Statistical Approach Leveraging Genealogies of Populations with a Founder Effect and Identical by Descent Segments to Identify Rare Variants in Complex Diseases
Bibliographic record
Abstract
Abstract The missing heritability caused by rare variants (RVs) poses a significant challenge to pre-established statistical methods. Our study aims at detecting RVs using identical-by-descent (IBD) segments as a proxy for recent variants in family data from a population with a founder effect for which genealogy is available—a distinguishing feature of our approach. Inferring IBD segments from genotype array data, which is more accessible than whole genome sequences, enables application to large sample sizes. Our approach involves dividing the genome into fixed-length windows, treating each window as a synthetic genomic region (SG), and then identifying groups of affected individuals sharing a specific IBD segment over an SG by analyzing genotype array data to infer pairwise IBD segments. Data from pairwise IBD segments is then used to identify densely connected haplotypes as IBD clusters via DASH. Lastly, we adapt, implement, and evaluate statistics to test for IBD sharing enrichment among affected individuals within SGs. The null distribution of the genome-wide maximal statistic value is obtained by simulating whole-genome transmission in a genealogy using msprime. For application purposes, Eastern Quebec has been studied as an example of a population with a founder effect. Using the BALSAC database to reconstruct the genealogy of 1,200 subjects across 48 schizophrenia and bipolar disorder multi-generational families led to an 18-generation pedigree with 84% completeness at the 10th generation. The statistic denoted as S msg for the “most shared haplotype in an SG” exhibits superior power in detecting causal SGs when compared to the adapted S all measure and (with a single causal variant in a region) to GMMAT (Generalized Linear Mixed Model Association Test) applied to IBD clusters. Our analysis of data pertaining to schizophrenia and bipolar disorder reveals no regions that surpass the conventional significance thresholds for harboring rare variants associated with these disorders. Two distinct regions—on chromosomes 5 and 11—stand out due to their maximal S msg values. These findings underscore the potential of leveraging genealogical data and IBD segments to uncover rare variants in complex diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".