"2026-05-18T03:40:49.036681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:40:49.036681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nFPC53arNsEobK4HErkh5y8AyU0RGM0OvwQvqdelHAH31/PdEVBifLpNWTenRDH+mrJFEqHq5FvHHb6MIYQkAA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:40:49.037479Z","signed_message":"canonical_sha256_bytes"},"source_id":"1209.1341","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67bb1441eae2154eb21fb96ac8f2dbd758618afeb152fb198d322432e887f2e1","sha256:386a01073ceedd84bf431fbb353847f931b0c57243bcd4f449c3cb02deef5c69"],"state_sha256":"8612effd28561ce8fc046d995a9707c9bcae82ee430cc249e3706e249af17e64"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+rDQWyd+MapZxLPLrBp+Gqj+jOIyn/ZDMN9QbJbPCDivzc+UIQYuKhiGaMQSUZaqKK6j/DY90U2ZUdTB+CXIBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T22:37:22.724800Z","bundle_sha256":"9848b72ed613d2c8837a4c9ea28e49ce11ff89c235034758f1957712a9990ae3"}}