Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman–Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

A fast linkage method for population GWAS cohorts with related individuals / Zajac, G. J. M.; Gagliano Taliun, S. A.; Sidore, C.; Graham, S. E.; Asvold, B. O.; Brumpton, B.; Nielsen, J. B.; Zhou, W.; Gabrielsen, M.; Skogholt, A. H.; Fritsche, L. G.; Schlessinger, D.; Cucca, F.; Hveem, K.; Willer, C. J.; Abecasis, G. R.. - In: GENETIC EPIDEMIOLOGY. - ISSN 1098-2272. - 47:3(2023), pp. 231-248. [10.1002/gepi.22516]

A fast linkage method for population GWAS cohorts with related individuals

Sidore C.;Cucca F.;
2023-01-01

Abstract

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman–Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.
2023
A fast linkage method for population GWAS cohorts with related individuals / Zajac, G. J. M.; Gagliano Taliun, S. A.; Sidore, C.; Graham, S. E.; Asvold, B. O.; Brumpton, B.; Nielsen, J. B.; Zhou, W.; Gabrielsen, M.; Skogholt, A. H.; Fritsche, L. G.; Schlessinger, D.; Cucca, F.; Hveem, K.; Willer, C. J.; Abecasis, G. R.. - In: GENETIC EPIDEMIOLOGY. - ISSN 1098-2272. - 47:3(2023), pp. 231-248. [10.1002/gepi.22516]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/346638
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact