Introduction

Alzheimer’s disease (AD) is the most common dementing disease and a pressing international healthcare concern affecting 24 to 35 million people worldwide. With the rapidly aging population, AD incidence is predicted to increase from 1 in 200 to 333 people now [1–3] to 1 in 85 people by 2050 [2]. A better understanding of genetic factors in AD would aid diagnosis and provide new treatment approaches. Genetics clearly plays a role in AD. Early-onset AD, or AD beginning before the age of 65, can be caused by one of more than 200 sequence variants in the amyloid beta precursor protein (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes [4–6]. However, just 1% of AD cases have a young age-of-onset and consequently late-onset AD (LOAD) warrants the most attention.

LOAD is also strongly influenced by genetics, but a clear Mendelian pattern of inheritance is often not observed. There are several factors that could account for this even if causative mutations for LOAD exist. 1) The chance for censoring bias increases with increasing age as family members die from other causes before the disease is manifest. 2) A definitive evaluation and autopsy are less likely to be performed in late- as compared to early-onset cases. 3) Younger individuals with AD are more likely to participate in research, particularly clinical drug trials. And lastly, family members are less likely to attribute cognitive changes to disease rather than an illness incident to aging, perhaps because of continuing societal stigma. Although older individuals occasionally have a genetic variant that typically causes early-onset AD [7], no genetic variants that cause LOAD have been discovered. Because of the difficulties in identifying familial LOAD, most attention has been focused on discovering genetic risk factors. However, despite considerable effort, genetic factors leading to LOAD have been difficult to fully explain. Thus far, genetic variants associated with LOAD only convey an added risk of disease. The most important genetic factor in LOAD is the ε4 allele of apolipoprotein E (APOE) [8]. Inheriting one ε4 allele increases risk for AD 4-fold while inheriting two ε4 alleles increases risk 13-fold. Genome-wide association studies (GWAS) are one strategy to identify genetic risk factors. GWAS studies have provided evidence that several gene variants in addition to APOE are associated with LOAD [9–12]. Unfortunately, these variants have only a very modest effect on risk and all GWAS-identified variants contribute only 20-30% of the genetic risk for LOAD [9]. Furthermore, data needed to power GWAS studies rely on large sample sizes of unrelated individuals and have insufficient statistical power to detect rare genetic variants, even if their effect in an individual is substantial or even causative. There is evidence that rare genetic variants play an important role in the risk of LOAD. Whole genome and exome sequencing studies have identified rare variants with large effects on LOAD risk [13,14]. Additional strategies sensitive to rare genetic variants are needed to more rapidly advance our understanding of the genetics of LOAD.

Family-based studies provide a promising approach to identify rare variants cause LOAD or result in a substantial risk. Population-based studies have contributed greatly to our understanding of AD; types of study design include linkage analysis [4,15,16], twin studies [17,18], initial AD population risk studies [19], and environmental factors [20,21]. Family-based studies have suggested a significant genetic role in LOAD by finding that first degree relatives of patients with AD have a two- to four-fold increased risk of dementia from ages 65-80 [22], A weakness of such studies though is that probands were referred for diagnostic evaluation and participation in research studies and such cases invariably report a strong family history of dementia. Furthermore, the disease status of relatives depends upon informant report rather than medical records. The Utah Population database (UPDB) offers a unique resource with the opportunity to combine the advantageous attributes of both population-based and family-based studies. Case ascertainment does not rely on individuals presenting clinically, but rather medical records can be used to identify disease. It is possible to identify multi-generational relatedness and genealogical records are as complete as possible and often verified by several family members as documented in church records, thus avoiding some of the major pitfalls of informant report alone. Here we present the largest genealogical-based analysis of LOAD to date. UPDB death certificates that attributed death at any level to AD were used to test the hypothesis of a genetic contribution to LOAD.

Methods

Results

Almost 4,000 individuals in the UPDB with at least 3 generations of genealogy data and at least 12 of their 14 immediate ancestors included in the UPDB have a cause-of-death of AD on their death certificate. The number of individuals by ICD 9 revision and code is shown in Table 1. No deaths from AD before the age of 35 years were observed. Table S1 shows the age-at-death distribution for the 3,998 individuals with an AD cause-of-death who are analyzed here; for 2,244 of these AD deaths, AD was the primary cause-of-death.

Summary results for the GIF test for excess relatedness are: number of individuals considered (3998), the case GIF (5.54), the mean control GIF (4.83), the p-value for the overall GIF (<0.001), the case dGIF (4.14), the mean control dGIF (4.39), and the p-value for the distant GIF test (0.005). When all individuals dying from AD are considered as a group, there is overall significant evidence for excess relatedness (GIF p < 0.001); in addition, when relationships closer than first cousins are ignored, a significant excess relatedness is still observed (dGIF p = 0.005), strongly supporting a genetic contribution to this phenotype. Few deaths occurring before age 65 years in the UPDB resource were attributed to AD on a Utah death certificate (n = 62); this sample size is too small to be informative with the GIF analysis.

Figure 1 shows the contribution to the overall GIF statistic by pairwise genetic distance for cases and for controls. It is clear in this figure that relatedness of individuals dying from AD is higher than expected for both close and distant relationships; this excess extends out to a pairwise genetic distance of at least 8 (equivalent to third cousins).

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Figure 1. Contribution to the GIF statistic for Alzheimer’s disease mortality relatedness by pairwise genetic distance for cases and controls.

https://doi.org/10.1371/journal.pone.0077087.g001

Relative Risk estimates for death from AD among first-, second-, and third-degree relatives of individuals dying from AD are shown in Table 2. This table displays the total number of relatives with a death certificate, the number of relatives with a death certificate indicating AD contributed to death (observed), the expected number of AD deaths (expected) among these relatives, the significance value for the test (p-value), the RR estimate, and 95% confidence interval (95% CI) for the RR. RRs for death from AD among first-, second-, and third-degree relatives of individuals dying from AD were all significantly elevated.

We also considered the RR for death from AD among the first-, second-, and third-degree relatives of individuals dying from AD before age 65 years (Table 2). Sample sizes are much smaller due to the small number of young AD deaths in the UPDB, leading to large confidence intervals for RRs. All RRs were elevated, but only the second-degree RR was significantly elevated (RR = 2.13; 95% CI 1.19, 3.52; p = 0.006).

Figure 2 shows an example high-risk Utah AD pedigree identified in the UPDB. The founding couple had over 4,100 descendants with data in the UPDB. Eleven of these descendants died from AD while only 3.2 AD deaths were expected. There were more than 60 additional high-risk AD death pedigrees (p<0.01) with at least 5 individuals dying from AD identified in the UPDB. As might be expected from the narrow window of view to AD deaths in Utah (only ICD9 and ICD10 coded death certificates), most individuals dying from AD are in the same generation (few parent offspring pairs). This is also evident in Figure 1 where dips associated with a smaller number of pairs of genetic distances 1, 3 and 5, which represent individuals from different generations.

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Figure 2. Example of a high-risk Alzheimer’s disease mortality pedigree.

Squares are males, circles are females, and diamonds are gender-censored (unspecified gender) individuals. Individuals with a diagonal line are deceased, and shaded shapes are Alzheimer’s disease cases; unaffected siblings are not shown. The founder has more than 4,100 descendants; 11 died from Alzheimer’s disease, only 3.2 Alzheimer’s disease deaths were expected (p= 0.0031).

https://doi.org/10.1371/journal.pone.0077087.g002

Discussion

Our understanding of the genetic basis for Alzheimer’s disease has dramatically increased in the past few years. Nevertheless, a significant proportion of genetic variance in risk for disease and the majority of high-risk late-onset AD pedigrees remain unexplained. Using a unique population-based resource in the state of Utah that combines genealogy data from the mid 1800s with death certificates from the early 1900s, we have presented an unbiased view of the familial clustering of Alzheimer’s disease mortality as well as strong evidence for a genetic contribution for late-onset AD. The methods used in this study have previously provided evidence for a heritable component to many diseases, including: influenza mortality, rotator cuff disease, asthma mortality, pancreatic cancer, and lumbar disc disease, among others [23–28]. Studies of Utah high-risk pedigrees identified in the UPDB have lead to the discovery of multiple disease predisposition genes including BRCA1, BRCA2, p16, and HPC2/ELAC2, among others [29–32]. The UPDB data analyzed represents a homogeneous population that has been shown to be genetically representative of Northern Europe, with normal levels of inbreeding levels and thus not representative of a genetically isolated group [47,48].

The vast majority of AD deaths in Utah occurred late in life, although we cannot determine age at symptom onset in the database. Comparison of the RR results from the potential early-onset AD cases (represented by early AD deaths, Table 2) with the overall RR results shows that the early-onset AD cases had very little impact in the overall results presented. The various analyses presented strongly support a genetic contribution to the observed clustering of individuals dying from AD. The GIF analysis showed that pairs of individuals dying from AD were significantly more related than expected for a similar group of individuals randomly selected from Utah deaths. An excess of relatedness was observed for both close, and distant relationships. RRs for death from AD among relatives of individuals dying from AD were significantly elevated, for both close and more distant relatives.

Multiple pedigrees showing a significant excess of death from AD have been identified in the UPDB. These pedigrees differ from published high-risk AD pedigrees in that the phenotype considered is “AD that contributed to death”. They also differ in their typically very large generational depth and breadth of identified individuals, which are both considerably larger than published AD pedigrees. In addition, because they have been ascertained from a population and we have considered population rates of AD death in this population, the pedigrees are truly high-risk (with a significant excess of observed over expected deaths) rather than just pedigrees with a large number of related AD deaths. As such, these pedigrees are ideal for future genetic studies of AD.

This study used a uniform, consistent source for all diagnoses, namely AD that contributed to cause-of-death as confirmed by presence on a death certificate. Thus the results are not limited by the bias caused from selected ascertainment of cases or requiring informant recall for diagnoses. The most significant limitation of this analysis is the narrow window of view to identify individuals diagnosed with death from AD. This results from the relatively short period of time for which coding for this diagnosis has existed (only present in ICD 9 and 10). These effects limit our ability to identify cases that might be related across different generations (e.g. grandparent/ grandchild or avunculars), but not our ability to identify related cases who died in the same cohort (e.g. distant cousins). Although individuals dying from AD may have been censored from our observation in this resource, the assumption can be made that cases are uniformly censored across the data set, leading to conservative, but unbiased, estimates of familiality due to potential inclusion of censored cases in estimation of death rates and selection of controls. An additional limitation is the recognized under-reporting of AD as a cause-of-death on death certificates, which could have resulted in lower estimates of AD mortality rates overall. On the other hand, the physicians’ willingness to specify this cause-of-death rather than using a general term such as senility or senile dementia, or omitting its mention altogether likely indicates that an evaluation was performed when cause was considered. Although in the ideal world disease classification would be based upon autopsy findings or review of medical records and consensus diagnosis, this is impractical in a large population-based study and the direct medical information the treating physician provides in the death certificate is a reasonable alternative. Because we apply the internal AD death rates to the UPDB population of individuals with death certificates uniformly, we assume there is no resulting bias in risk estimates.

The presence of familiality in AD is unsurprising. ApoE genotype does affect risk of AD among family members, but not to the degree observed in this study. In one report, among relatives in the ApoE 3/3 group, the lifetime risk for AD by age 90 was greater than 3 times the expected proportion of ε4 carriers [49]. In addition, by age 93 at least 50% of ε4/ε4 individuals did not develop dementia. Although we have not determined apoE status in family members, we believe it is unlikely that inheritance of a particular apoE genotype explains our observations. Likewise, variants that confer only a modest increased risk of AD are unlikely to be explanatory. Because older individuals are at higher risk for AD, there may be some confounding of longevity heritability with AD heritability. Such confounding does not invalidate the results, but rather indicates the complexity of the study of the genetics of all late-onset complex disease.

This study of AD heritability does not allow determination of the mechanisms that lead to genetic predisposition to death from AD. This will require in depth assessment of families with high familiality for AD and comparison with families in which the prevalence of AD diagnoses are either typical or under-represented. We have identified multiple pedigrees with a significant excess of late-onset AD deaths. The presence of these high-risk pedigrees strongly supports the possibility of rare predisposition variants not yet identified. We propose the study of these pedigrees to identify the missing heritability (rare variants) predisposing to AD, as well as to better understand mechanisms and potential environmental factors.

Supporting Information

Acknowledgments

The authors gratefully acknowledge the efforts of those who developed The Utah Population Database and the individuals whose genealogical and health records data make this database possible.