{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:FBF56IBYDFKWZGRVYO2VHCBRN4","short_pith_number":"pith:FBF56IBY","schema_version":"1.0","canonical_sha256":"284bdf203819556c9a35c3b55388316f1d1fdd5c20cfbb3b977bf77265df73d1","source":{"kind":"arxiv","id":"1611.03361","version":3},"attestation_state":"computed","paper":{"title":"Modelling correlated marker effects in genome-wide prediction via Gaussian concentration graph models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"q-bio.QM","authors_text":"Carlos Alberto Mart\\'inez, Kshitij Khare, Mauricio A. Elzo, Syed Rahman","submitted_at":"2016-11-10T15:50:53Z","abstract_excerpt":"In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1611.03361","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2016-11-10T15:50:53Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"128029dc56a4e0b60b4c5928ba2996d52f90a77752a8849125f49489534431ab","abstract_canon_sha256":"bf8399e3aa8388b5aab9d5f97be2720e7f5456fa039ecdb120c83a0a193406b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:42.210005Z","signature_b64":"TUEk0HLyKZ9ZlqEOYfNtlfVd5mIo61FnWsbbDAoiNvGQFhFG6Qumzz9SsMP1mrTYe3++kPTG5HmNCptaOltECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"284bdf203819556c9a35c3b55388316f1d1fdd5c20cfbb3b977bf77265df73d1","last_reissued_at":"2026-05-18T00:34:42.209225Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:42.209225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modelling correlated marker effects in genome-wide prediction via Gaussian concentration graph models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"q-bio.QM","authors_text":"Carlos Alberto Mart\\'inez, Kshitij Khare, Mauricio A. Elzo, Syed Rahman","submitted_at":"2016-11-10T15:50:53Z","abstract_excerpt":"In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03361","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.03361","created_at":"2026-05-18T00:34:42.209373+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.03361v3","created_at":"2026-05-18T00:34:42.209373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03361","created_at":"2026-05-18T00:34:42.209373+00:00"},{"alias_kind":"pith_short_12","alias_value":"FBF56IBYDFKW","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"FBF56IBYDFKWZGRV","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"FBF56IBY","created_at":"2026-05-18T12:30:15.759754+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4","json":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4.json","graph_json":"https://pith.science/api/pith-number/FBF56IBYDFKWZGRVYO2VHCBRN4/graph.json","events_json":"https://pith.science/api/pith-number/FBF56IBYDFKWZGRVYO2VHCBRN4/events.json","paper":"https://pith.science/paper/FBF56IBY"},"agent_actions":{"view_html":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4","download_json":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4.json","view_paper":"https://pith.science/paper/FBF56IBY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.03361&json=true","fetch_graph":"https://pith.science/api/pith-number/FBF56IBYDFKWZGRVYO2VHCBRN4/graph.json","fetch_events":"https://pith.science/api/pith-number/FBF56IBYDFKWZGRVYO2VHCBRN4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4/action/storage_attestation","attest_author":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4/action/author_attestation","sign_citation":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4/action/citation_signature","submit_replication":"https://pith.science/pith/FBF56IBYDFKWZGRVYO2VHCBRN4/action/replication_record"}},"created_at":"2026-05-18T00:34:42.209373+00:00","updated_at":"2026-05-18T00:34:42.209373+00:00"}