Adjusting for Familial Relatedness in the Analysis of GWAS Data

A fast linkage method for population GWAS cohorts with related individuals

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.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Family-Based Genome-Wide Association Study of Autism Spectrum Disorder in Middle Eastern Families

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by abnormalities in language and social communication with substantial clinical heterogeneity. Genetic factors play an important role in ASD with heritability estimated between 70% to 80%. Genome-wide association studies (GWAS) have identified multiple loci associated with ASD. However, most studies were performed on European populations and little is known about the genetic architecture of ASD in Middle Eastern populations. Here, we report the first GWAS of ASD in the Middle eastern population of Qatar. We analyzed 171 families with ASD, using linear mixed models adjusting for relatedness and other confounders. Results showed that common single nucleotide polymorphisms (SNP) in seven loci are associated with ASD (p < 1 × 10-5). Although the identified loci did not reach genome-wide significance, many of the top associated SNPs are located within or near genes that have been implicated in ASD or related neurodevelopmental disorders. These include GORASP2, GABBR2, ANKS6, THSD4, ERCC6L, ARHGEF6, and HDAC8. Additionally, three of the top associated SNPs were significantly associated with gene expression. We also found evidence of association signals in two previously reported ASD-susceptibility loci (rs10099100 and rs4299400). Our results warrant further functional studies and replication to provide further insights into the genetic architecture of ASD.

Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

Background

T zone lymphoma (TZL), a histologic variant of peripheral T cell lymphoma, represents about 12% of all canine lymphomas. Golden Retrievers appear predisposed, representing over 40% of TZL cases. Prior research found that asymptomatic aged Golden Retrievers frequently have populations of T zone-like cells (phenotypically identical to TZL) of undetermined significance (TZUS), potentially representing a pre-clinical state. These findings suggest a genetic risk factor for this disease and caused us to investigate potential genes of interest using a genome-wide association study of privately-owned U.S. Golden Retrievers.

Results

Dogs were categorized as TZL (n = 95), TZUS (n = 142), or control (n = 101) using flow cytometry and genotyped using the Illumina CanineHD BeadChip. Using a mixed linear model adjusting for population stratification, we found association with genome-wide significance in regions on chromosomes 8 and 14. The chromosome 14 peak included four SNPs (Odds Ratio = 1.18–1.19, p = .3 × 10^− 5–5.1 × 10^− 5) near three hyaluronidase genes (SPAM1, HYAL4, and HYALP1). Targeted resequencing of this region using a custom sequence capture array identified missense mutations in all three genes; the variant in SPAM1 was predicted to be damaging. These mutations were also associated with risk for mast cell tumors among Golden Retrievers in an unrelated study. The chromosome 8 peak contained 7 SNPs (Odds Ratio = 1.24–1.42, p = 2.7 × 10^− 7–7.5 × 10^− 5) near genes involved in thyroid hormone regulation (DIO2 and TSHR). A prior study from our laboratory found hypothyroidism is inversely associated with TZL risk. No coding mutations were found with targeted resequencing but identified variants may play a regulatory role for all or some of the genes.

Conclusions

The pathogenesis of canine TZL may be related to hyaluronan breakdown and subsequent production of pro-inflammatory and pro-oncogenic byproducts. The association on chromosome 8 may indicate thyroid hormone is involved in TZL development, consistent with findings from a previous study evaluating epidemiologic risk factors for TZL. Future work is needed to elucidate these mechanisms.

New Genetic Loci Associated With Chronic Kidney Disease in an Indigenous Australian Population

The common occurrence of renal disease in Australian Aboriginal populations such as Tiwi Islanders may be determined by environmental and genetic factors. To explore genetic contributions, we performed a genome-wide association study (GWAS) of urinary albumin creatinine ratio (ACR) in a sample of 249 Tiwi individuals with genotype data from a 370K Affymetrix single nucleotide polymorphism (SNP) array. A principal component analysis (PCA) of the 249 individual Tiwi cohort and samples from 11 populations included in phase III of the HapMap Project indicated that Tiwi Islanders are a relatively distinct and unique population with no close genetic relationships to the other ethnic groups. After adjusting for age and sex, the proportion of ACR variance explained by the 370K SNPs was estimated to be 37% (using the software GCTA.31; likelihood ratio = 8.06, p-value = 0.002). The GWAS identified eight SNPs that were nominally significantly associated with ACR (p < 0.0005). A replication study of these SNPs was performed in an independent cohort of 497 individuals on the eight SNPs. Four of these SNPs were significantly associated with ACR in the replication sample (p < 0.05), rs4016189 located near the CRIM1 gene (p = 0.000751), rs443816 located in the gene encoding UGT2B11 (p = 0.022), rs6461901 located near the NFE2L3 gene, and rs1535656 located in the RAB14 gene. The SNP rs4016189 was still significant after adjusting for multiple testing. A structural equation model (SEM) demonstrated that the rs4016189 SNP was not associated with other phenotypes such as estimated glomerular filtration rate (eGFR), diabetes, and blood pressure.